Merge branch 'main' into wfh/cli_from_module

This PR only allows this in the HTTP client. 

I can follow up with a PR to allow throughout the entire API.

The use case is to allow instantiating the client once (w/ a single connection pool), but allowing changing api keys and any other headers at run time

.github/workflows/run_notebooks.yml

@@ -57,13 +57,13 @@ jobs:
         env:
           # these won't actually be used because of the VCR cassettes
           # but need to set them to avoid triggering getpass()
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-          TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
-          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
-          NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
-          COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
-          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
+          OPENAI_API_KEY: "very-secret-key"
+          ANTHROPIC_API_KEY: "very-secret-key"
+          TAVILY_API_KEY: "very-secret-key"
+          LANGSMITH_API_KEY: "very-secret-key"
+          NOMIC_API_KEY: "very-secret-key"
+          COHERE_API_KEY: "very-secret-key"
+          FIREWORKS_API_KEY: "very-secret-key"
         run: |
           if [ "${{ github.event_name }}" = "workflow_dispatch" ] || [ "${{ github.event_name }}" = "schedule" ]; then
             echo "Running all notebooks"

.github/workflows/size.yml

@@ -1,29 +0,0 @@
-name: Check File Size
-
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - main
-  workflow_dispatch:
-
-jobs:
-  file-size-check:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - name: Get changed files
-        id: changed-files
-        uses: tj-actions/changed-files@v44
-      - name: Filter by size
-        # TODO: roll back the web voyager hack
-        run: |
-          large_added_files=$(find ${{ steps.changed-files.outputs.added_files }} -maxdepth 0 -size +1M | grep -v "web_voyager" || true)
-          if [ -n "$large_added_files" ]; then
-            echo "Large files added: $large_added_files"
-            echo "# Large files added:" >> $GITHUB_STEP_SUMMARY
-            echo "$large_added_files" >> $GITHUB_STEP_SUMMARY
-            exit 1
-          fi

README.md

@@ -1,7 +1,7 @@
 <picture class="github-only">
-  <source media="(prefers-color-scheme: light)" srcset="docs/docs/static/wordmark_dark.svg">
-  <source media="(prefers-color-scheme: dark)" srcset="docs/docs/static/wordmark_light.svg">
-  <img alt="LangGraph Logo" src="docs/docs/static/wordmark_dark.svg" width="80%">
+  <source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
+  <source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
+  <img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="80%">
 </picture>
 
 <div>

docs/README.md

@@ -14,6 +14,8 @@ To run the documentation server locally you can run:
 make serve-docs
 ```
 
+This will start the documentation server on [http://127.0.0.1:8000/langgraph/](http://127.0.0.1:8000/langgraph/).
+
 ## Execute notebooks
 
 If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:

docs/_scripts/third_party_page/packages.yml

@@ -30,6 +30,12 @@ packages:
   - name: "langgraph-bigtool"
     repo: "langchain-ai/langgraph-bigtool"
     description: "Build LangGraph agents with large numbers of tools."
+  - name: "ai-data-science-team"
+    repo: "business-science/ai-data-science-team"
+    description: "An AI-powered data science team of agents to help you perform common data science tasks 10X faster."
   - name: "langgraph-reflection"
     repo: "langchain-ai/langgraph-reflection"
-    description: "LangGraph agent that runs a reflection step."
+    description: "LangGraph agent that runs a reflection step."
+  - name: "langmanus"
+    repo: "langmanus/langmanus"
+    description: "A community-driven AI automation framework that builds upon the incredible work of the open source community. Our goal is to combine language models with specialized tools for tasks like web search, crawling, and Python code execution, while giving back to the community that made this possible."

docs/docs/cloud/deployment/setup_pyproject.md

@@ -64,7 +64,7 @@ license = "MIT"
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = ">=3.9.0,<3.13"
+python = ">=3.9"
 langgraph = "^0.2.0"
 langchain-fireworks = "^0.1.3"
 

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

@@ -63,7 +63,7 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
         "messages": [
             {
                 "role": "user",
-                "content": "Use the search tool to ask the user where they are, then look up the weather there",
+                "content": "Ask the user where they are, then look up the weather there",
             }
         ]
     }
@@ -85,8 +85,7 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
       messages: [
         {
           role: "human",
-          content: "Use the search tool to ask the user where they are, then look up the weather there"
-        }
+          content: "Ask the user where they are, then look up the weather there" }
       ]
     };
 
@@ -115,7 +114,7 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
      --header 'Content-Type: application/json' \
      --data "{
        \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Use the search tool to ask the user where they are, then look up the weather there\"}]},
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Ask the user where they are, then look up the weather there\"}]},
        \"interrupt_before\": [\"ask_human\"],
        \"stream_mode\": [
          \"updates\"

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

@@ -1,6 +1,133 @@
 # Prompt Engineering in LangGraph Studio
 
-In LangGraph Studio you can iterate on the prompts used within your graph by utilizing the LangSmith Playground. To do so:
+## Overview
+
+A central aspect of agent development is prompt engineering. LangGraph Studio makes it easy to iterate on the prompts used within your graph directly within the UI.
+
+## Setup
+
+The first step is to define your [configuration](https://langchain-ai.github.io/langgraph/how-tos/configuration/) such that LangGraph Studio is aware of the prompts you want to iterate on and which nodes they are associated with.
+
+### Reference
+
+When defining your configuration, you can use special metadata keys to instruct LangGraph Studio how to handle different fields. Here's a reference for the available configuration options:
+
+#### `langgraph_nodes`
+
+- **Description**: Specifies which graph nodes a configuration field is associated with.
+- **Value Type**: Array of strings, where each string is the name of a node in your graph.
+- **Usage Context**: Include in the `json_schema_extra` dictionary for Pydantic models or the `metadata["json_schema_extra"]` dictionary for dataclasses.
+- **Required**: No, but necessary if you want a field to be editable for specific nodes in the UI.
+- **Example**:
+  ```python
+  system_prompt: str = Field(
+      default="You are a helpful AI assistant.",
+      json_schema_extra={"langgraph_nodes": ["call_model", "other_node"]},
+  )
+  ```
+
+#### `langgraph_type`
+
+- **Description**: Specifies the type of configuration field, which determines how it's handled in the UI.
+- **Value Type**: String
+- **Supported Values**:
+  - `"prompt"`: Indicates the field contains prompt text that should be treated specially in the UI.
+- **Usage Context**: Include in the `json_schema_extra` dictionary for Pydantic models or the `metadata["json_schema_extra"]` dictionary for dataclasses.
+- **Required**: No, but helpful for prompt fields to enable special handling.
+- **Example**:
+  ```python
+  system_prompt: str = Field(
+      default="You are a helpful AI assistant.",
+      json_schema_extra={
+          "langgraph_nodes": ["call_model"],
+          "langgraph_type": "prompt",
+      },
+  )
+  ```
+
+### Example
+
+For example, if you have a node called `call_model` whose system prompt you want to iterate on, you can define a configuration like the following.
+
+```python
+## Using Pydantic
+from pydantic import BaseModel, Field
+from typing import Annotated, Literal
+
+class Configuration(BaseModel):
+    """The configuration for the agent."""
+
+    system_prompt: str = Field(
+        default="You are a helpful AI assistant.",
+        description="The system prompt to use for the agent's interactions. "
+        "This prompt sets the context and behavior for the agent.",
+        json_schema_extra={
+            "langgraph_nodes": ["call_model"],
+            "langgraph_type": "prompt",
+        },
+    )
+
+    model: Annotated[
+        Literal[
+            "anthropic/claude-3-7-sonnet-latest",
+            "anthropic/claude-3-5-haiku-latest",
+            "openai/o1",
+            "openai/gpt-4o-mini",
+            "openai/o1-mini",
+            "openai/o3-mini",
+        ],
+        {"__template_metadata__": {"kind": "llm"}},
+    ] = Field(
+        default="openai/gpt-4o-mini",
+        description="The name of the language model to use for the agent's main interactions. "
+        "Should be in the form: provider/model-name.",
+        json_schema_extra={"langgraph_nodes": ["call_model"]},
+    )
+
+## Using Dataclasses
+from dataclasses import dataclass, field
+
+@dataclass(kw_only=True)
+class Configuration:
+    """The configuration for the agent."""
+
+    system_prompt: str = field(
+        default="You are a helpful AI assistant.",
+        metadata={
+            "description": "The system prompt to use for the agent's interactions. "
+            "This prompt sets the context and behavior for the agent.",
+            "json_schema_extra": {"langgraph_nodes": ["call_model"]},
+        },
+    )
+
+    model: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = field(
+        default="anthropic/claude-3-5-sonnet-20240620",
+        metadata={
+            "description": "The name of the language model to use for the agent's main interactions. "
+            "Should be in the form: provider/model-name.",
+            "json_schema_extra": {"langgraph_nodes": ["call_model"]},
+        },
+    )
+
+```
+
+## Iterating on prompts
+
+### Node Configuration
+
+With this set up, running your graph and viewing in LangGraph Studio will result in the graph rendering like such.
+
+**Note the configuration icon in the top right corner of the `call_model` node**:
+
+![Graph in Studio](../img/studio_graph_with_configuration.png){width=1200}
+
+Clicking this icon will open a modal where you can edit the configuration for all of the fields associated with the `call_model` node. From here, you can save your changes and apply them to the graph. Note that these values reflect the currently active assistant, and saving will update the assistant with the new values.
+
+![Configuration modal](../img/studio_node_configuration.png){width=1200}
+
+### Playground
+
+LangGraph Studio also supports prompt engineering through an integration with the LangSmith Playground. To do so:
 
 1. Open an existing thread or create a new one.
 2. Within the thread log, any nodes that have made an LLM call will have a "View LLM Runs" button. Clicking this will open a popover with the LLM runs for that node.
@@ -8,8 +135,6 @@ In LangGraph Studio you can iterate on the prompts used within your graph by uti
 
 ![Playground in Studio](../img/studio_playground.png){width=1200}
 
-
-
 From here you can edit the prompt, test different model configurations and re-run just this LLM call without having to re-run the entire graph. When you are happy with your changes, you can copy the updated prompt back into your graph.
 
 For more information on how to use the LangSmith Playground, see the [LangSmith Playground documentation](https://docs.smith.langchain.com/prompt_engineering/how_to_guides#playground).

docs/docs/concepts/agentic_concepts.md

@@ -14,7 +14,7 @@ As a result, there are many different types of [agent architectures](https://blo
 
 ## Router
 
-A router allows an LLM to select a single step from a specified set of options. This is an agent architecture that exhibits a relatively limited level of control because the LLM usually focuses on making a single decision and produces a specific output from limited set of pre-defined options. Routers typically employ a few different concepts to achieve this.
+A router allows an LLM to select a single step from a specified set of options. This is an agent architecture that exhibits a relatively limited level of control because the LLM usually focuses on making a single decision and produces a specific output from a limited set of pre-defined options. Routers typically employ a few different concepts to achieve this.
 
 ### Structured Output
 

docs/docs/concepts/high_level.md

@@ -2,7 +2,7 @@
 
 ## LLM applications
 
-LLMs make it possible to embed intelligence into a new class of applications. There are many patterns for building applications that use LLMs. [Workflows](https://www.anthropic.com/research/building-effective-agents) have scaffolding of predefined code paths around LLM calls. LLMs can direct the control flow through these predefined code paths, which some consider to be an "[agentic system](https://www.anthropic.com/research/building-effective-agents)". In other cases, it's possible to remove this scaffolding, creating autonomous agents that can [plan](https://huyenchip.com/2025/01/07/agents.html), take actions via [tool calls](https://python.langchain.com/docs/concepts/tool_calling/), and directly respond [to the feedback from their own actions](https://research.google/blog/react-synergizing-reasoning-and-acting-in-language-models/) with further actions.
+LLMs make it possible to embed intelligence into a new class of applications. There are many patterns for building applications that use LLMs. Workflows have scaffolding of predefined code paths around LLM calls. LLMs can direct the control flow through these predefined code paths, which some consider to be an "agentic system". In other cases, it's possible to remove this scaffolding, creating autonomous agents that can [plan](https://huyenchip.com/2025/01/07/agents.html), take actions via [tool calls](https://python.langchain.com/docs/concepts/tool_calling/), and directly respond [to the feedback from their own actions](https://research.google/blog/react-synergizing-reasoning-and-acting-in-language-models/) with further actions.
 
 ![Agent Workflow](img/agent_workflow.png)
 

docs/docs/concepts/langgraph_platform.md

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

docs/docs/concepts/low_level.md

@@ -360,7 +360,7 @@ Use [conditional edges](#conditional-edges) to route between nodes conditionally
 If you are using [subgraphs](#subgraphs), you might want to navigate from a node within a subgraph to a different subgraph (i.e. a different node in the parent graph). To do so, you can specify `graph=Command.PARENT` in `Command`:
 
 ```python
-def my_node(state: State) -> Command[Literal["my_other_node"]]:
+def my_node(state: State) -> Command[Literal["other_subgraph"]]:
     return Command(
         update={"foo": "bar"},
         goto="other_subgraph",  # where `other_subgraph` is a node in the parent graph

docs/docs/concepts/memory.md

@@ -275,7 +275,7 @@ See this how-to [video](https://www.youtube.com/watch?v=37VaU7e7t5o) for example
 
 [Procedural memory](https://en.wikipedia.org/wiki/Procedural_memory), in both humans and AI agents, involves remembering the rules used to perform tasks. In humans, procedural memory is like the internalized knowledge of how to perform tasks, such as riding a bike via basic motor skills and balance. Episodic memory, on the other hand, involves recalling specific experiences, such as the first time you successfully rode a bike without training wheels or a memorable bike ride through a scenic route. For AI agents, procedural memory is a combination of model weights, agent code, and agent's prompt that collectively determine the agent's functionality. 
 
-In practice, it is fairly uncommon for agents to modify their model weights or rewrite their code. However, it is more common for agents to [modify their own prompts](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/prompt-generator). 
+In practice, it is fairly uncommon for agents to modify their model weights or rewrite their code. However, it is more common for agents to modify their own prompts. 
 
 One effective approach to refining an agent's instructions is through ["Reflection"](https://blog.langchain.dev/reflection-agents/) or meta-prompting. This involves prompting the agent with its current instructions (e.g., the system prompt) along with recent conversations or explicit user feedback. The agent then refines its own instructions based on this input. This method is particularly useful for tasks where instructions are challenging to specify upfront, as it allows the agent to learn and adapt from its interactions.
 

docs/docs/concepts/multi_agent.md

@@ -89,7 +89,7 @@ def transfer_to_bob(state):
     )
 ```
 
-This is a special case of updating the graph state from tools where in addition the state update, the control flow is included as well.
+This is a special case of updating the graph state from tools where, in addition to the state update, the control flow is included as well.
 
 !!! important
 
@@ -235,7 +235,7 @@ supervisor = create_react_agent(model, tools)
 
 ### Hierarchical
 
-As you add more agents to your system, it might become too hard for the supervisor to manage all of them. The supervisor might start making poor decisions about which agent to call next, the context might become too complex for a single supervisor to keep track of. In other words, you end up with the same problems that motivated the multi-agent architecture in the first place.
+As you add more agents to your system, it might become too hard for the supervisor to manage all of them. The supervisor might start making poor decisions about which agent to call next, or the context might become too complex for a single supervisor to keep track of. In other words, you end up with the same problems that motivated the multi-agent architecture in the first place.
 
 To address this, you can design your system _hierarchically_. For example, you can create separate, specialized teams of agents managed by individual supervisors, and a top-level supervisor to manage the teams.
 
@@ -339,9 +339,9 @@ builder.add_edge("agent_1", "agent_2")
 
 ## Communication between agents
 
-The most important thing when building multi-agent systems is figuring out how the agents communicate. There are few different considerations:
+The most important thing when building multi-agent systems is figuring out how the agents communicate. There are a few different considerations:
 
-- Do agents communicate via [**via graph state or via tool calls**](#graph-state-vs-tool-calls)?
+- Do agents communicate [**via graph state or via tool calls**](#graph-state-vs-tool-calls)?
 - What if two agents have [**different state schemas**](#different-state-schemas)?
 - How to communicate over a [**shared message list**](#shared-message-list)?
 

docs/docs/concepts/persistence.md

@@ -232,7 +232,7 @@ from langgraph.store.memory import InMemoryStore
 in_memory_store = InMemoryStore()
 ```
 
-Memories are namespaced by a `tuple`, which in this specific example will be `(<user_id>, "memories")`. The namespace can be any length and represent anything, does not have be user specific.
+Memories are namespaced by a `tuple`, which in this specific example will be `(<user_id>, "memories")`. The namespace can be any length and represent anything, does not have to be user specific.
 
 ```python 
 user_id = "1"
@@ -387,6 +387,9 @@ We can access the memories and use them in our model call.
 def call_model(state: MessagesState, config: RunnableConfig, *, store: BaseStore):
     # Get the user id from the config
     user_id = config["configurable"]["user_id"]
+
+    # Namespace the memory
+    namespace = (user_id, "memories")
     
     # Search based on the most recent message
     memories = store.search(

docs/docs/concepts/v0-human-in-the-loop.md

@@ -1,3 +1,8 @@
+---
+search:
+  exclude: true
+---
+
 # Human-in-the-loop
 
 !!! note "Use the `interrupt` function instead."

docs/docs/how-tos/command.ipynb

@@ -33,7 +33,7 @@
     "    )\n",
     "```\n",
     "\n",
-    "If you are using [subgraphs](#subgraphs), you might want to navigate from a node a subgraph to a different subgraph (i.e. a different node in the parent graph). To do so, you can specify `graph=Command.PARENT` in `Command`:\n",
+    "If you are using [subgraphs](#subgraphs), you might want to navigate from a node within a subgraph to a different subgraph (i.e. a different node in the parent graph). To do so, you can specify `graph=Command.PARENT` in `Command`:\n",
     "\n",
     "```python\n",
     "def my_node(state: State) -> Command[Literal[\"my_other_node\"]]:\n",

docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb

@@ -397,7 +397,8 @@
     "# We define a fake node to ask the human\n",
     "def ask_human(state):\n",
     "    tool_call_id = state[\"messages\"][-1].tool_calls[0][\"id\"]\n",
-    "    location = interrupt(\"Please provide your location:\")\n",
+    "    ask = AskHuman.model_validate(state[\"messages\"][-1].tool_calls[0][\"args\"])\n",
+    "    location = interrupt(ask.question)\n",
     "    tool_message = [{\"tool_call_id\": tool_call_id, \"type\": \"tool\", \"content\": location}]\n",
     "    return {\"messages\": tool_message}\n",
     "\n",
@@ -491,7 +492,7 @@
     "        \"messages\": [\n",
     "            (\n",
     "                \"user\",\n",
-    "                \"Use the search tool to ask the user where they are, then look up the weather there\",\n",
+    "                \"Ask the user where they are, then look up the weather there\",\n",
     "            )\n",
     "        ]\n",
     "    },\n",

docs/docs/how-tos/memory/add-summary-conversation-history.ipynb

@@ -99,7 +99,7 @@
     "from typing import Literal\n",
     "\n",
     "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_core.messages import SystemMessage, RemoveMessage\n",
+    "from langchain_core.messages import SystemMessage, RemoveMessage, HumanMessage\n",
     "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.graph import MessagesState, StateGraph, START, END\n",
     "\n",

docs/docs/how-tos/memory/manage-conversation-history.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# How to manage conversation history\n",
     "\n",
-    "One of the most common use cases for persistence is to use it to keep track of conversation history. This is great - it makes it easy to continue conversations. As conversations get longer and longer, however, this conversation history can build up and take up more and more of the context window. This can often be undesirable as it leads to more expensive and longer calls to the LLM, and potentially ones that error. In order to prevent this from happening, you need to probably manage the conversation history.\n",
+    "One of the most common use cases for persistence is to use it to keep track of conversation history. This is great - it makes it easy to continue conversations. As conversations get longer and longer, however, this conversation history can build up and take up more and more of the context window. This can often be undesirable as it leads to more expensive and longer calls to the LLM, and potentially ones that error. In order to prevent this from happening, you need to properly manage the conversation history.\n",
     "\n",
     "Note: this guide focuses on how to do this in LangGraph, where you can fully customize how this is done. If you want a more off-the-shelf solution, you can look into functionality provided in LangChain:\n",
     "\n",

docs/docs/how-tos/pass-run-time-values-to-tools.ipynb

@@ -38,7 +38,7 @@
     "    </p>\n",
     "</div> \n",
     "\n",
-    "The core technique the examples below is to **annotate** a parameter as \"injected\", meaning it will be injected by your program and should not be seen or populated by the LLM. Let the following codesnippet serve as a tl;dr:\n",
+    "The core technique in the examples below is to **annotate** a parameter as \"injected\", meaning it will be injected by your program and should not be seen or populated by the LLM. Let the following codesnippet serve as a tl;dr:\n",
     "\n",
     "```python\n",
     "from typing import Annotated\n",

docs/docs/how-tos/react-agent-structured-output.ipynb

@@ -65,7 +65,7 @@
     "\n",
     "**Pros and Cons**\n",
     "\n",
-    "The benefit to this format is that you only need one LLM, and can save money and latency because of this. The downside to this option is that it isn't guaranteed that the single LLM will call the correct tool when you want it to. We can help the LLM by setting `tool_choice` to `any` when we use `bind_tools` which forces the LLM to select at least one tool at every turn, but this is far from a fool proof strategy. In addition, another downside is that the agent might call *multiple* tools, so we need to check for this explicitly in our routing function (or if we are using OpenAI we an set `parallell_tool_calling=False` to ensure only one tool is called at a time).\n",
+    "The benefit to this format is that you only need one LLM, and can save money and latency because of this. The downside to this option is that it isn't guaranteed that the single LLM will call the correct tool when you want it to. We can help the LLM by setting `tool_choice` to `any` when we use `bind_tools` which forces the LLM to select at least one tool at every turn, but this is far from a foolproof strategy. In addition, another downside is that the agent might call *multiple* tools, so we need to check for this explicitly in our routing function (or if we are using OpenAI we can set `parallell_tool_calling=False` to ensure only one tool is called at a time).\n",
     "\n",
     "**Option 2**\n",
     "\n",

docs/docs/how-tos/state-model.ipynb

@@ -266,6 +266,235 @@
     "    print(\"An exception was raised because bad_node sets `a` to an integer.\")\n",
     "    print(e)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2270bc3c",
+   "metadata": {},
+   "source": [
+    "## Multiple Nodes\n",
+    "\n",
+    "Run-time validation will also work in a multi-node graph. In the example below `bad_node` updates `a` to an integer. \n",
+    "\n",
+    "Because run-time validation occurs on **inputs**, the validation error will occur when `ok_node` is called (not when `bad_node` returns an update to the state which is inconsistent with the schema)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d832cdcc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import StateGraph, START, END\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "from pydantic import BaseModel\n",
+    "\n",
+    "\n",
+    "# The overall state of the graph (this is the public state shared across nodes)\n",
+    "class OverallState(BaseModel):\n",
+    "    a: str\n",
+    "\n",
+    "\n",
+    "def bad_node(state: OverallState):\n",
+    "    return {\n",
+    "        \"a\": 123  # Invalid\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def ok_node(state: OverallState):\n",
+    "    return {\"a\": \"goodbye\"}\n",
+    "\n",
+    "\n",
+    "# Build the state graph\n",
+    "builder = StateGraph(OverallState)\n",
+    "builder.add_node(bad_node)\n",
+    "builder.add_node(ok_node)\n",
+    "builder.add_edge(START, \"bad_node\")\n",
+    "builder.add_edge(\"bad_node\", \"ok_node\")\n",
+    "builder.add_edge(\"ok_node\", END)\n",
+    "graph = builder.compile()\n",
+    "\n",
+    "# Test the graph with a valid input\n",
+    "try:\n",
+    "    graph.invoke({\"a\": \"hello\"})\n",
+    "except Exception as e:\n",
+    "    print(\"An exception was raised because bad_node sets `a` to an integer.\")\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "456b1f77",
+   "metadata": {},
+   "source": [
+    "## Advanced Pydantic Model Usage\n",
+    "\n",
+    "This section covers more advanced topics when using Pydantic models with LangGraph.\n",
+    "\n",
+    "### Serialization Behavior\n",
+    "\n",
+    "When using Pydantic models as state schemas, it's important to understand how serialization works, especially when:\n",
+    "- Passing Pydantic objects as inputs\n",
+    "- Receiving outputs from the graph\n",
+    "- Working with nested Pydantic models\n",
+    "\n",
+    "Let's see these behaviors in action:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e919cdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import StateGraph, START, END\n",
+    "from pydantic import BaseModel\n",
+    "\n",
+    "\n",
+    "class NestedModel(BaseModel):\n",
+    "    value: str\n",
+    "\n",
+    "\n",
+    "class ComplexState(BaseModel):\n",
+    "    text: str\n",
+    "    count: int\n",
+    "    nested: NestedModel\n",
+    "\n",
+    "\n",
+    "def process_node(state: ComplexState):\n",
+    "    # Node receives a validated Pydantic object\n",
+    "    print(f\"Input state type: {type(state)}\")\n",
+    "    print(f\"Nested type: {type(state.nested)}\")\n",
+    "\n",
+    "    # Return a dictionary update\n",
+    "    return {\"text\": state.text + \" processed\", \"count\": state.count + 1}\n",
+    "\n",
+    "\n",
+    "# Build the graph\n",
+    "builder = StateGraph(ComplexState)\n",
+    "builder.add_node(\"process\", process_node)\n",
+    "builder.add_edge(START, \"process\")\n",
+    "builder.add_edge(\"process\", END)\n",
+    "graph = builder.compile()\n",
+    "\n",
+    "# Create a Pydantic instance for input\n",
+    "input_state = ComplexState(text=\"hello\", count=0, nested=NestedModel(value=\"test\"))\n",
+    "print(f\"Input object type: {type(input_state)}\")\n",
+    "\n",
+    "# Invoke graph with a Pydantic instance\n",
+    "result = graph.invoke(input_state)\n",
+    "print(f\"Output type: {type(result)}\")\n",
+    "print(f\"Output content: {result}\")\n",
+    "\n",
+    "# Convert back to Pydantic model if needed\n",
+    "output_model = ComplexState(**result)\n",
+    "print(f\"Converted back to Pydantic: {type(output_model)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f13f28ce",
+   "metadata": {},
+   "source": [
+    "### Runtime Type Coercion\n",
+    "\n",
+    "Pydantic performs runtime type coercion for certain data types. This can be helpful but also lead to unexpected behavior if you're not aware of it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "faf59316",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import StateGraph, START, END\n",
+    "from pydantic import BaseModel\n",
+    "\n",
+    "\n",
+    "class CoercionExample(BaseModel):\n",
+    "    # Pydantic will coerce string numbers to integers\n",
+    "    number: int\n",
+    "    # Pydantic will parse string booleans to bool\n",
+    "    flag: bool\n",
+    "\n",
+    "\n",
+    "def inspect_node(state: CoercionExample):\n",
+    "    print(f\"number: {state.number} (type: {type(state.number)})\")\n",
+    "    print(f\"flag: {state.flag} (type: {type(state.flag)})\")\n",
+    "    return {}\n",
+    "\n",
+    "\n",
+    "builder = StateGraph(CoercionExample)\n",
+    "builder.add_node(\"inspect\", inspect_node)\n",
+    "builder.add_edge(START, \"inspect\")\n",
+    "builder.add_edge(\"inspect\", END)\n",
+    "graph = builder.compile()\n",
+    "\n",
+    "# Demonstrate coercion with string inputs that will be converted\n",
+    "result = graph.invoke({\"number\": \"42\", \"flag\": \"true\"})\n",
+    "\n",
+    "# This would fail with a validation error\n",
+    "try:\n",
+    "    graph.invoke({\"number\": \"not-a-number\", \"flag\": \"true\"})\n",
+    "except Exception as e:\n",
+    "    print(f\"\\nExpected validation error: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2844475b",
+   "metadata": {},
+   "source": [
+    "### Working with Message Models\n",
+    "\n",
+    "When working with LangChain message types in your state schema, there are important considerations for serialization. You should use `AnyMessage` (rather than `BaseMessage`) for proper serialization/deserialization when using message objects over the wire:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd0734b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import StateGraph, START, END\n",
+    "from pydantic import BaseModel\n",
+    "from langchain_core.messages import HumanMessage, AIMessage, AnyMessage\n",
+    "from typing import List\n",
+    "\n",
+    "\n",
+    "class ChatState(BaseModel):\n",
+    "    messages: List[AnyMessage]\n",
+    "    context: str\n",
+    "\n",
+    "\n",
+    "def add_message(state: ChatState):\n",
+    "    return {\"messages\": state.messages + [AIMessage(content=\"Hello there!\")]}\n",
+    "\n",
+    "\n",
+    "builder = StateGraph(ChatState)\n",
+    "builder.add_node(\"add_message\", add_message)\n",
+    "builder.add_edge(START, \"add_message\")\n",
+    "builder.add_edge(\"add_message\", END)\n",
+    "graph = builder.compile()\n",
+    "\n",
+    "# Create input with a message\n",
+    "initial_state = ChatState(\n",
+    "    messages=[HumanMessage(content=\"Hi\")], context=\"Customer support chat\"\n",
+    ")\n",
+    "\n",
+    "result = graph.invoke(initial_state)\n",
+    "print(f\"Output: {result}\")\n",
+    "\n",
+    "# Convert back to Pydantic model to see message types\n",
+    "output_model = ChatState(**result)\n",
+    "for i, msg in enumerate(output_model.messages):\n",
+    "    print(f\"Message {i}: {type(msg).__name__} - {msg.content}\")"
+   ]
   }
  ],
  "metadata": {

docs/docs/how-tos/update-state-from-tools.ipynb

@@ -210,7 +210,7 @@
    "id": "cbb06aea-6654-4245-91f8-af6e8f2b5377",
    "metadata": {},
    "source": [
-    "Let's now add personalization: we'll respond differently to the user based on the state values AFTER the state has been updated from the tool. To achieve this, let's define a function that will dynamically construct the system prompt based on the graph state. It will be called ever time the LLM is called and the function output will be passed to the LLM:"
+    "Let's now add personalization: we'll respond differently to the user based on the state values AFTER the state has been updated from the tool. To achieve this, let's define a function that will dynamically construct the system prompt based on the graph state. It will be called every time the LLM is called and the function output will be passed to the LLM:"
    ]
   },
   {

docs/docs/index.md

@@ -20,7 +20,7 @@ title: Home
 </p>
 
 <style>
-h1 {
+.md-content h1 {
   display: none;
 }
 </style>

docs/docs/llms-txt-overview.md

@@ -0,0 +1,36 @@
+# LLMs-txt for LangGraph
+
+## Overview
+
+LangGraph provides documentation files in the [`llms.txt`](https://llmstxt.org/) format, specifically `llms.txt` and `llms-full.txt`. These files allow large language models (LLMs) and agents to access programming documentation and APIs, particularly useful within integrated development environments (IDEs).
+
+| Language Version | llms.txt                                                                                                   | llms-full.txt                                                                                                        |
+|------------------|------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|
+| LangGraph Python | [https://langchain-ai.github.io/langgraph/llms.txt](https://langchain-ai.github.io/langgraph/llms.txt)     | [https://langchain-ai.github.io/langgraph/llms-full.txt](https://langchain-ai.github.io/langgraph/llms-full.txt)     |
+| LangGraph JS     | [https://langchain-ai.github.io/langgraphjs/llms.txt](https://langchain-ai.github.io/langgraphjs/llms.txt) | [https://langchain-ai.github.io/langgraphjs/llms-full.txt](https://langchain-ai.github.io/langgraphjs/llms-full.txt) |
+
+## Differences Between `llms.txt` and `llms-full.txt`
+
+- **`llms.txt`** is an index file containing links with brief descriptions of the content. An LLM or agent must follow these links to access detailed information.
+
+- **`llms-full.txt`** includes all the detailed content directly in a single file, eliminating the need for additional navigation.
+
+A key consideration when using `llms-full.txt` is its size. For extensive documentation, this file may become too large to fit into an LLM's context window.
+
+## Using `llms.txt` via an MCP Server
+
+As of March 9, 2025, IDEs [do not yet have robust native support for `llms.txt`](https://x.com/jeremyphoward/status/1902109312216129905?t=1eHFv2vdNdAckajnug0_Vw&s=19). However, you can utilize `llms.txt` effectively through an MCP server.
+
+We provide an MCP server specifically designed to serve documentation, called [`mcpdoc`](https://github.com/langchain-ai/mcpdoc). This setup is compatible with IDEs and platforms such as Cursor, Windsurf, Claude, and Claude Code. Instructions for using `mcpdoc` with these tools are available in the repository.
+
+## Using `llms-full.txt`
+
+The LangGraph `llms-full.txt` file typically contains several hundred thousand tokens, exceeding the context window limitations of most LLMs. To effectively use this file:
+
+1. **With IDEs (e.g., Cursor, Windsurf)**:
+    - Add the `llms-full.txt` as custom documentation. The IDE will automatically chunk and index the content, implementing Retrieval-Augmented Generation (RAG).
+
+2. **Without IDE support**:
+    - Use a chat model with a large context window.
+    - Implement a RAG strategy to manage and query the documentation efficiently.
+

docs/docs/llms.txt

@@ -1,191 +1,204 @@
 # LangGraph
 
-## Quickstart
+## Tutorials
 
-These guides are designed to help you get started with LangGraph.
+[Learn the basics](https://langchain-ai.github.io/langgraph/tutorials/introduction/): LLM should read this page when needing to build a LangGraph chatbot or when learning about chat agents with memory, human-in-the-loop functionality, and state management. This page provides a comprehensive LangGraph quickstart tutorial covering building a support chatbot with web search capability, conversation memory, human review routing, custom state management, and time travel functionality to explore alternative conversation paths.
 
-- [LangGraph Quickstart](https://langchain-ai.github.io/langgraph/tutorials/introduction/): Build a chatbot that can use tools and keep track of conversation history. Add human-in-the-loop capabilities and explore how time-travel works.
-- [Common Workflows](https://langchain-ai.github.io/langgraph/tutorials/workflows/): Overview of the most common workflows using LLMs implemented with LangGraph.
-- [LangGraph Server Quickstart](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/): Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI.
-- [Deploy with LangGraph Cloud Quickstart](https://langchain-ai.github.io/langgraph/cloud/quick_start/): Deploy a LangGraph app using LangGraph Cloud.
+[Local Deploy](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/): LLM should read this page when setting up a LangGraph app locally using `langgraph dev` and troubleshooting LangGraph server deployment. This page contains a quickstart guide for launching a LangGraph server locally, including installation steps, app creation from templates, environment setup, API testing with Python/JS SDKs, and links to deployment options and further documentation.
 
-## Concepts
+[Workflows and Agents](https://langchain-ai.github.io/langgraph/tutorials/workflows/): LLM should read this page when implementing agent systems, designing workflow architectures, or troubleshooting LLM orchestration strategies. The page covers patterns for LLM system design, comparing workflows (predefined paths) vs agents (dynamic control), with implementations of prompt chaining, parallelization, routing, orchestrator-worker, evaluator-optimizer, and agent patterns using both graph and functional APIs in LangGraph.
 
-These guides provide explanations of the key concepts behind the LangGraph framework.
+## Concepts 
 
-- [Why LangGraph?](https://langchain-ai.github.io/langgraph/concepts/high_level/): Motivation for LangGraph, a library for building agentic applications with LLMs.
-- [LangGraph Glossary](https://langchain-ai.github.io/langgraph/concepts/low_level/): LangGraph workflows are designed as graphs, with nodes representing different components and edges representing the flow of information between them. This guide provides an overview of the key concepts associated with LangGraph graph primitives.
-- [Common Agentic Patterns](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/): An agent uses an LLM to pick its own control flow to solve more complex problems! Agents are a key building block in many LLM applications. This guide explains the different types of agent architectures and how they can be used to control the flow of an application.
-- [Multi-Agent Systems](https://langchain-ai.github.io/langgraph/concepts/multi_agent/): Complex LLM applications can often be broken down into multiple agents, each responsible for a different part of the application. This guide explains common patterns for building multi-agent systems.
-- [Breakpoints](https://langchain-ai.github.io/langgraph/concepts/breakpoints/): Breakpoints allow pausing the execution of a graph at specific points. Breakpoints allow stepping through graph execution for debugging purposes.
-- [Human-in-the-Loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Explains different ways of integrating human feedback into a LangGraph application.
-- [Time Travel](https://langchain-ai.github.io/langgraph/concepts/time-travel/): Time travel allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues.
-- [Persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/): LangGraph has a built-in persistence layer, implemented through checkpointers. This persistence layer helps to support powerful capabilities like human-in-the-loop, memory, time travel, and fault-tolerance.
-- [Memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Memory in AI applications refers to the ability to process, store, and effectively recall information from past interactions. With memory, your agents can learn from feedback and adapt to users' preferences.
-- [Streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/): Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
-- [Functional API](https://langchain-ai.github.io/langgraph/concepts/functional_api/): `@entrypoint` and `@task` decorators that allow you to add LangGraph functionality to an existing codebase.
-- [Durable Execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): LangGraph's built-in [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) layer provides durable execution for workflows, ensuring that the state of each execution step is saved to a durable store. 
-- [Pregel](https://langchain-ai.github.io/langgraph/concepts/pregel/): Pregel is LangGraph's runtime, which is responsible for managing the execution of LangGraph applications.
-- [FAQ](https://langchain-ai.github.io/langgraph/concepts/faq/): Frequently asked questions about LangGraph.
+[Concepts](https://langchain-ai.github.io/langgraph/concepts/): LLM should read this page when needing to understand LangGraph's key concepts or when planning to deploy LangGraph applications. Comprehensive guide covering LangGraph fundamentals (graph primitives, agents, multi-agent systems, breakpoints, persistence), features (time travel, memory, streaming), and LangGraph Platform deployment options (self-hosted, cloud, enterprise).
 
-## How-tos
+[Agent architectures](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/): LLM should read this page when designing agent architectures, implementing control flows for LLM applications, or customizing agent behavior patterns. This page covers different LLM agent architectures including routers, tool calling agents (ReAct), structured outputs, memory systems, planning capabilities, and advanced customization options like human-in-the-loop, parallelization, subgraphs, and reflection mechanisms.
 
-Here you’ll find answers to “How do I...?” types of questions. 
+[Application Structure](https://langchain-ai.github.io/langgraph/concepts/application_structure/): LLM should read this page when needing to understand LangGraph application structure, preparing to deploy a LangGraph application, or troubleshooting configuration issues. This page details the structure of LangGraph applications, including required components (graphs, langgraph.json config file, dependency files, optional .env), file organization patterns for Python/JavaScript projects, configuration file format with all supported fields, and how to specify dependencies, graphs, and environment variables.
 
-These guides are **goal-oriented** and concrete. 
+[Assistants](https://langchain-ai.github.io/langgraph/concepts/assistants/): LLM should read this page when looking for information about LangGraph assistants, understanding assistant configuration in LangGraph Platform, or learning about versioning agent configurations. This page explains LangGraph assistants, which allow developers to modify agent configurations (prompts, models, etc.) without changing graph logic, supports versioning for tracking changes, and is available only in LangGraph Platform (not open source).
 
-They're meant to help you complete a specific task.
+[Authentication & Access Control](https://langchain-ai.github.io/langgraph/concepts/auth/): LLM should read this page when implementing authentication in LangGraph Platform, designing access control for LangGraph applications, or troubleshooting security issues in LangGraph deployments. This page explains LangGraph's authentication and authorization system, covering the difference between authentication and authorization, system architecture, implementing custom auth handlers, common access patterns, and supported resources/actions for access control.
 
-### Graph API Basics
+[Bring Your Own Cloud (BYOC)](https://langchain-ai.github.io/langgraph/concepts/bring_your_own_cloud/): LLM should read this page when learning about LangGraph Platform deployment options, understanding Bring Your Own Cloud architecture, or managing deployments in AWS. This page explains LangGraph's BYOC deployment model, detailing how it separates control plane (managed by LangChain) from data plane (in customer's AWS account), outlines AWS requirements, infrastructure setup via Terraform, required permissions, and explains the deployment workflow.
 
-- [How to update graph state from nodes](https://langchain-ai.github.io/langgraph/how-tos/state-reducers/)
-- [How to create a sequence of steps](https://langchain-ai.github.io/langgraph/how-tos/sequence/)
-- [How to create branches for parallel execution](https://langchain-ai.github.io/langgraph/how-tos/branching/)
-- [How to create and control loops with recursion limits](https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/)
-- [How to visualize your graph](https://langchain-ai.github.io/langgraph/how-tos/visualization/)
+[Deployment Options](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): LLM should read this page when needing information about LangGraph deployment options, comparing different deployment methods, or understanding LangGraph Platform plans. This page outlines four deployment options for LangGraph Platform: Self-Hosted Lite (available for all plans), Self-Hosted Enterprise (Enterprise plan only), Cloud SaaS (Plus and Enterprise plans), and Bring Your Own Cloud (Enterprise plan only, AWS-only).
 
-### Fine-grained Control
+[Double Texting](https://langchain-ai.github.io/langgraph/concepts/double_texting/): LLM should read this page when handling concurrent user interactions in LangGraph Platform, implementing double-texting safeguards, or designing stateful conversation systems. This page explains four approaches to handling "double texting" in LangGraph (when users send a second message before the first completes): Reject, Enqueue, Interrupt, and Rollback, noting these features are currently only available in LangGraph Platform.
 
-These guides demonstrate LangGraph features that grant fine-grained control over the execution of your graph.
+[Durable Execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): LLM should read this page when needing to understand durable execution in LangGraph, implementing workflow persistence, or troubleshooting workflow resumption. This page explains durable execution in LangGraph: how workflows save progress to resume later, requirements (checkpointers and thread IDs), determinism guidelines for consistent replay, using tasks to encapsulate non-deterministic operations, and approaches for pausing/resuming workflows.
 
-- [How to create map-reduce branches for parallel execution](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/)
-- [How to update state and jump to nodes in graphs and subgraphs](https://langchain-ai.github.io/langgraph/how-tos/command/)
-- [How to add runtime configuration to your graph](https://langchain-ai.github.io/langgraph/how-tos/configuration/)
-- [How to add node retries](https://langchain-ai.github.io/langgraph/how-tos/node-retries/)
-- [How to return state before hitting recursion limit](https://langchain-ai.github.io/langgraph/how-tos/return-when-recursion-limit-hits/)
+[FAQ](https://langchain-ai.github.io/langgraph/concepts/faq/): LLM should read this page when needing to understand differences between LangGraph and LangChain, exploring deployment options for LangGraph Platform, or determining compatibility with various LLMs. FAQ covering LangGraph basics, comparisons with other frameworks, deployment options (free self-hosted, Cloud SaaS, BYOC, Enterprise), compatibility with different LLMs including OSS models, and feature differences between open-source LangGraph and proprietary LangGraph Platform.
 
-### Persistence
- 
-Persistence makes it easy to persist state across graph runs (per-thread persistence) and across threads (cross-thread persistence). 
+[Functional API](https://langchain-ai.github.io/langgraph/concepts/functional_api/): LLM should read this page when implementing workflows with persistent state, adding human-in-the-loop features, or converting existing code to use LangGraph. The page documents LangGraph's Functional API, which allows adding persistence, memory, and human-in-the-loop capabilities with minimal code changes using @entrypoint and @task decorators, handling serialization requirements, state management, and common patterns for parallel execution and error handling.
 
-These how-to guides show how to add persistence to your graph.
+[Why LangGraph?](https://langchain-ai.github.io/langgraph/concepts/high_level/): LLM should read this page when understanding LangGraph's core capabilities, exploring LLM application infrastructure, or evaluating agent/workflow persistence options. LangGraph provides infrastructure for LLM applications with three key benefits: persistence for memory and human-in-the-loop capabilities, streaming of workflow events and LLM outputs, and tools for debugging and deployment via LangGraph Platform.
 
-- [How to add thread-level persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/persistence/)
-- [How to add thread-level persistence to a subgraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph-persistence/)
-- [How to add cross-thread persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence/)
-- [How to use Postgres checkpointer for persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence_postgres/)
-- [How to use MongoDB checkpointer for persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence_mongodb/)
-- [How to create a custom checkpointer using Redis](https://langchain-ai.github.io/langgraph/how-tos/persistence_redis/)
+[Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): LLM should read this page when implementing human-in-the-loop workflows in LangGraph, designing approval systems with LLMs, or creating interactive multi-turn conversation agents. This page explains human-in-the-loop patterns in LangGraph using the interrupt function, showing how to pause graph execution for human review/input and resume with Command. Includes design patterns for approval workflows, state editing, tool call reviews, and multi-turn conversations, with code examples and warnings about execution flow and common pitfalls.
 
-See the below guides for how-to add persistence to your workflow using the [Functional API](https://langchain-ai.github.io/langgraph/concepts/functional_api/):
+[LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli/): LLM should read this page when looking for information about LangGraph CLI installation or when needing to deploy a LangGraph API server locally. The page covers LangGraph CLI installation methods (Homebrew, pip), key commands (build, dev, up, dockerfile), and features like hot reloading, debugger support, and database management for running LangGraph servers.
 
-- [How to add thread-level persistence (functional API)](https://langchain-ai.github.io/langgraph/how-tos/persistence-functional/)
-- [How to add cross-thread persistence (functional API)](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence-functional/)
+[Cloud SaaS](https://langchain-ai.github.io/langgraph/concepts/langgraph_cloud/): LLM should read this page when learning about LangGraph's Cloud SaaS offering, understanding deployment options for LangGraph Servers, or planning autoscaling infrastructure for LangGraph applications. This page describes LangGraph Cloud SaaS, a managed deployment service for LangGraph Servers with details on deployment types (Development/Production), revisions, persistence, autoscaling capabilities (up to 10 containers), LangSmith integration, IP whitelisting, and automatic deletion policies after 28 days of non-use.
 
-### Memory
+[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/): LLM should read this page when seeking information about LangGraph Platform's components or evaluating production deployment options for agentic applications. The page details the LangGraph Platform, a commercial solution for deploying agentic applications, including its components (Server, Studio, CLI, SDK, Remote Graph) and key benefits like streaming support, background runs, long run handling, burstiness management, and human-in-the-loop capabilities.
 
-LangGraph makes it easy to manage conversation memory in your graph. These how-to guides show how to implement different strategies for that.
+[LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server/): LLM should read this page when developing applications with LangGraph Server, deploying agent-based applications, or integrating persistent state management in agent workflows. LangGraph Server provides an API for creating and managing agent applications with key features like streaming endpoints, background runs, task queues, persistence, webhooks, cron jobs, and monitoring capabilities through a structured system of assistants, threads, runs, and stores.
 
-- [How to manage conversation history](https://langchain-ai.github.io/langgraph/how-tos/memory/manage-conversation-history/)
-- [How to delete messages](https://langchain-ai.github.io/langgraph/how-tos/memory/delete-messages/)
-- [How to add summary conversation memory](https://langchain-ai.github.io/langgraph/how-tos/memory/add-summary-conversation-history/)
-- [How to add long-term memory (cross-thread)](https://langchain-ai.github.io/langgraph/how-tos/memory/cross-thread-persistence/)
-- [How to use semantic search for long-term memory](https://langchain-ai.github.io/langgraph/how-tos/memory/semantic-search/)
+[LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/): LLM should read this page when looking for information about LangGraph Studio features, needing to troubleshoot LangGraph Studio issues, or learning how to connect a LangGraph application to the Studio. LangGraph Studio is a specialized agent IDE for visualizing, interacting with, and debugging LLM applications, offering features such as graph visualization, state editing, assistant management, and integration with LangSmith, with instructions for connecting via deployed applications or local development servers, plus troubleshooting FAQs.
 
-### Human-in-the-loop
+[LangGraph Glossary](https://langchain-ai.github.io/langgraph/concepts/low_level/): LLM should read this page when needing to understand LangGraph terminology, implementing agent workflows as graphs, or developing modular multi-step AI systems. The page covers core LangGraph concepts including StateGraph, nodes, edges, state management, messaging, persistence, configuration, human-in-the-loop features, subgraphs, and visualization capabilities.
 
-Human-in-the-loop functionality allows you to involve humans in the decision-making process of your graph.
+[Memory](https://langchain-ai.github.io/langgraph/concepts/memory/): LLM should read this page when implementing memory systems for AI agents, managing conversation context across sessions, or designing systems that require both short-term and long-term information retention. This page explains memory systems in LangGraph, covering short-term (thread-scoped) memory for managing conversation history and long-term memory across threads, with techniques for handling long conversations, summarizing past interactions, and organizing persistent memories in namespaces.
 
-These how-to guides show how to implement human-in-the-loop workflows in your graph.
+[Multi-agent Systems](https://langchain-ai.github.io/langgraph/concepts/multi_agent/): LLM should read this page when implementing multi-agent systems, troubleshooting complex agent architectures, or designing agent communication patterns. Multi-agent systems organize LLMs into modular architectures (network, supervisor, hierarchical, custom) with different communication patterns, using Command objects for handoffs between agents, and supporting various state management approaches.
 
-- [How to wait for user input](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/wait-user-input/): A basic example that shows how to implement a human-in-the-loop workflow in your graph using the `interrupt` function.
-- [How to review tool calls](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/review-tool-calls/): Incorporate human-in-the-loop for reviewing/editing/accepting tool call requests before they executed using the `interrupt` function.
-- [How to add static breakpoints](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): Use for debugging purposes. For human-in-the-loop workflows, we recommend the [`interrupt` function](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.interrupt) instead.
-- [How to edit graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/edit-graph-state/): Edit graph state using `graph.update_state` method. Use this if implementing a **human-in-the-loop** workflow via **static breakpoints**.
+[Persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/): LLM should read this page when needing to understand LangGraph persistence mechanisms, implementing stateful workflows, or managing conversation history across interactions. This page covers LangGraph's persistence features including checkpointers, threads, state snapshots, replay functionality, forking state, cross-thread memory via InMemoryStore, and semantic search capabilities for stored memories.
 
-See the below guides for how-to implement human-in-the-loop workflows with the Functional API.
+[LangGraph Platform Plans](https://langchain-ai.github.io/langgraph/concepts/plans/): LLM should read this page when determining LangGraph Platform pricing tiers, comparing deployment options, or researching features available across different plans. This page outlines LangGraph Platform plans (Developer, Plus, Enterprise), detailing deployment options, usage limitations, feature availability, and pricing structure for agentic application deployment.
 
-- [How to wait for user input (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/wait-user-input-functional/)
-- [How to review tool calls (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/review-tool-calls-functional/)
+[LangGraph Platform Architecture](https://langchain-ai.github.io/langgraph/concepts/platform_architecture/): LLM should read this page when needing to understand LangGraph Platform's technical architecture or troubleshooting deployment issues. The page details how LangGraph Platform uses Postgres for persistent storage of user/run data and Redis for worker communication (run cancellation, output streaming) and ephemeral metadata storage (retry attempts).
 
-### Time Travel
+[LangGraph's Runtime (Pregel)](https://langchain-ai.github.io/langgraph/concepts/pregel/): LLM should read this page when learning about LangGraph's runtime, implementing applications with Pregel directly, or understanding how LangGraph executes graph applications. Explains LangGraph's Pregel runtime which manages graph application execution through a three-phase process (Plan, Execution, Update), describes different channel types (LastValue, Topic, Context, BinaryOperatorAggregate), provides direct implementation examples, and contrasts the StateGraph API with the Functional API.
 
-[Time travel](https://langchain-ai.github.io/langgraph/concepts/time-travel/) allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues. These how-to guides show how to use time travel in your graph.
+[LangGraph Platform: Scalability & Resilience](https://langchain-ai.github.io/langgraph/concepts/scalability_and_resilience/): LLM should read this page when needing to understand LangGraph Platform's scaling capabilities, designing high-availability LangGraph deployments, or troubleshooting resilience issues. This page details LangGraph Platform's horizontal scaling features including stateless server instances, queue worker scaling, resilience mechanisms for handling crashes, and database failover strategies in Postgres and Redis.
 
-- [How to view and update past graph state](https://langchain-ai.github.io/langgraph/how-tos/time-travel/)
+[LangGraph SDK](https://langchain-ai.github.io/langgraph/concepts/sdk/): LLM should read this page when looking for installation instructions for LangGraph SDK, needing to choose between sync and async Python clients, or requiring SDK API references. The page covers LangGraph SDK installation for Python and JS, provides API reference links, explains the difference between synchronous and asynchronous Python clients, and includes code examples for both client types.
 
-### Streaming
+[Self-Hosted](https://langchain-ai.github.io/langgraph/concepts/self_hosted/): LLM should read this page when looking for LangGraph deployment options, understanding self-hosted versions, or seeking requirements for self-hosting LangGraph. This page details two self-hosted deployment options for LangGraph Platform: Self-Hosted Lite (limited to 1M nodes/year) and Self-Hosted Enterprise (full version requiring license). Includes requirements, deployment process using Redis/Postgres, Docker, and optional Kubernetes deployment via Helm chart.
 
-[Streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/) is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
+[Streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/): LLM should read this page when implementing streaming features in LangGraph applications, understanding different streaming modes, or building responsive LLM applications. This page explains streaming in LangGraph, covering the main types (workflow progress, LLM tokens, custom updates) and streaming modes (values, updates, custom, messages, debug, events), with details on how to use multiple modes simultaneously and differences between LangGraph library and Platform implementations.
 
-- [How to stream](https://langchain-ai.github.io/langgraph/how-tos/streaming/)
-- [How to stream LLM tokens](https://langchain-ai.github.io/langgraph/how-tos/streaming-tokens/)
-- [How to stream LLM tokens from specific nodes](https://langchain-ai.github.io/langgraph/how-tos/streaming-specific-nodes/)
-- [How to stream data from within a tool](https://langchain-ai.github.io/langgraph/how-tos/streaming-events-from-within-tools/)
-- [How to stream from subgraphs](https://langchain-ai.github.io/langgraph/how-tos/streaming-subgraphs/)
-- [How to disable streaming for models that don't support it](https://langchain-ai.github.io/langgraph/how-tos/disable-streaming/)
+[Template Applications](https://langchain-ai.github.io/langgraph/concepts/template_applications/): LLM should read this page when looking for LangGraph template applications, setting up a new LangGraph project, or finding reference implementations for agentic workflows. This page presents LangGraph template applications with installation requirements, available templates (including ReAct Agent, Memory Agent, Retrieval Agent, etc.), instructions for creating new apps using the CLI, deployment options, and links to further learning resources.
 
-### Tool calling
+[Time Travel ⏱️](https://langchain-ai.github.io/langgraph/concepts/time-travel/): LLM should read this page when debugging LLM-based agent behavior, analyzing decision-making paths, or exploring alternative execution branches in LangGraph. This page explains LangGraph's Time Travel debugging features: Replaying (reproducing past actions up to specific checkpoints) and Forking (creating alternative execution paths from specific points), with code examples for retrieving checkpoints, configuring replay, and creating forked states.
 
-[Tool calling](https://python.langchain.com/docs/concepts/tool_calling/) is a type of [chat model](https://python.langchain.com/docs/concepts/chat_models/) API.
+## How Tos
 
-It accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message.
+[How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): LLM should read this page when looking for specific implementation techniques in LangGraph or when trying to deploy LangGraph applications to production environments. This page contains an extensive collection of how-to guides for LangGraph, covering graph fundamentals, persistence, memory management, human-in-the-loop features, tool calling, multi-agent systems, streaming, and deployment options through LangGraph Platform.
 
-These how-to guides show common patterns for tool calling with LangGraph:
+[How to implement handoffs between agents](https://langchain-ai.github.io/langgraph/how-tos/agent-handoffs/): LLM should read this page when implementing multi-agent systems that require agent coordination, when building systems with specialized agents that need to work together, or when needing to implement handoffs between agents. This page explains how to implement handoffs between agents in LangGraph using Command objects, both directly from agent nodes and through specialized handoff tools, with code examples for creating multi-agent systems.
 
-- [How to call tools using ToolNode](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/)
-- [How to handle tool calling errors](https://langchain-ai.github.io/langgraph/how-tos/tool-calling-errors/)
-- [How to pass runtime values to tools](https://langchain-ai.github.io/langgraph/how-tos/pass-run-time-values-to-tools/)
-- [How to pass config to tools](https://langchain-ai.github.io/langgraph/how-tos/pass-config-to-tools/)
-- [How to update graph state from tools](https://langchain-ai.github.io/langgraph/how-tos/update-state-from-tools/)
-- [How to handle large numbers of tools](https://langchain-ai.github.io/langgraph/how-tos/many-tools/)
+[How to run a graph asynchronously](https://langchain-ai.github.io/langgraph/how-tos/async/): LLM should read this page when needing to implement asynchronous graph execution in LangGraph or when optimizing IO-bound LLM applications. This page explains how to convert synchronous graphs to asynchronous in LangGraph, including updating node definitions with async/await, using StateGraph with TypedDict, implementing conditional edges, and streaming results.
 
-### Subgraphs
+[How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](https://langchain-ai.github.io/langgraph/how-tos/autogen-integration/): LLM should read this page when integrating LangGraph with other agent frameworks, building multi-agent systems, or adding persistence features to agents. The page demonstrates how to combine LangGraph with AutoGen by calling AutoGen agents inside LangGraph nodes, showing code examples for setting up the integration with memory and conversation persistence.
 
-Subgraphs allow you to reuse an existing graph from another graph. 
+[How to integrate LangGraph (functional API) with AutoGen, CrewAI, and other frameworks](https://langchain-ai.github.io/langgraph/how-tos/autogen-integration-functional/): LLM should read this page when integrating LangGraph with other agent frameworks, building multi-agent systems with different frameworks, or adding LangGraph features to existing agent systems. This page demonstrates how to integrate LangGraph's functional API with AutoGen, including code examples for creating a workflow that calls AutoGen agents, leveraging LangGraph's memory and persistence features.
 
-These how-to guides show how to use subgraphs:
+[How to create branches for parallel node execution](https://langchain-ai.github.io/langgraph/how-tos/branching/): LLM should read this page when needing to implement parallel node execution in LangGraph, optimizing graph performance, or handling conditional branching in workflows. This page explains how to create branches for parallel execution in LangGraph using fan-out/fan-in mechanisms, reducer functions for state accumulation, handling exceptions during parallel execution, and implementing conditional branching logic between nodes.
 
-- [How to use subgraphs](https://langchain-ai.github.io/langgraph/how-tos/subgraph/)
-- [How to view and update state in subgraphs](https://langchain-ai.github.io/langgraph/how-tos/subgraphs-manage-state/)
-- [How to transform inputs and outputs of a subgraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph-transform-state/)
+[How to combine control flow and state updates with Command](https://langchain-ai.github.io/langgraph/how-tos/command): LLM should read this page when learning how to combine control flow with state updates in LangGraph, understanding Command objects, or navigating between parent graphs and subgraphs. This page explains how to use Command objects to simultaneously update state and control flow between nodes, demonstrates using Command.PARENT to navigate from subgraphs to parent graphs, and includes examples of implementing reducers for state updates across graph hierarchies.
 
-### Multi-agent
+[How to add runtime configuration to your graph](https://langchain-ai.github.io/langgraph/how-tos/configuration/): LLM should read this page when implementing runtime configuration for LangGraph, adding model selection options to agents, or enabling dynamic system messages. This page demonstrates how to configure LangGraph at runtime, including selecting different LLMs dynamically and adding custom configuration options like system messages through the configurable dictionary.
 
-Multi-agent systems are useful to break down complex LLM applications into multiple agents, each responsible for a different part of the application. 
+[How to use the pre-built ReAct agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/): LLM should read this page when implementing a ReAct agent, needing pre-built agent solutions, or learning how to integrate tools with LLM agents. This page covers how to use the pre-built ReAct agent in LangGraph, including setup instructions, creating a weather checking tool, implementing the agent architecture, and examples of running the agent with and without tool calls.
 
-These how-to guides show how to implement multi-agent systems in LangGraph:
+[How to add human-in-the-loop processes to the prebuilt ReAct agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent-hitl/): LLM should read this page when implementing human-in-the-loop processes for ReAct agents, debugging tool calls, or learning about interrupts in LangGraph. This guide demonstrates how to add human-in-the-loop functionality to prebuilt ReAct agents using interrupt_before=["tools"], working with MemorySaver checkpoints, and showing how to approve or edit tool calls before they execute.
 
-- [How to implement handoffs between agents](https://langchain-ai.github.io/langgraph/how-tos/agent-handoffs/)
-- [How to build a multi-agent network](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-network/)
-- [How to add multi-turn conversation in a multi-agent application](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-multi-turn-convo/)
+[How to add thread-level memory to a ReAct Agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent-memory/): LLM should read this page when adding memory to ReAct agents, implementing thread-level persistence in LangGraph, or building stateful conversational agents. This guide demonstrates how to add memory to a ReAct agent using LangGraph's checkpointer interface, with code examples showing MemorySaver implementation, thread_id configuration, and persistent chat context across multiple interactions.
 
-### State Management
+[How to return structured output from the prebuilt ReAct agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent-structured-output/): LLM should read this page when implementing structured output with ReAct agents, customizing agent response formats, or working with LangGraph agents. This page explains how to return structured output from prebuilt ReAct agents by providing a response_format parameter with a Pydantic schema, including examples with weather data and options for customizing the prompt.
 
-- [How to use Pydantic model as graph state](https://langchain-ai.github.io/langgraph/how-tos/state-model/)
-- [How to define input/output schema for your graph](https://langchain-ai.github.io/langgraph/how-tos/input_output_schema/)
-- [How to pass private state between nodes inside the graph](https://langchain-ai.github.io/langgraph/how-tos/pass_private_state/)
+[How to add a custom system prompt to the prebuilt ReAct agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent-system-prompt/): LLM should read this page when learning to customize ReAct agents, needing to add system prompts to agents, or working with LangGraph's prebuilt agents. This tutorial demonstrates how to add a custom system prompt to a prebuilt ReAct agent, with code examples showing model setup, tool creation, and using the prompt parameter in the create_react_agent function.
 
-### Other
+[How to add cross-thread persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence): LLM should read this page when needing to implement persistence across multiple threads in LangGraph, when storing user data between conversations, or when implementing shared memory in graph-based LLM applications. This page demonstrates how to use LangGraph's Store API to persist data across threads, including creating an InMemoryStore with embedding search capabilities, passing stores to graph nodes, and accessing user-specific memories in different conversation threads.
 
-- [How to run graph asynchronously](https://langchain-ai.github.io/langgraph/how-tos/async/)
-- [How to force tool-calling agent to structure output](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output/)
-- [How to pass custom LangSmith run ID for graph runs](https://langchain-ai.github.io/langgraph/how-tos/run-id-langsmith/)
-- [How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](https://langchain-ai.github.io/langgraph/how-tos/autogen-integration/)
+[How to add cross-thread persistence (functional API)](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence-functional): LLM should read this page when needing to implement cross-thread persistence in LangGraph functional API, storing user data across different conversation threads, or creating shared memory between workflows. This page explains how to add cross-thread persistence to LangGraph using the Store interface, including defining a store, configuring the entrypoint decorator, and implementing a workflow that can store and retrieve user information across different conversation threads.
 
-## Use cases 
+[How to do a Self-hosted deployment of LangGraph](https://langchain-ai.github.io/langgraph/how-tos/deploy-self-hosted/): LLM should read this page when implementing a self-hosted deployment of LangGraph, configuring required environment variables, or building Docker images for LangGraph applications. This page explains how to deploy LangGraph applications using Docker, covering environment requirements (Redis, Postgres), how to build Docker images with the LangGraph CLI, configuration using environment variables, and deployment options using Docker or Docker Compose.
 
-Explore practical implementations tailored for specific scenarios:
+[How to disable streaming for models that don't support it](https://langchain-ai.github.io/langgraph/how-tos/disable-streaming/): LLM should read this page when handling models that don't support streaming, implementing LangGraph with non-streaming models, or troubleshooting streaming errors with OpenAI's O1 models. This page explains how to use the disable_streaming=True parameter with ChatOpenAI to make non-streaming models work with LangGraph's astream_events API, with code examples showing the error case and proper implementation.
 
-### Chatbots
+[How to edit graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/edit-graph-state/): LLM should read this page when needing to implement human intervention in LangGraph workflows, wanting to edit graph state during execution, or implementing breakpoints in agent systems. This page explains how to edit graph state in LangGraph using breakpoints, including implementing human-in-the-loop interactions, setting up interruptions before specific nodes, and updating state during agent execution.
 
-- [Customer Support](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/): Build a multi-functional support bot for flights, hotels, and car rentals.
-- [Prompt Generation from User Requirements](https://langchain-ai.github.io/langgraph/tutorials/chatbots/information-gather-prompting/): Build an information gathering chatbot.
-- [Code Assistant](https://langchain-ai.github.io/langgraph/tutorials/code_assistant/langgraph_code_assistant/): Build a code analysis and generation assistant.
+[How to Review Tool Calls](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/review-tool-calls/): LLM should read this page when implementing human review of tool calls, creating interactive agent workflows, or building approval systems for AI actions. This page explains how to implement human-in-the-loop review for tool calls in LangGraph, including approving tool calls, modifying tool calls manually, and providing natural language feedback to agents with complete code examples and explanations.
 
-### RAG
+[How to view and update past graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/time-travel/): LLM should read this page when needing to access or modify past states in LangGraph, when debugging agent execution, or when implementing user interventions in agent workflows. This page demonstrates how to view and update past graph states in LangGraph using get_state and update_state methods, with examples of replaying execution from checkpoints and branching workflows.
 
-- [Agentic RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag/): Use an agent to figure out how to retrieve the most relevant information before using the retrieved information to answer the user's question.
-- [Adaptive RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag/): Adaptive RAG is a strategy for RAG that unites (1) query analysis with (2) active / self-corrective RAG. Implementation of: https://arxiv.org/abs/2403.14403
-    - For a version that uses a local LLM: [Adaptive RAG using local LLMs](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/)
-- [Corrective RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_crag/): Uses an LLM to grade the quality of the retrieved information from the given source, and if the quality is low, it will try to retrieve the information from another source. Implementation of: https://arxiv.org/pdf/2401.15884.pdf 
-    - For a version that uses a local LLM: [Corrective RAG using local LLMs](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_crag_local/)
-- [Self-RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag/): Self-RAG is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents and generations. Implementation of https://arxiv.org/abs/2310.11511.
-    - For a version that uses a local LLM: [Self-RAG using local LLMs](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag_local/) 
-- [SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/): Build a SQL agent that can answer questions about a SQL database.
+[How to wait for user input using interrupt](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/wait-user-input/): LLM should read this page when implementing wait-for-user functions in LangGraph, implementing human-in-the-loop interactions, or learning how to use the interrupt() function. This page explains how to pause graph execution to collect user input using LangGraph's interrupt() function, with examples of simple feedback collection and more complex agent interactions that ask clarifying questions.
 
-### Multi-Agent Systems
+[How to define input/output schema for your graph](https://langchain-ai.github.io/langgraph/how-tos/input_output_schema/): LLM should read this page when needing to define separate input/output schemas for LangGraph, implementing schema-based data filtering, or understanding schema definitions in StateGraph. This page explains how to define distinct input and output schemas for a StateGraph, showing how input schema validates the provided data structure while output schema filters internal data to return only relevant information, with code examples demonstrating implementation.
+
+[How to handle large numbers of tools](https://langchain-ai.github.io/langgraph/how-tos/many-tools/): LLM should read this page when handling large tool collections, implementing dynamic tool selection, or creating retrieval-based tool management in LangGraph. This page demonstrates how to manage large numbers of tools by using vector search to dynamically select relevant tools based on user queries, implementing tool selection nodes in LangGraph, and handling tool selection errors with retry mechanisms.
+
+[How to create map-reduce branches for parallel execution](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/): LLM should read this page when learning to implement parallel execution in LangGraph, creating map-reduce operations, or handling dynamic task decomposition. This guide explains how to use LangGraph's Send API to create map-reduce workflows, breaking tasks into parallel sub-tasks and recombining results, with examples showing joke generation across multiple subjects.
+
+[How to add summary of the conversation history](https://langchain-ai.github.io/langgraph/how-tos/memory/add-summary-conversation-history/): LLM should read this page when implementing conversation summarization, managing context windows, or building chatbots with memory management. This page demonstrates how to add summary functionality to conversation history using LangGraph, including checking conversation length, creating summaries, and removing old messages while maintaining context.
+
+[How to delete messages](https://langchain-ai.github.io/langgraph/how-tos/memory/delete-messages): LLM should read this page when attempting to manage message history in LangGraph, needing to delete specific messages from conversational state, or implementing memory management in LLM applications. This page explains how to delete messages from a LangGraph application using RemoveMessage modifiers, covering both manual deletion with message IDs and programmatic deletion within graph logic to maintain conversation history limits.
+
+[How to manage conversation history](https://langchain-ai.github.io/langgraph/how-tos/memory/manage-conversation-history/): LLM should read this page when managing conversation history in LangGraph, preventing context window issues, or implementing custom message filtering. This page explains how to manage conversation history in LangGraph to prevent context window overflow by implementing message filtering functions that control which messages are sent to the LLM.
+
+[How to add semantic search to your agent's memory](https://langchain-ai.github.io/langgraph/how-tos/memory/semantic-search/): LLM should read this page when implementing semantic search in agent memory, enabling memory-aware AI assistants, or configuring advanced memory retrieval systems. This page demonstrates how to add semantic search to LangGraph agent memory stores, covering basic setup with embeddings, storing memories, searching by semantic similarity, integrating memory in agents and ReAct agents, and advanced usage like multi-vector indexing and selective memory indexing.
+
+[How to add multi-turn conversation in a multi-agent application](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-multi-turn-convo/): LLM should read this page when implementing multi-turn conversations between agents, creating interactive agent systems with human input, or learning about langgraph interrupts and agent handoffs. This page demonstrates how to build a multi-agent system with multi-turn conversations, including human-in-the-loop interactions, agent handoffs, and state management using LangGraph, Command objects, and interrupts.
+
+[How to add multi-turn conversation in a multi-agent application (functional API)](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-multi-turn-convo-functional/): LLM should read this page when building multi-turn conversational agents, implementing agent-to-agent handoffs, or using interrupts to collect user input in LangGraph. This guide demonstrates how to create a multi-agent system with multi-turn conversations using LangGraph's functional API, featuring agent handoffs, interrupt mechanics for user input, and a complete example of travel and hotel advisor agents that can transfer control between each other.
+
+[How to build a multi-agent network](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-network/): LLM should read this page when implementing multi-agent networks, setting up agent communication via handoffs, or building travel assistance agents. This page explains how to create a fully-connected multi-agent network with LangGraph where agents can communicate with each other via handoffs, including custom agent implementation and using prebuilt ReAct agents with tools.
+
+[How to build a multi-agent network (functional API)](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-network-functional/): LLM should read this page when building multi-agent systems, implementing agent handoffs between specialists, or creating fully-connected agent networks. This guide demonstrates how to create a multi-agent network using LangGraph's functional API, with tasks for individual agents and entrypoint functions to manage agent handoffs based on tool calls.
+
+[How to add node retry policies](https://langchain-ai.github.io/langgraph/how-tos/node-retries/): LLM should read this page when implementing error handling in LangGraph nodes, configuring API retry mechanisms, or troubleshooting node failures in graph workflows. Shows how to add custom retry policies to LangGraph nodes, including specifying which exceptions to retry on, setting max attempts, intervals, backoff factors, and implementing different retry behaviors for different node types.
+
+[How to pass config to tools](https://langchain-ai.github.io/langgraph/how-tos/pass-config-to-tools/): LLM should read this page when implementing secure tool configuration in LangChain, passing user-specific parameters to tools, or configuring tools with runtime values. This page explains how to pass configuration to LangChain tools using RunnableConfig, allowing application-controlled values (like user IDs) to be securely passed to tools without LLM control, with examples of implementing tools that access user-specific data.
+
+[How to pass private state between nodes](https://langchain-ai.github.io/langgraph/how-tos/pass_private_state/): LLM should read this page when implementing data sharing between specific nodes in LangGraph, handling private state in graph workflows, or designing multi-node sequential processes with selective data visibility. This page demonstrates how to pass private data between specific nodes in a LangGraph without making it part of the main schema, using typed dictionaries to define both public and private states, and showing a three-node example where private data flows only between the first two nodes.
+
+[How to add thread-level persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/persistence/): LLM should read this page when implementing persistence in LangGraph, needing to preserve context across user interactions, or learning about thread-level state management. This page explains how to add thread-level persistence to LangGraph applications using MemorySaver, including code examples for creating stateful conversations where context is maintained across multiple interactions.
+
+[How to add thread-level persistence (functional API)](https://langchain-ai.github.io/langgraph/how-tos/persistence-functional/): LLM should read this page when implementing thread-level persistence in LangGraph, creating conversational agents with memory, or using functional API with state management. This page explains how to add thread-level persistence to LangGraph functional API workflows using checkpointers, including code examples for creating a simple chatbot with memory across conversation turns.
+
+[How to use MongoDB checkpointer for persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence_mongodb/): LLM should read this page when implementing persistence in LangGraph agents, setting up MongoDB for state checkpointing, or working with MongoDB connections in LangGraph applications. This page explains how to use the MongoDB checkpointer for LangGraph persistence, covering connection methods (direct, client-based, async), basic setup requirements, and practical examples of saving and retrieving agent state between interactions.
+
+[How to use Postgres checkpointer for persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence_postgres/): LLM should read this page when setting up persistence for LangGraph agents, implementing PostgreSQL as a checkpoint storage backend, or working with either synchronous or asynchronous database connections. This page details how to use PostgreSQL for persisting LangGraph agent state, covering setup and configuration of PostgresSaver and AsyncPostgresSaver with different connection methods (pool, direct connection, connection string).
+
+[How to create a custom checkpointer using Redis](https://langchain-ai.github.io/langgraph/how-tos/persistence_redis/): LLM should read this page when implementing persistence in LangGraph applications, creating custom checkpoint mechanisms for agents, or working with Redis as a storage backend. This page demonstrates how to create custom checkpointers for LangGraph agents using Redis, including implementations for both synchronous and asynchronous interfaces that save and retrieve agent state.
+
+[How to create a ReAct agent from scratch](https://langchain-ai.github.io/langgraph/how-tos/react-agent-from-scratch/): LLM should read this page when needing to create a custom ReAct agent, wanting more control than prebuilt agents, or implementing ReAct from scratch with LangGraph. This guide shows how to build a custom ReAct agent using LangGraph, covering state definition, model/tool setup, node/edge configuration, graph creation, and testing the implementation with a weather query example.
+
+[How to create a ReAct agent from scratch (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/react-agent-from-scratch-functional): LLM should read this page when creating a ReAct agent using LangGraph's Functional API, implementing tool-calling workflows, or building conversational agents with thread persistence. This page explains how to build a ReAct agent from scratch using LangGraph's Functional API, including model and tool setup, defining tasks for model/tool calling, creating an entrypoint for orchestration, and adding thread-level persistence for conversational experiences.
+
+[How to force tool-calling agent to structure output](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output): LLM should read this page when needing to force tool-calling agents to produce structured output, implementing consistent output formats for downstream software, or choosing between single-LLM vs two-LLM structured output approaches. The page explains two methods for implementing structured output with tool-calling agents: binding output as a tool (single LLM approach) and using two LLMs with structured output conversion, with code examples for both approaches using LangGraph.
+
+[How to create and control loops](https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/): LLM should read this page when building loops in computational graphs, needing to implement termination conditions, or handling recursion limits in LangGraph. The page explains how to create graphs with loops using conditional edges for termination, set recursion limits, handle GraphRecursionError, and implement complex loops with branches.
+
+[How to review tool calls (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/review-tool-calls-functional/): LLM should read this page when implementing human review of tool calls, creating ReAct agents with Functional API, or adding human-in-the-loop workflows. This page demonstrates how to review tool calls before execution in a ReAct agent using LangGraph's Functional API, including accepting, revising, or generating custom tool messages with the interrupt function.
+
+[How to pass custom run ID or set tags and metadata for graph runs in LangSmith](https://langchain-ai.github.io/langgraph/how-tos/run-id-langsmith/): LLM should read this page when needing to customize trace information in LangSmith for LangGraph runs or when debugging graph runs with custom identifiers. The page explains how to pass custom run_id, set tags, add metadata, and customize run names for LangGraph traces in LangSmith using RunnableConfig, with examples showing implementation with a ReAct agent.
+
+[How to create a sequence of steps](https://langchain-ai.github.io/langgraph/how-tos/sequence/): LLM should read this page when implementing sequential workflows in LangGraph, creating multi-step processes in applications, or learning about state management in graph-based systems. This page explains how to create sequences in LangGraph, covering methods for building sequential graphs using .add_node/.add_edge or the shorthand .add_sequence, defining state with TypedDict, creating nodes as functions that update state, and compiling/invoking graphs with examples.
+
+[How to use Pydantic model as graph state](https://langchain-ai.github.io/langgraph/how-tos/state-model): LLM should read this page when implementing Pydantic models for state validation in LangGraph, handling complex state schema definitions, or troubleshooting validation errors in graph nodes. This guide explains how to use Pydantic BaseModel as a state schema in LangGraph for runtime validation, covering basic implementation, limitations, validation behavior across multiple nodes, serialization patterns, type coercion, and working with message models.
+
+[How to update graph state from nodes](https://langchain-ai.github.io/langgraph/how-tos/state-reducers/): LLM should read this page when needing to update state in LangGraph, designing graphs with nodes that modify state, or implementing reducers for state management. This page explains how to define state schemas in LangGraph using TypedDict, how nodes can update state, and how to use reducers to control state updates, with specific examples using message handling.
+
+[How to stream](https://langchain-ai.github.io/langgraph/how-tos/streaming/): LLM should read this page when needing to implement streaming in LangGraph applications, understanding different streaming modes, or troubleshooting LLM response delivery. This page explains how to stream LLM outputs using LangGraph, covering different streaming modes (values, updates, custom, messages, debug), with code examples for each mode and how to combine multiple streaming modes.
+
+[How to stream data from within a tool](https://langchain-ai.github.io/langgraph/how-tos/streaming-events-from-within-tools/): LLM should read this page when implementing streaming functionality in tools, integrating LLM outputs with custom data streams, or developing LangGraph applications with real-time feedback. This page explains how to stream data from within tools using LangGraph, covering custom data streaming with stream_mode="custom", LLM token streaming with stream_mode="messages", and implementation approaches both with and without LangChain.
+
+[How to stream LLM tokens from specific nodes](https://langchain-ai.github.io/langgraph/how-tos/streaming-specific-nodes/): LLM should read this page when needing to filter token streaming from specific nodes in LangGraph, implementing selective streaming in multi-node workflows, or controlling which node outputs are displayed. Guide explains how to stream LLM tokens from specific nodes using stream_mode="messages" and filtering by the langgraph_node metadata field, with complete code examples for implementing this in StateGraph applications.
+
+[How to stream from subgraphs](https://langchain-ai.github.io/langgraph/how-tos/streaming-subgraphs/): LLM should read this page when needing to stream outputs from subgraphs in LangGraph, implementing nested graph streaming, or debugging hierarchical graph execution. This page explains how to stream outputs from subgraphs in LangGraph by using the subgraphs=True parameter in the parent graph's stream() method, with a complete code example showing the difference between regular streaming and subgraph streaming.
+
+[How to stream LLM tokens from your graph](https://langchain-ai.github.io/langgraph/how-tos/streaming-tokens): LLM should read this page when needing to stream LLM tokens from a LangGraph application, implementing custom token streaming, or filtering streamed outputs. This page explains how to stream individual LLM tokens from LangGraph nodes using graph.stream() with different stream_mode options, including examples with and without LangChain, async implementations, and how to filter streamed tokens using metadata.
+
+[How to use subgraphs](https://langchain-ai.github.io/langgraph/how-tos/subgraph/): LLM should read this page when building complex systems with subgraphs, implementing multi-agent systems, or needing to share state between parent graphs and subgraphs. The page explains two methods for using subgraphs: adding compiled subgraphs when schemas share keys, and invoking subgraphs via node functions when schemas differ, with code examples for both approaches.
+
+[How to add thread-level persistence to a subgraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph-persistence/): LLM should read this page when implementing persistence in nested LangGraph architectures, adding thread-level storage to subgraphs, or debugging state propagation in LangGraph applications. This guide demonstrates how to add thread-level persistence to subgraphs by passing a checkpointer only to the parent graph during compilation, accessing persisted states from both parent and child graphs, and retrieving subgraph state using the proper configuration parameters.
+
+[How to transform inputs and outputs of a subgraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph-transform-state/): LLM should read this page when needing to work with nested subgraphs, transforming state between parent and child graphs, or integrating independent state components in LangGraph. This page demonstrates how to transform inputs and outputs between parent graphs and subgraphs with different state structures, showing implementation of three nested graphs (parent, child, grandchild) with separate state dictionaries and transformation functions.
+
+[How to view and update state in subgraphs](https://langchain-ai.github.io/langgraph/how-tos/subgraphs-manage-state/): LLM should read this page when working with state management in nested subgraphs, implementing human-in-the-loop patterns, or debugging complex graph flows. This guide covers viewing and updating state in LangGraph subgraphs, including how to resume execution from breakpoints, modify subgraph state, act as specific nodes, and work with multi-level nested subgraphs.
+
+[How to call tools using ToolNode](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/): LLM should read this page when learning how to implement tool calling with LangGraph, when working with the ToolNode component, or when building ReAct agents. This page covers using LangGraph's ToolNode for tool calling, including setup, manual invocation, working with chat models, building a ReAct agent, handling single and parallel tool calls, and error handling.
+
+[How to handle tool calling errors](https://langchain-ai.github.io/langgraph/how-tos/tool-calling-errors/): LLM should read this page when handling tool call errors, implementing error handling for LLM-tool interactions, or creating fallback strategies for failed tool calls. This page covers strategies for handling tool calling errors in LangGraph, including using the prebuilt ToolNode with built-in error handling, implementing custom error handling patterns, and fallback mechanisms with model upgrades when tools fail.
+
+[How to update graph state from tools](https://langchain-ai.github.io/langgraph/how-tos/update-state-from-tools/): LLM should read this page when needing to update graph state from tools in LangGraph, implementing personalized responses based on tool updates, or using Command objects to modify state. This page details how to update graph state from tools using Command objects, creating personalized agents with state tracking, and implementing dynamic prompt construction based on updated state values.
+
+[How to interact with the deployment using RemoteGraph](https://langchain-ai.github.io/langgraph/how-tos/use-remote-graph/): LLM should read this page when needing to interact with LangGraph Platform deployments remotely, when implementing RemoteGraph interfaces, or when using deployed graphs as subgraphs. This page explains how to use RemoteGraph to interact with LangGraph Platform deployments, covering initialization methods (URL-based or client-based), synchronous/asynchronous invocation, thread-level persistence, and using RemoteGraph as a subgraph in larger applications.
+
+[How to visualize your graph](https://langchain-ai.github.io/langgraph/how-tos/visualization): LLM should read this page when needing to visualize LangGraph graphs, looking for graph visualization methods, or working with graph visualization in Python. Comprehensive guide for visualizing graphs in LangGraph with multiple methods: Mermaid syntax, Mermaid.ink API for PNG rendering, Pyppeteer-based visualization, and Graphviz, with customization options for colors, styles, and layout.
+
+[How to wait for user input (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/wait-user-input-functional/): LLM should read this page when implementing human-in-the-loop workflows, integrating user input into agent systems, or adding interruption capabilities to LangGraph applications. The page explains how to use the `interrupt()` function in LangGraph's Functional API to pause execution for human input, with examples for both simple workflows and ReAct agents, including code implementations with checkpointing.
 
-- [Network](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/multi-agent-collaboration/): Enable two or more agents to collaborate on a task
-- [Supervisor](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/): Use an LLM to orchestrate and delegate to individual agents
-- [Hierarchical Teams](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/): Orchestrate nested teams of agents to solve problems

docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb

@@ -125,7 +125,7 @@
     "\n",
     "### Code solution\n",
     "\n",
-    "First, we will try OpenAI and [Claude3](https://docs.anthropic.com/en/docs/about-claude/models) with function calling.\n",
+    "First, we will try OpenAI and [Claude3](https://python.langchain.com/docs/integrations/providers/anthropic/) with function calling.\n",
     "\n",
     "We will create a `code_gen_chain` w/ either OpenAI or Claude and test them here."
    ]

docs/docs/tutorials/deployment.md

@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Deployment
 
 Get started deploying your LangGraph applications locally or on the cloud with

docs/docs/tutorials/storm/storm.ipynb

@@ -153,7 +153,7 @@
     "\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
-    "from pydantic import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field, field_validator\n",
     "\n",
     "direct_gen_outline_prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
@@ -336,6 +336,10 @@
     "        description=\"Description of the editor's focus, concerns, and motives.\",\n",
     "    )\n",
     "\n",
+    "    @field_validator(\"name\", mode=\"before\")\n",
+    "    def sanitize_name(cls, value: str) -> str:\n",
+    "        return value.replace(\" \", \"\").replace(\".\", \"\")\n",
+    "\n",
     "    @property\n",
     "    def persona(self) -> str:\n",
     "        return f\"Name: {self.name}\\nRole: {self.role}\\nAffiliation: {self.affiliation}\\nDescription: {self.description}\\n\"\n",
@@ -362,9 +366,9 @@
     "    ]\n",
     ")\n",
     "\n",
-    "gen_perspectives_chain = gen_perspectives_prompt | ChatOpenAI(\n",
-    "    model=\"gpt-3.5-turbo\"\n",
-    ").with_structured_output(Perspectives)"
+    "gen_perspectives_chain = gen_perspectives_prompt | fast_llm.with_structured_output(\n",
+    "    Perspectives, method=\"function_calling\"\n",
+    ")"
    ]
   },
   {
@@ -451,7 +455,7 @@
     }
    ],
    "source": [
-    "perspectives.dict()"
+    "perspectives.model_dump()"
    ]
   },
   {
@@ -559,7 +563,7 @@
     "    converted = []\n",
     "    for message in state[\"messages\"]:\n",
     "        if isinstance(message, AIMessage) and message.name != name:\n",
-    "            message = HumanMessage(**message.dict(exclude={\"type\"}))\n",
+    "            message = HumanMessage(**message.model_dump(exclude={\"type\"}))\n",
     "        converted.append(message)\n",
     "    return {\"messages\": converted}\n",
     "\n",
@@ -637,9 +641,9 @@
     "        MessagesPlaceholder(variable_name=\"messages\", optional=True),\n",
     "    ]\n",
     ")\n",
-    "gen_queries_chain = gen_queries_prompt | ChatOpenAI(\n",
-    "    model=\"gpt-3.5-turbo\"\n",
-    ").with_structured_output(Queries, include_raw=True)"
+    "gen_queries_chain = gen_queries_prompt | fast_llm.with_structured_output(\n",
+    "    Queries, include_raw=True, method=\"function_calling\"\n",
+    ")"
    ]
   },
   {
@@ -1695,6 +1699,13 @@
     "# We will down-header the sections to create less confusion in this notebook\n",
     "Markdown(article.replace(\"\\n#\", \"\\n##\"))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

docs/docs/tutorials/workflows/index.md

@@ -1,6 +1,6 @@
 # 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 [here](https://www.anthropic.com/research/building-effective-agents) by Anthropic:
+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:
 
 > Workflows are systems where LLMs and tools are orchestrated through predefined code paths.
 > Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.
@@ -9,7 +9,7 @@ Here is a simple way to visualize these differences:
 
 ![Agent Workflow](../../concepts/img/agent_workflow.png)
 
-When building agents and workflows, LangGraph [offers a number of benefits](https://langchain-ai.github.io/langgraph/concepts/high_level/) including persistence, streaming, and support for debugging as well as deployment.
+When building agents and workflows, LangGraph offers a number of benefits including persistence, streaming, and support for debugging as well as deployment.
 
 ## Set up
 
@@ -41,7 +41,7 @@ llm = ChatAnthropic(model="claude-3-5-sonnet-latest")
 
 ## Building Blocks: The Augmented LLM 
 
-LLM have [augmentations](https://www.anthropic.com/research/building-effective-agents) that support building workflows and agents. These include [structured outputs](https://python.langchain.com/docs/concepts/structured_outputs/) and [tool calling](https://python.langchain.com/docs/concepts/tool_calling/), as shown in this image from the Anthropic [blog](https://www.anthropic.com/research/building-effective-agents):
+LLM have augmentations that support building workflows and agents. These include [structured outputs](https://python.langchain.com/docs/concepts/structured_outputs/) and [tool calling](https://python.langchain.com/docs/concepts/tool_calling/), as shown in this image from the Anthropic blog on `Building Effective Agents`:
 
 ![augmented_llm.png](./img/augmented_llm.png)
 
@@ -81,7 +81,7 @@ msg.tool_calls
 
 In prompt chaining, each LLM call processes the output of the previous one. 
 
-As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): 
+As noted in the Anthropic blog on `Building Effective Agents`: 
 
 > Prompt chaining decomposes a task into a sequence of steps, where each LLM call processes the output of the previous one. You can add programmatic checks (see "gate” in the diagram below) on any intermediate steps to ensure that the process is still on track.
 
@@ -392,7 +392,7 @@ With parallelization, LLMs work simultaneously on a task:
 
 ## Routing
 
-Routing classifies an input and directs it to a followup task. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): 
+Routing classifies an input and directs it to a followup task. As noted in the Anthropic blog on `Building Effective Agents`: 
 
 > Routing classifies an input and directs it to a specialized followup task. This workflow allows for separation of concerns, and building more specialized prompts. Without this workflow, optimizing for one kind of input can hurt performance on other inputs.
 
@@ -603,7 +603,7 @@ Routing classifies an input and directs it to a followup task. As noted in the [
 
 ## Orchestrator-Worker
 
-With orchestrator-worker, an orchestrator breaks down a task and delegates each sub-task to workers. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): 
+With orchestrator-worker, an orchestrator breaks down a task and delegates each sub-task to workers. As noted in the Anthropic blog on `Building Effective Agents`: 
 
 > In the orchestrator-workers workflow, a central LLM dynamically breaks down tasks, delegates them to worker LLMs, and synthesizes their results.
 
@@ -948,7 +948,7 @@ In the evaluator-optimizer workflow, one LLM call generates a response while ano
 
     **Examples**
 
-    [Here](https://github.com/langchain-ai/research-rabbit) is an assistant that uses evaluator-optimizer to improve a report. See our video [here](https://www.youtube.com/watch?v=XGuTzHoqlj8).
+    [Here](https://github.com/langchain-ai/local-deep-researcher) is an assistant that uses evaluator-optimizer to improve a report. See our video [here](https://www.youtube.com/watch?v=XGuTzHoqlj8).
 
     [Here](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/) is a RAG workflow that grades answers for hallucinations or errors. See our video [here](https://www.youtube.com/watch?v=bq1Plo2RhYI).
 
@@ -1012,7 +1012,7 @@ In the evaluator-optimizer workflow, one LLM call generates a response while ano
 
 ## Agent
 
-Agents are typically implemented as an LLM performing actions (via tool-calling) based on environmental feedback in a loop. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents):
+Agents are typically implemented as an LLM performing actions (via tool-calling) based on environmental feedback in a loop. As noted in the Anthropic blog on `Building Effective Agents`:
 
 > Agents can handle sophisticated tasks, but their implementation is often straightforward. They are typically just LLMs using tools based on environmental feedback in a loop. It is therefore crucial to design toolsets and their documentation clearly and thoughtfully.
 

docs/mkdocs.yml

@@ -54,7 +54,7 @@ theme:
     code: "Roboto Mono"
 plugins:
   - search:
-      separator: '[\s\u200b\-_,:!=\[\]()"`/]+|\.(?!\d)|&[lg]t;|(?!\b)(?=[A-Z][a-z])'
+      separator: '[\s\u200b\-,:!=\[\]()"`/]+|\.(?!\d)|&[lg]t;'
   - autorefs
   - mkdocstrings:
       handlers:
@@ -361,6 +361,7 @@ nav:
       # NOTE: prebuilt.md is auto-generated by `make build-prebuilt`
       - Prebuilt Agents: prebuilt.md
       - Companies using LangGraph: adopters.md
+      - LLMS-txt: llms-txt-overview.md
       - FAQ: concepts/faq.md
       - Troubleshooting:
           - Troubleshooting: troubleshooting/errors/index.md

docs/pyproject.toml

@@ -10,6 +10,7 @@ readme = "README.md"
 python = "^3.10"
 aiohappyeyeballs = "2.4.3"
 hub = "^3.0.1"
+xxhash = "^3.5.0"
 
 [tool.poetry.group.docs.dependencies]
 langgraph = { path = "../libs/langgraph/", develop = true }

libs/checkpoint-postgres/README.md

@@ -25,7 +25,7 @@ with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
     # call .setup() the first time you're using the checkpointer
     checkpointer.setup()
     checkpoint = {
-        "v": 1,
+        "v": 2,
         "ts": "2024-07-31T20:14:19.804150+00:00",
         "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
         "channel_values": {
@@ -67,7 +67,7 @@ from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
 
 async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
     checkpoint = {
-        "v": 1,
+        "v": 2,
         "ts": "2024-07-31T20:14:19.804150+00:00",
         "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
         "channel_values": {

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

@@ -78,7 +78,7 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
             await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False)  # don't index
 
             # Search by similarity
-            results = await store.asearch(("docs",), "programming guides", limit=2)
+            results = await store.asearch(("docs",), query="programming guides", limit=2)
         ```
 
         Using connection pooling for better performance:

libs/checkpoint-postgres/langgraph/store/postgres/base.py

@@ -79,12 +79,12 @@ CREATE INDEX CONCURRENTLY IF NOT EXISTS store_prefix_idx ON store USING btree (p
     """
 -- Add expires_at column to store table
 ALTER TABLE store
-ADD COLUMN expires_at TIMESTAMP WITH TIME ZONE,
-ADD COLUMN ttl_minutes INT;
+ADD COLUMN IF NOT EXISTS expires_at TIMESTAMP WITH TIME ZONE,
+ADD COLUMN IF NOT EXISTS ttl_minutes INT;
 """,
     """
 -- Add indexes for efficient TTL sweeping
-CREATE INDEX idx_store_expires_at ON store (expires_at)
+CREATE INDEX IF NOT EXISTS idx_store_expires_at ON store (expires_at)
 WHERE expires_at IS NOT NULL;
 """,
 ]

libs/checkpoint-postgres/poetry.lock

@@ -397,7 +397,7 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.18"
+version = "2.0.21"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -1404,4 +1404,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9.0,<4.0"
-content-hash = "369bfffecb9489835b43b8255932e043176a11d2f639aad2d055ffd89263ca1e"
+content-hash = "4b0efdd115566f294fcd876334f9c3787aafc81f2689473759d88189a71d4635"

libs/checkpoint-postgres/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.17"
+version = "2.0.19"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 license = "MIT"
@@ -10,7 +10,7 @@ packages = [{ include = "langgraph" }]
 
 [tool.poetry.dependencies]
 python = "^3.9.0,<4.0"
-langgraph-checkpoint = "^2.0.15"
+langgraph-checkpoint = "^2.0.21"
 orjson = ">=3.10.1"
 psycopg = "^3.2.0"
 psycopg-pool = "^3.2.0"

libs/checkpoint-postgres/tests/test_async_store.py

@@ -60,15 +60,17 @@ async def store(request) -> AsyncIterator[AsyncPostgresStore]:
         ) as store:
             store.MIGRATIONS = [
                 (
-                    mig.replace(
-                        "ADD COLUMN ttl_minutes INT;", "ADD COLUMN ttl_minutes FLOAT;"
-                    )
+                    mig.replace("ttl_minutes INT;", "ttl_minutes FLOAT;")
                     if isinstance(mig, str)
                     else mig
                 )
                 for mig in store.MIGRATIONS
             ]
             await store.setup()
+            async with store._cursor() as cur:
+                # drop the migration index
+                await cur.execute("DROP TABLE IF EXISTS store_migrations")
+            await store.setup()  # Will fail if migrations aren't idempotent
 
         if request.param == "pipe":
             async with AsyncPostgresStore.from_conn_string(

libs/checkpoint-postgres/tests/test_store.py

@@ -52,9 +52,7 @@ def store(request) -> PostgresStore:
         with PostgresStore.from_conn_string(conn_string, ttl=ttl_config) as store:
             store.MIGRATIONS = [
                 (
-                    mig.replace(
-                        "ADD COLUMN ttl_minutes INT;", "ADD COLUMN ttl_minutes FLOAT;"
-                    )
+                    mig.replace("ttl_minutes INT;", "ttl_minutes FLOAT;")
                     if isinstance(mig, str)
                     else mig
                 )
@@ -415,6 +413,10 @@ def _create_vector_store(
             ttl={"default_ttl": 2, "refresh_on_read": True} if enable_ttl else None,
         ) as store:
             store.setup()
+            with store._cursor() as cur:
+                # drop the migration index
+                cur.execute("DROP TABLE IF EXISTS store_migrations")
+            store.setup()  # Will fail if migrations aren't idempotent
             yield store
     finally:
         with Connection.connect(admin_conn_string, autocommit=True) as conn:

libs/checkpoint-sqlite/README.md

@@ -12,7 +12,7 @@ read_config = {"configurable": {"thread_id": "1"}}
 
 with SqliteSaver.from_conn_string(":memory:") as checkpointer:
     checkpoint = {
-        "v": 1,
+        "v": 2,
         "ts": "2024-07-31T20:14:19.804150+00:00",
         "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
         "channel_values": {
@@ -54,7 +54,7 @@ from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
 
 async with AsyncSqliteSaver.from_conn_string(":memory:") as checkpointer:
     checkpoint = {
-        "v": 1,
+        "v": 2,
         "ts": "2024-07-31T20:14:19.804150+00:00",
         "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
         "channel_values": {

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

@@ -56,7 +56,10 @@ class SqliteSaver(BaseCheckpointSaver[str]):
         >>> builder.add_node("add_one", lambda x: x + 1)
         >>> builder.set_entry_point("add_one")
         >>> builder.set_finish_point("add_one")
-        >>> conn = sqlite3.connect("checkpoints.sqlite")
+        >>> # Create a new SqliteSaver instance
+        >>> # Note: check_same_thread=False is OK as the implementation uses a lock
+        >>> # to ensure thread safety.
+        >>> conn = sqlite3.connect("checkpoints.sqlite", check_same_thread=False)
         >>> memory = SqliteSaver(conn)
         >>> graph = builder.compile(checkpointer=memory)
         >>> config = {"configurable": {"thread_id": "1"}}

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

@@ -70,15 +70,18 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         >>> from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
         >>> from langgraph.graph import StateGraph
         >>>
-        >>> builder = StateGraph(int)
-        >>> builder.add_node("add_one", lambda x: x + 1)
-        >>> builder.set_entry_point("add_one")
-        >>> builder.set_finish_point("add_one")
-        >>> async with AsyncSqliteSaver.from_conn_string("checkpoints.db") as memory:
-        >>>     graph = builder.compile(checkpointer=memory)
-        >>>     coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}})
-        >>>     print(asyncio.run(coro))
-        Output: 2
+        >>> async def main():
+        >>>     builder = StateGraph(int)
+        >>>     builder.add_node("add_one", lambda x: x + 1)
+        >>>     builder.set_entry_point("add_one")
+        >>>     builder.set_finish_point("add_one")
+        >>>     async with AsyncSqliteSaver.from_conn_string("checkpoints.db") as memory:
+        >>>         graph = builder.compile(checkpointer=memory)
+        >>>         coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}})
+        >>>         print(await asyncio.gather(coro))
+        >>>
+        >>> asyncio.run(main())
+        Output: [2]
         ```
         Raw usage:
 
@@ -90,12 +93,12 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         >>> async def main():
         >>>     async with aiosqlite.connect("checkpoints.db") as conn:
         ...         saver = AsyncSqliteSaver(conn)
-        ...         config = {"configurable": {"thread_id": "1"}}
-        ...         checkpoint = {"ts": "2023-05-03T10:00:00Z", "data": {"key": "value"}}
+        ...         config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
+        ...         checkpoint = {"ts": "2023-05-03T10:00:00Z", "data": {"key": "value"}, "id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}
         ...         saved_config = await saver.aput(config, checkpoint, {}, {})
         ...         print(saved_config)
         >>> asyncio.run(main())
-        {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}
+        {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '0c62ca34-ac19-445d-bbb0-5b4984975b2a'}}
         ```
     """
 

libs/checkpoint/README.md

@@ -51,7 +51,7 @@ read_config = {"configurable": {"thread_id": "1"}}
 
 checkpointer = MemorySaver()
 checkpoint = {
-    "v": 1,
+    "v": 2,
     "ts": "2024-07-31T20:14:19.804150+00:00",
     "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
     "channel_values": {

libs/checkpoint/langgraph/checkpoint/base/__init__.py

@@ -30,6 +30,7 @@ from langgraph.checkpoint.serde.types import (
 
 V = TypeVar("V", int, float, str)
 PendingWrite = Tuple[str, str, Any]
+LATEST_VERSION = 2
 
 
 # Marked as total=False to allow for future expansion.
@@ -101,7 +102,7 @@ class Checkpoint(TypedDict):
 
 def empty_checkpoint() -> Checkpoint:
     return Checkpoint(
-        v=1,
+        v=LATEST_VERSION,
         id=str(uuid6(clock_seq=-2)),
         ts=datetime.now(timezone.utc).isoformat(),
         channel_values={},
@@ -144,7 +145,7 @@ def create_checkpoint(
             except EmptyChannelError:
                 pass
     return Checkpoint(
-        v=1,
+        v=LATEST_VERSION,
         ts=ts,
         id=id or str(uuid6(clock_seq=step)),
         channel_values=values,

libs/checkpoint/langgraph/checkpoint/memory/__init__.py

@@ -7,7 +7,7 @@ from collections import defaultdict
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
 from types import TracebackType
-from typing import Any, Optional
+from typing import Any, Optional, Union
 
 from langchain_core.runnables import RunnableConfig
 
@@ -70,6 +70,12 @@ class InMemorySaver(
         tuple[str, str, str],
         dict[tuple[str, int], tuple[str, str, tuple[str, bytes], str]],
     ]
+    blobs: dict[
+        tuple[
+            str, str, str, Union[str, int, float]
+        ],  # thread id, checkpoint ns, channel, version
+        tuple[str, bytes],
+    ]
 
     def __init__(
         self,
@@ -80,6 +86,7 @@ class InMemorySaver(
         super().__init__(serde=serde)
         self.storage = factory(lambda: defaultdict(dict))
         self.writes = factory(dict)
+        self.blobs = factory()
         self.stack = ExitStack()
         if factory is not defaultdict:
             self.stack.enter_context(self.storage)  # type: ignore[arg-type]
@@ -107,6 +114,18 @@ class InMemorySaver(
     ) -> Optional[bool]:
         return self.stack.__exit__(__exc_type, __exc_value, __traceback)
 
+    def _load_blobs(
+        self, thread_id: str, checkpoint_ns: str, versions: ChannelVersions
+    ) -> dict[str, Any]:
+        channel_values: dict[str, Any] = {}
+        for k, v in versions.items():
+            kk = (thread_id, checkpoint_ns, k, v)
+            if kk in self.blobs:
+                vv = self.blobs[kk]
+                if vv[0] != "empty":
+                    channel_values[k] = self.serde.loads_typed(vv)
+        return channel_values
+
     def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
         """Get a checkpoint tuple from the in-memory storage.
 
@@ -121,8 +140,8 @@ class InMemorySaver(
         Returns:
             Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
         """
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        thread_id: str = config["configurable"]["thread_id"]
+        checkpoint_ns: str = config["configurable"].get("checkpoint_ns", "")
         if checkpoint_id := get_checkpoint_id(config):
             if saved := self.storage[thread_id][checkpoint_ns].get(checkpoint_id):
                 checkpoint, metadata, parent_checkpoint_id = saved
@@ -140,10 +159,14 @@ class InMemorySaver(
                     )
                 else:
                     sends = []
+                checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
                 return CheckpointTuple(
                     config=config,
                     checkpoint={
-                        **self.serde.loads_typed(checkpoint),
+                        **checkpoint_,
+                        "channel_values": self._load_blobs(
+                            thread_id, checkpoint_ns, checkpoint_["channel_versions"]
+                        ),
                         "pending_sends": [self.serde.loads_typed(s[2]) for s in sends],
                     },
                     metadata=self.serde.loads_typed(metadata),
@@ -180,6 +203,9 @@ class InMemorySaver(
                     )
                 else:
                     sends = []
+
+                checkpoint_ = self.serde.loads_typed(checkpoint)
+
                 return CheckpointTuple(
                     config={
                         "configurable": {
@@ -189,7 +215,10 @@ class InMemorySaver(
                         }
                     },
                     checkpoint={
-                        **self.serde.loads_typed(checkpoint),
+                        **checkpoint_,
+                        "channel_values": self._load_blobs(
+                            thread_id, checkpoint_ns, checkpoint_["channel_versions"]
+                        ),
                         "pending_sends": [self.serde.loads_typed(s[2]) for s in sends],
                     },
                     metadata=self.serde.loads_typed(metadata),
@@ -297,6 +326,8 @@ class InMemorySaver(
                     else:
                         sends = []
 
+                    checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
+
                     yield CheckpointTuple(
                         config={
                             "configurable": {
@@ -306,7 +337,12 @@ class InMemorySaver(
                             }
                         },
                         checkpoint={
-                            **self.serde.loads_typed(checkpoint),
+                            **checkpoint_,
+                            "channel_values": self._load_blobs(
+                                thread_id,
+                                checkpoint_ns,
+                                checkpoint_["channel_versions"],
+                            ),
                             "pending_sends": [
                                 self.serde.loads_typed(s[2]) for s in sends
                             ],
@@ -353,6 +389,11 @@ class InMemorySaver(
         c.pop("pending_sends")  # type: ignore[misc]
         thread_id = config["configurable"]["thread_id"]
         checkpoint_ns = config["configurable"]["checkpoint_ns"]
+        values: dict[str, Any] = c.pop("channel_values")  # type: ignore[misc]
+        for k, v in new_versions.items():
+            self.blobs[(thread_id, checkpoint_ns, k, v)] = (
+                self.serde.dumps_typed(values[k]) if k in values else ("empty", b"")
+            )
         self.storage[thread_id][checkpoint_ns].update(
             {
                 checkpoint["id"]: (

libs/checkpoint/langgraph/checkpoint/serde/base.py

@@ -45,3 +45,18 @@ def maybe_add_typed_methods(serde: SerializerProtocol) -> SerializerProtocol:
         return SerializerCompat(serde)
 
     return serde
+
+
+class CipherProtocol(Protocol):
+    """Protocol for encryption and decryption of data.
+    - `encrypt`: Encrypt plaintext.
+    - `decrypt`: Decrypt ciphertext.
+    """
+
+    def encrypt(self, plaintext: bytes) -> tuple[str, bytes]:
+        """Encrypt plaintext. Returns a tuple (cipher name, ciphertext)."""
+        ...
+
+    def decrypt(self, ciphername: str, ciphertext: bytes) -> bytes:
+        """Decrypt ciphertext. Returns the plaintext."""
+        ...

libs/checkpoint/langgraph/checkpoint/serde/encrypted.py

@@ -0,0 +1,86 @@
+import os
+from typing import Any
+
+from langgraph.checkpoint.serde.base import CipherProtocol, SerializerProtocol
+from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+
+
+class EncryptedSerializer(SerializerProtocol):
+    """Serializer that encrypts and decrypts data using an encryption protocol."""
+
+    def __init__(
+        self, cipher: CipherProtocol, serde: SerializerProtocol = JsonPlusSerializer()
+    ) -> None:
+        self.cipher = cipher
+        self.serde = serde
+
+    def dumps(self, obj: Any) -> bytes:
+        return self.serde.dumps(obj)
+
+    def loads(self, data: bytes) -> Any:
+        return self.serde.loads(data)
+
+    def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
+        """Serialize an object to a tuple (type, bytes) and encrypt the bytes."""
+        # serialize data
+        typ, data = self.serde.dumps_typed(obj)
+        # encrypt data
+        ciphername, ciphertext = self.cipher.encrypt(data)
+        # add cipher name to type
+        return f"{typ}+{ciphername}", ciphertext
+
+    def loads_typed(self, data: tuple[str, bytes]) -> Any:
+        enc_cipher, ciphertext = data
+        # unencrypted data
+        if "+" not in enc_cipher:
+            return self.serde.loads_typed(data)
+        # extract cipher name
+        typ, ciphername = enc_cipher.split("+", 1)
+        # decrypt data
+        decrypted_data = self.cipher.decrypt(ciphername, ciphertext)
+        # deserialize data
+        return self.serde.loads_typed((typ, decrypted_data))
+
+    @classmethod
+    def from_pycryptodome_aes(
+        cls, serde: SerializerProtocol = JsonPlusSerializer(), **kwargs: Any
+    ) -> "EncryptedSerializer":
+        """Create an EncryptedSerializer using AES encryption."""
+        try:
+            from Crypto.Cipher import AES  # type: ignore
+        except ImportError:
+            raise ImportError(
+                "Pycryptodome is not installed. Please install it with `pip install pycryptodome`."
+            ) from None
+
+        # check if AES key is provided
+        if "key" in kwargs:
+            key: bytes = kwargs.pop("key")
+        else:
+            key_str = os.getenv("LANGGRAPH_AES_KEY")
+            if key_str is None:
+                raise ValueError("LANGGRAPH_AES_KEY environment variable is not set.")
+            key = key_str.encode()
+            if len(key) not in (16, 24, 32):
+                raise ValueError("LANGGRAPH_AES_KEY must be 16, 24, or 32 bytes long.")
+
+        # set default mode to EAX if not provided
+        if kwargs.get("mode") is None:
+            kwargs["mode"] = AES.MODE_EAX
+
+        class PycryptodomeAesCipher(CipherProtocol):
+            def encrypt(self, plaintext: bytes) -> tuple[str, bytes]:
+                cipher = AES.new(key, **kwargs)
+                ciphertext, tag = cipher.encrypt_and_digest(plaintext)
+                return "aes", cipher.nonce + tag + ciphertext
+
+            def decrypt(self, ciphername: str, ciphertext: bytes) -> bytes:
+                assert ciphername == "aes", f"Unsupported cipher: {ciphername}"
+                nonce = ciphertext[:16]
+                tag = ciphertext[16:32]
+                actual_ciphertext = ciphertext[32:]
+
+                cipher = AES.new(key, **kwargs, nonce=nonce)
+                return cipher.decrypt_and_verify(actual_ciphertext, tag)
+
+        return cls(PycryptodomeAesCipher(), serde)

libs/checkpoint/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint"
-version = "2.0.20"
+version = "2.0.21"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 license = "MIT"

libs/checkpoint/tests/test_memory.py

@@ -68,7 +68,9 @@ class TestMemorySaver:
             },
             "metadata": {"run_id": "my_run_id"},
         }
-        self.memory_saver.put(config, self.chkpnt_2, self.metadata_2, {})
+        self.memory_saver.put(
+            config, self.chkpnt_2, self.metadata_2, self.chkpnt_2["channel_versions"]
+        )
         checkpoint = self.memory_saver.get_tuple(config)
         assert checkpoint is not None
         assert checkpoint.metadata == {
@@ -80,9 +82,24 @@ class TestMemorySaver:
     async def test_search(self) -> None:
         # set up test
         # save checkpoints
-        self.memory_saver.put(self.config_1, self.chkpnt_1, self.metadata_1, {})
-        self.memory_saver.put(self.config_2, self.chkpnt_2, self.metadata_2, {})
-        self.memory_saver.put(self.config_3, self.chkpnt_3, self.metadata_3, {})
+        self.memory_saver.put(
+            self.config_1,
+            self.chkpnt_1,
+            self.metadata_1,
+            self.chkpnt_1["channel_versions"],
+        )
+        self.memory_saver.put(
+            self.config_2,
+            self.chkpnt_2,
+            self.metadata_2,
+            self.chkpnt_2["channel_versions"],
+        )
+        self.memory_saver.put(
+            self.config_3,
+            self.chkpnt_3,
+            self.metadata_3,
+            self.chkpnt_3["channel_versions"],
+        )
 
         # call method / assertions
         query_1 = {"source": "input"}  # search by 1 key
@@ -129,9 +146,24 @@ class TestMemorySaver:
     async def test_asearch(self) -> None:
         # set up test
         # save checkpoints
-        self.memory_saver.put(self.config_1, self.chkpnt_1, self.metadata_1, {})
-        self.memory_saver.put(self.config_2, self.chkpnt_2, self.metadata_2, {})
-        self.memory_saver.put(self.config_3, self.chkpnt_3, self.metadata_3, {})
+        self.memory_saver.put(
+            self.config_1,
+            self.chkpnt_1,
+            self.metadata_1,
+            self.chkpnt_1["channel_versions"],
+        )
+        self.memory_saver.put(
+            self.config_2,
+            self.chkpnt_2,
+            self.metadata_2,
+            self.chkpnt_2["channel_versions"],
+        )
+        self.memory_saver.put(
+            self.config_3,
+            self.chkpnt_3,
+            self.metadata_3,
+            self.chkpnt_3["channel_versions"],
+        )
 
         # call method / assertions
         query_1 = {"source": "input"}  # search by 1 key

libs/cli/README.md

@@ -79,7 +79,7 @@ The CLI uses a `langgraph.json` configuration file with these key settings:
 }
 ```
 
-See the [full documentation](https://langchain-ai.github.io/langgraph/docs/cloud/reference/cli.html) for detailed configuration options.
+See the [full documentation](https://langchain-ai.github.io/langgraph/cloud/reference/cli/) for detailed configuration options.
 
 ## Development
 

libs/cli/langgraph_cli/cli.py

@@ -574,6 +574,12 @@ def dockerfile(save_path: str, config: pathlib.Path, add_docker_compose: bool) -
     help="Wait for a debugger client to connect to the debug port before starting the server",
     default=False,
 )
+@click.option(
+    "--studio-url",
+    type=str,
+    default=None,
+    help="URL of the LangGraph Studio instance to connect to. Defaults to https://smith.langchain.com",
+)
 @cli.command(
     "dev",
     help="🏃‍♀️‍➡️ Run LangGraph API server in development mode with hot reloading and debugging support",
@@ -588,6 +594,7 @@ def dev(
     no_browser: bool,
     debug_port: Optional[int],
     wait_for_client: bool,
+    studio_url: Optional[str],
 ):
     """CLI entrypoint for running the LangGraph API server."""
     try:
@@ -651,6 +658,7 @@ def dev(
         wait_for_client=wait_for_client,
         auth=config_json.get("auth"),
         http=config_json.get("http"),
+        studio_url=studio_url,
     )
 
 

libs/cli/poetry.lock

@@ -535,42 +535,42 @@ langgraph-sdk = ">=0.1.42,<0.2.0"
 
 [[package]]
 name = "langgraph-api"
-version = "0.0.27"
+version = "0.0.32"
 description = ""
 optional = true
 python-versions = "<4.0,>=3.11.0"
 files = [
-    {file = "langgraph_api-0.0.27-py3-none-any.whl", hash = "sha256:9b21742238b15b8db9c2d3fd760a670332c8897d0bcbbd9d82e43b6ac15a7937"},
-    {file = "langgraph_api-0.0.27.tar.gz", hash = "sha256:c21eb2b7fe3b93998379f7b13ad7d23b3ef06ab821b008c6b12b954acfb587ec"},
+    {file = "langgraph_api-0.0.32-py3-none-any.whl", hash = "sha256:7990cedc65f784813aba867c5bde3fdfae3fa4588baef1aa346cbeac7c3aebf1"},
+    {file = "langgraph_api-0.0.32.tar.gz", hash = "sha256:6f5b698ad8d136b73c2c53bcfa30670e9244a318b08b5e9cf00a707ea57c058c"},
 ]
 
 [package.dependencies]
 cryptography = ">=43.0.3,<44.0.0"
 httpx = ">=0.25.0"
-jsonschema-rs = ">=0.20.0,<0.21.0"
+jsonschema-rs = ">=0.20.0,<0.30"
 langchain-core = ">=0.2.38,<0.4.0"
 langgraph = ">=0.2.56,<0.4.0"
-langgraph-checkpoint = ">=2.0.15,<3.0"
-langgraph-sdk = ">=0.1.53,<0.2.0"
+langgraph-checkpoint = ">=2.0.21,<3.0"
+langgraph-sdk = ">=0.1.58,<0.2.0"
 langsmith = ">=0.1.63,<0.4.0"
 orjson = ">=3.9.7"
 pyjwt = ">=2.9.0,<3.0.0"
 sse-starlette = ">=2.1.0,<2.2.0"
 starlette = ">=0.38.6"
-structlog = ">=23.1.0,<24.0.0"
+structlog = ">=24.1.0,<26"
 tenacity = ">=8.0.0"
 uvicorn = ">=0.26.0"
 watchfiles = ">=0.13"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.16"
+version = "2.0.21"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = true
 python-versions = "<4.0.0,>=3.9.0"
 files = [
-    {file = "langgraph_checkpoint-2.0.16-py3-none-any.whl", hash = "sha256:dfab51076a6eddb5f9e146cfe1b977e3dd6419168b2afa23ff3f4e47973bf06f"},
-    {file = "langgraph_checkpoint-2.0.16.tar.gz", hash = "sha256:49ba8cfa12b2aae845ccc3b1fbd1d7a8d3a6c4a2e387ab3a92fca40dd3d4baa5"},
+    {file = "langgraph_checkpoint-2.0.21-py3-none-any.whl", hash = "sha256:ca89c2090cd9729f83f9782226935dc5ff9fe7756c24936f484ccb0ce367f87b"},
+    {file = "langgraph_checkpoint-2.0.21.tar.gz", hash = "sha256:52beeb6dc1bd8c487b8315466cab271093b65eb97f54a0942dfe105cd20b237f"},
 ]
 
 [package.dependencies]
@@ -594,13 +594,13 @@ langgraph-checkpoint = ">=2.0.10,<3.0.0"
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.53"
+version = "0.1.58"
 description = "SDK for interacting with LangGraph API"
 optional = true
 python-versions = "<4.0.0,>=3.9.0"
 files = [
-    {file = "langgraph_sdk-0.1.53-py3-none-any.whl", hash = "sha256:4fab62caad73661ffe4c3ababedcd0d7bfaaba986bee4416b9c28948458a3af5"},
-    {file = "langgraph_sdk-0.1.53.tar.gz", hash = "sha256:12906ed965905fa27e0c28d9fa07dc6fd89e6895ff321ff049fdf3965d057cc4"},
+    {file = "langgraph_sdk-0.1.58-py3-none-any.whl", hash = "sha256:65f88cf5582da0c316714dc475126fa03c5f74d72bc0b9221dd42649de8e23d4"},
+    {file = "langgraph_sdk-0.1.58.tar.gz", hash = "sha256:ef8b0e4c08af8c7efd3919497879c87a3627806b51e4ba5e8b06e0717e3d44cd"},
 ]
 
 [package.dependencies]
@@ -1357,18 +1357,18 @@ full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart
 
 [[package]]
 name = "structlog"
-version = "23.3.0"
+version = "25.2.0"
 description = "Structured Logging for Python"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "structlog-23.3.0-py3-none-any.whl", hash = "sha256:d6922a88ceabef5b13b9eda9c4043624924f60edbb00397f4d193bd754cde60a"},
-    {file = "structlog-23.3.0.tar.gz", hash = "sha256:24b42b914ac6bc4a4e6f716e82ac70d7fb1e8c3b1035a765591953bfc37101a5"},
+    {file = "structlog-25.2.0-py3-none-any.whl", hash = "sha256:0fecea2e345d5d491b72f3db2e5fcd6393abfc8cd06a4851f21fcd4d1a99f437"},
+    {file = "structlog-25.2.0.tar.gz", hash = "sha256:d9f9776944207d1035b8b26072b9b140c63702fd7aa57c2f85d28ab701bd8e92"},
 ]
 
 [package.extras]
-dev = ["structlog[tests,typing]"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-mermaid", "sphinxext-opengraph", "twisted"]
+dev = ["freezegun (>=0.2.8)", "mypy (>=1.4)", "pretend", "pytest (>=6.0)", "pytest-asyncio (>=0.17)", "rich", "simplejson", "twisted"]
+docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-mermaid", "sphinxext-opengraph", "twisted"]
 tests = ["freezegun (>=0.2.8)", "pretend", "pytest (>=6.0)", "pytest-asyncio (>=0.17)", "simplejson"]
 typing = ["mypy (>=1.4)", "rich", "twisted"]
 
@@ -1717,4 +1717,4 @@ inmem = ["langgraph-api", "python-dotenv"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9.0,<4.0"
-content-hash = "d0e2bdcb600ad031867413025fcc58bb162609209359d63ca99a77060cf8cbb4"
+content-hash = "f5aa4d66f9c0b98b8321a70a82387dc6e5f3a3a7ecedd87ac00d6415199038f9"

libs/cli/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-cli"
-version = "0.1.77"
+version = "0.1.79"
 description = "CLI for interacting with LangGraph API"
 authors = []
 license = "MIT"
@@ -14,7 +14,7 @@ langgraph = "langgraph_cli.cli:cli"
 [tool.poetry.dependencies]
 python = "^3.9.0,<4.0"
 click = "^8.1.7"
-langgraph-api = { version = ">=0.0.27,<0.1.0", optional = true, python = ">=3.11,<4.0" }
+langgraph-api = { version = ">=0.0.32,<0.1.0", optional = true, python = ">=3.11,<4.0" }
 python-dotenv = { version = ">=0.8.0", optional = true }
 
 [tool.poetry.group.dev.dependencies]

libs/langgraph/Makefile

@@ -58,9 +58,11 @@ WORKERS ?= auto
 XDIST_ARGS := $(if $(WORKERS),-n $(WORKERS) --dist worksteal,)
 MAXFAIL ?=
 MAXFAIL_ARGS := $(if $(MAXFAIL),--maxfail $(MAXFAIL),)
+# Add an '-x' if xdist is enabled
+XDIST_ARGS := $(if $(WORKERS),-x $(XDIST_ARGS),)
 
 test_watch:
-	make start-postgres && poetry run ptw . -- --ff -vv -x $(XDIST_ARGS) $(MAXFAIL_ARGS) --snapshot-update --tb short $(TEST); \
+	make start-postgres && poetry run ptw . -- --ff -vv $(XDIST_ARGS) $(MAXFAIL_ARGS) --snapshot-update --tb short $(TEST); \
 	EXIT_CODE=$$?; \
 	make stop-postgres; \
 	exit $$EXIT_CODE

libs/langgraph/README.md

@@ -1,7 +1,7 @@
 <picture class="github-only">
-  <source media="(prefers-color-scheme: light)" srcset="docs/docs/static/wordmark_dark.svg">
-  <source media="(prefers-color-scheme: dark)" srcset="docs/docs/static/wordmark_light.svg">
-  <img alt="LangGraph Logo" src="docs/docs/static/wordmark_dark.svg" width="80%">
+  <source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
+  <source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
+  <img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="80%">
 </picture>
 
 <div>

libs/langgraph/bench/__main__.py

@@ -6,9 +6,12 @@ from pyperf._runner import Runner
 from uvloop import new_event_loop
 
 from bench.fanout_to_subgraph import fanout_to_subgraph, fanout_to_subgraph_sync
+from bench.pydantic_state import pydantic_state
 from bench.react_agent import react_agent
+from bench.sequential import create_sequential
 from bench.wide_state import wide_state
 from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import StateGraph
 from langgraph.pregel import Pregel
 
 
@@ -27,6 +30,26 @@ async def arun(graph: Pregel, input: dict):
     )
 
 
+async def arun_first_event_latency(graph: Pregel, input: dict) -> None:
+    """Latency for the first event.
+
+    Run the graph until the first event is processed and then stop.
+    """
+    stream = graph.astream(
+        input,
+        {
+            "configurable": {"thread_id": str(uuid4())},
+            "recursion_limit": 1000000000,
+        },
+    )
+
+    try:
+        async for _ in stream:
+            break
+    finally:
+        await stream.aclose()
+
+
 def run(graph: Pregel, input: dict):
     len(
         [
@@ -42,6 +65,31 @@ def run(graph: Pregel, input: dict):
     )
 
 
+def run_first_event_latency(graph: Pregel, input: dict) -> None:
+    """Latency for the first event.
+
+    Run the graph until the first event is processed and then stop.
+    """
+    stream = graph.stream(
+        input,
+        {
+            "configurable": {"thread_id": str(uuid4())},
+            "recursion_limit": 1000000000,
+        },
+    )
+
+    try:
+        for _ in stream:
+            break
+    finally:
+        stream.close()
+
+
+def compile_graph(graph: StateGraph) -> None:
+    """Compile the graph."""
+    graph.compile()
+
+
 benchmarks = (
     (
         "fanout_to_subgraph_10x",
@@ -203,12 +251,183 @@ benchmarks = (
             ]
         },
     ),
+    (
+        "sequential_20",
+        create_sequential(20).compile(),
+        create_sequential(20).compile(),
+        {"messages": []},  # Empty list of messages
+    ),
+    (
+        "sequential_50",
+        create_sequential(50).compile(),
+        create_sequential(50).compile(),
+        {"messages": []},  # Empty list of messages
+    ),
+    (
+        "sequential_100",
+        create_sequential(100).compile(),
+        create_sequential(100).compile(),
+        {"messages": []},  # Empty list of messages
+    ),
+    (
+        "sequential_200",
+        create_sequential(200).compile(),
+        create_sequential(200).compile(),
+        {"messages": []},  # Empty list of messages
+    ),
+    (
+        "pydantic_state_25x300",
+        pydantic_state(300).compile(checkpointer=None),
+        pydantic_state(300).compile(checkpointer=None),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(5)
+                    }
+                    for i in range(5)
+                }
+            ]
+        },
+    ),
+    (
+        "pydantic_state_25x300_checkpoint",
+        pydantic_state(300).compile(checkpointer=MemorySaver()),
+        pydantic_state(300).compile(checkpointer=MemorySaver()),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(5)
+                    }
+                    for i in range(5)
+                }
+            ]
+        },
+    ),
+    (
+        "pydantic_state_15x600",
+        pydantic_state(600).compile(checkpointer=None),
+        pydantic_state(600).compile(checkpointer=None),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(5)
+                    }
+                    for i in range(3)
+                }
+            ]
+        },
+    ),
+    (
+        "pydantic_state_15x600_checkpoint",
+        pydantic_state(600).compile(checkpointer=MemorySaver()),
+        pydantic_state(600).compile(checkpointer=MemorySaver()),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(5)
+                    }
+                    for i in range(3)
+                }
+            ]
+        },
+    ),
+    (
+        "pydantic_state_9x1200",
+        pydantic_state(1200).compile(checkpointer=None),
+        pydantic_state(1200).compile(checkpointer=None),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(3)
+                    }
+                    for i in range(3)
+                }
+            ]
+        },
+    ),
+    (
+        "pydantic_state_9x1200_checkpoint",
+        pydantic_state(1200).compile(checkpointer=MemorySaver()),
+        pydantic_state(1200).compile(checkpointer=MemorySaver()),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(3)
+                    }
+                    for i in range(3)
+                }
+            ]
+        },
+    ),
 )
 
 
 r = Runner()
 
+# Full graph run time
 for name, agraph, graph, input in benchmarks:
     r.bench_async_func(name, arun, agraph, input, loop_factory=new_event_loop)
     if graph is not None:
         r.bench_func(name + "_sync", run, graph, input)
+
+
+# First event latency
+for name, agraph, graph, input in benchmarks:
+    r.bench_async_func(
+        name + "_first_event_latency",
+        arun_first_event_latency,
+        agraph,
+        input,
+        loop_factory=new_event_loop,
+    )
+    if graph is not None:
+        r.bench_func(
+            name + "_first_event_latency_sync", run_first_event_latency, graph, input
+        )
+
+# Graph compilation times
+compilation_benchmarks = (
+    (
+        "sequential_1000",
+        create_sequential(1_000),
+    ),
+    (
+        "sequential_10000",
+        create_sequential(10_000),
+    ),
+    (
+        "pydantic_state_25x300",
+        pydantic_state(300),
+    ),
+    (
+        "pydantic_state_15x600",
+        pydantic_state(600),
+    ),
+    (
+        "pydantic_state_9x1200",
+        pydantic_state(1200),
+    ),
+    (
+        "wide_state_15x600",
+        wide_state(600),
+    ),
+    (
+        "wide_state_9x1200",
+        wide_state(1200),
+    ),
+)
+
+for name, graph in compilation_benchmarks:
+    r.bench_func(name + "_compilation", compile_graph, graph)

libs/langgraph/bench/pydantic_state.py

@@ -0,0 +1,327 @@
+import operator
+from functools import partial
+from random import choice
+from typing import Annotated, Optional, Sequence
+
+from pydantic import BaseModel, Field, field_validator
+
+from langgraph.constants import END, START
+from langgraph.graph.state import StateGraph
+
+
+def pydantic_state(n: int) -> StateGraph:
+    class State(BaseModel):
+        messages: Annotated[list, operator.add] = Field(default_factory=list)
+
+        @field_validator("messages", mode="after")
+        @classmethod
+        def validate_messages(cls, v):
+            if not isinstance(v, list):
+                raise TypeError("messages must be a list")
+            for msg in v:
+                if not isinstance(msg, dict):
+                    raise TypeError("messages must be a list of dicts")
+                if not all(isinstance(k, str) for k in msg.keys()):
+                    raise TypeError("messages must be a list of dicts with str keys")
+            return v
+
+        trigger_events: Annotated[list, operator.add] = Field(default_factory=list)
+        """The external events that are converted by the graph."""
+
+        @field_validator("trigger_events", mode="after")
+        @classmethod
+        def validate_trigger_events(cls, v):
+            if not isinstance(v, list):
+                raise TypeError("trigger_events must be a list")
+            for event in v:
+                if not isinstance(event, dict):
+                    raise TypeError("trigger_events must be a list of dicts")
+                if not all(isinstance(k, str) for k in event.keys()):
+                    raise TypeError(
+                        "trigger_events must be a list of dicts with str keys"
+                    )
+            return v
+
+        primary_issue_medium: Annotated[str, lambda x, y: y or x] = Field(
+            default="email"
+        )
+        """The primary issue medium for the current conversation."""
+
+        @field_validator("primary_issue_medium", mode="after")
+        @classmethod
+        def validate_primary_issue_medium(cls, v):
+            if not isinstance(v, str):
+                raise TypeError("primary_issue_medium must be a string")
+            return v
+
+        autoresponse: Annotated[Optional[dict], lambda _, y: y] = Field(
+            default=None
+        )  # Always overwrite
+
+        @field_validator("autoresponse", mode="after")
+        @classmethod
+        def validate_autoresponse(cls, v):
+            if v is not None and not isinstance(v, dict):
+                raise TypeError("autoresponse must be a dict or None")
+            return v
+
+        issue: Annotated[dict | None, lambda x, y: y if y else x] = Field(default=None)
+
+        @field_validator("issue", mode="after")
+        @classmethod
+        def validate_issue(cls, v):
+            if v is not None and not isinstance(v, dict):
+                raise TypeError("issue must be a dict or None")
+            return v
+
+        relevant_rules: Optional[list[dict]] = Field(default=None)
+        """SOPs fetched from the rulebook that are relevant to the current conversation."""
+
+        @field_validator("relevant_rules", mode="after")
+        @classmethod
+        def validate_relevant_rules(cls, v):
+            if v is None:
+                return v
+            if not isinstance(v, list):
+                raise TypeError("relevant_rules must be a list or None")
+            for rule in v:
+                if not isinstance(rule, dict):
+                    raise TypeError("relevant_rules must be a list of dicts")
+                if not all(isinstance(k, str) for k in rule.keys()):
+                    raise TypeError(
+                        "relevant_rules must be a list of dicts with str keys"
+                    )
+            return v
+
+        memory_docs: Optional[list[dict]] = Field(default=None)
+        """Memory docs fetched from the memory service that are relevant to the current conversation."""
+
+        @field_validator("memory_docs", mode="after")
+        @classmethod
+        def validate_memory_docs(cls, v):
+            if v is None:
+                return v
+            if not isinstance(v, list):
+                raise TypeError("memory_docs must be a list or None")
+            for doc in v:
+                if not isinstance(doc, dict):
+                    raise TypeError("memory_docs must be a list of dicts")
+                if not all(isinstance(k, str) for k in doc.keys()):
+                    raise TypeError("memory_docs must be a list of dicts with str keys")
+            return v
+
+        categorizations: Annotated[list[dict], operator.add] = Field(
+            default_factory=list
+        )
+        """The issue categorizations auto-generated by the AI."""
+
+        @field_validator("categorizations", mode="after")
+        @classmethod
+        def validate_categorizations(cls, v):
+            if not isinstance(v, list):
+                raise TypeError("categorizations must be a list")
+            for categorization in v:
+                if not isinstance(categorization, dict):
+                    raise TypeError("categorizations must be a list of dicts")
+                if not all(isinstance(k, str) for k in categorization.keys()):
+                    raise TypeError(
+                        "categorizations must be a list of dicts with str keys"
+                    )
+            return v
+
+        responses: Annotated[list[dict], operator.add] = Field(default_factory=list)
+        """The draft responses recommended by the AI."""
+
+        @field_validator("responses", mode="after")
+        @classmethod
+        def validate_responses(cls, v):
+            if not isinstance(v, list):
+                raise TypeError("responses must be a list")
+            for response in v:
+                if not isinstance(response, dict):
+                    raise TypeError("responses must be a list of dicts")
+                if not all(isinstance(k, str) for k in response.keys()):
+                    raise TypeError("responses must be a list of dicts with str keys")
+            return v
+
+        user_info: Annotated[Optional[dict], lambda x, y: y if y is not None else x] = (
+            Field(default=None)
+        )
+        """The current user state (by email)."""
+
+        @field_validator("user_info", mode="after")
+        @classmethod
+        def validate_user_info(cls, v):
+            if v is not None and not isinstance(v, dict):
+                raise TypeError("user_info must be a dict or None")
+            return v
+
+        crm_info: Annotated[Optional[dict], lambda x, y: y if y is not None else x] = (
+            Field(default=None)
+        )
+        """The CRM information for organization the current user is from."""
+
+        @field_validator("crm_info", mode="after")
+        @classmethod
+        def validate_crm_info(cls, v):
+            if v is not None and not isinstance(v, dict):
+                raise TypeError("crm_info must be a dict or None")
+            return v
+
+        email_thread_id: Annotated[
+            Optional[str], lambda x, y: y if y is not None else x
+        ] = Field(default=None)
+        """The current email thread ID."""
+
+        @field_validator("email_thread_id", mode="after")
+        @classmethod
+        def validate_email_thread_id(cls, v):
+            if v is not None and not isinstance(v, str):
+                raise TypeError("email_thread_id must be a string or None")
+            return v
+
+        slack_participants: Annotated[dict, operator.or_] = Field(default_factory=dict)
+        """The growing list of current slack participants."""
+
+        @field_validator("slack_participants", mode="after")
+        @classmethod
+        def validate_slack_participants(cls, v):
+            if not isinstance(v, dict):
+                raise TypeError("slack_participants must be a dict")
+            for participant in v:
+                if not isinstance(participant, str):
+                    raise TypeError("slack_participants must be a dict with str keys")
+            return v
+
+        bot_id: Optional[str] = Field(default=None)
+        """The ID of the bot user in the slack channel."""
+
+        @field_validator("bot_id", mode="after")
+        @classmethod
+        def validate_bot_id(cls, v):
+            if v is not None and not isinstance(v, str):
+                raise TypeError("bot_id must be a string or None")
+            return v
+
+        notified_assignees: Annotated[dict, operator.or_] = Field(default_factory=dict)
+
+        @field_validator("notified_assignees", mode="after")
+        def validate_notified_assignees(cls, v):
+            if not isinstance(v, dict):
+                raise TypeError("notified_assignees must be a dict")
+            for assignee in v:
+                if not isinstance(assignee, str):
+                    raise TypeError("notified_assignees must be a dict with str keys")
+            return v
+
+    list_fields = {
+        "messages",
+        "trigger_events",
+        "categorizations",
+        "responses",
+        "memory_docs",
+        "relevant_rules",
+    }
+    dict_fields = {
+        "user_info",
+        "crm_info",
+        "slack_participants",
+        "notified_assignees",
+        "autoresponse",
+        "issue",
+    }
+
+    def read_write(read: str, write: Sequence[str], input: State) -> dict:
+        val = getattr(input, read)
+        val = {val: val} if isinstance(val, str) else val
+        val_single = val[-1] if isinstance(val, list) else val
+        val_list = val if isinstance(val, list) else [val]
+        return {
+            k: val_list
+            if k in list_fields
+            else val_single
+            if k in dict_fields
+            else "".join(choice("abcdefghijklmnopqrstuvwxyz") for _ in range(n))
+            for k in write
+        }
+
+    builder = StateGraph(State)
+    builder.add_edge(START, "one")
+    builder.add_node(
+        "one",
+        partial(read_write, "messages", ["trigger_events", "primary_issue_medium"]),
+    )
+    builder.add_edge("one", "two")
+    builder.add_node(
+        "two",
+        partial(read_write, "trigger_events", ["autoresponse", "issue"]),
+    )
+    builder.add_edge("two", "three")
+    builder.add_edge("two", "four")
+    builder.add_node(
+        "three",
+        partial(read_write, "autoresponse", ["relevant_rules"]),
+    )
+    builder.add_node(
+        "four",
+        partial(
+            read_write,
+            "trigger_events",
+            ["categorizations", "responses", "memory_docs"],
+        ),
+    )
+    builder.add_node(
+        "five",
+        partial(
+            read_write,
+            "categorizations",
+            [
+                "user_info",
+                "crm_info",
+                "email_thread_id",
+                "slack_participants",
+                "bot_id",
+                "notified_assignees",
+            ],
+        ),
+    )
+    builder.add_edge(["three", "four"], "five")
+    builder.add_edge("five", "six")
+    builder.add_node(
+        "six",
+        partial(read_write, "responses", ["messages"]),
+    )
+    builder.add_conditional_edges(
+        "six", lambda state: END if len(state.messages) > n else "one"
+    )
+
+    return builder
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    import uvloop
+
+    from langgraph.checkpoint.memory import MemorySaver
+
+    graph = pydantic_state(1000).compile(checkpointer=MemorySaver())
+    input = {
+        "messages": [
+            {
+                str(i) * 10: {
+                    str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                    for j in range(5)
+                }
+                for i in range(5)
+            }
+        ]
+    }
+    config = {"configurable": {"thread_id": "1"}, "recursion_limit": 20000000000}
+
+    async def run():
+        async for c in graph.astream(input, config=config):
+            print(c.keys())
+
+    uvloop.install()
+    asyncio.run(run())

libs/langgraph/bench/sequential.py

@@ -0,0 +1,48 @@
+"""Create a sequential no-op graph consisting of a few hundred nodes."""
+
+from langgraph.graph import MessagesState, StateGraph
+from langgraph.utils.runnable import RunnableCallable
+
+
+def create_sequential(number_nodes: int) -> StateGraph:
+    """Create a sequential no-op graph consisting of a few hundred nodes."""
+    builder = StateGraph(MessagesState)
+
+    def noop(state: MessagesState) -> None:
+        """No-op function."""
+        pass
+
+    async def anoop(state: MessagesState) -> None:
+        """No-op function."""
+        pass
+
+    prev_node = "__start__"
+
+    for i in range(number_nodes):
+        name = f"node_{i}"
+        builder.add_node(name, RunnableCallable(noop, anoop))
+        builder.add_edge(prev_node, name)
+        prev_node = name
+
+    builder.add_edge(prev_node, "__end__")
+    return builder
+
+
+if __name__ == "__main__":
+    import asyncio
+    import time
+
+    import uvloop
+
+    graph = create_sequential(2000).compile()
+    input = {"messages": []}  # Empty list of messages
+    config = {"recursion_limit": 20000000000}
+
+    async def run():
+        len([c async for c in graph.astream(input, config=config)])
+
+    uvloop.install()
+    start = time.time()
+    asyncio.run(run())
+    end = time.time()
+    print(f"Time taken: {end - start:.4f} seconds")

libs/langgraph/langgraph/__main__.py

@@ -0,0 +1,30 @@
+try:
+    import sys
+
+    from langgraph_cli.cli import cli
+
+except ImportError:
+    # Provide more detailed error message with installation instructions
+    error_message = (
+        "\nError: langgraph_cli package not found.\n\n"
+        "This could be due to one of the following reasons:\n"
+        "1. You haven't installed the CLI package\n"
+        "2. Your virtual environment doesn't have the package installed\n"
+        "3. There's a path issue with your Python environment\n\n"
+        "To fix this, try one of the following solutions:\n"
+        "- Install the CLI package: pip install langgraph-cli\n"
+        "- If you're using a virtual environment, activate it first\n"
+        "- Use the standalone CLI directly by running: langgraph\n\n"
+        "For more help, visit: https://github.com/langchain-ai/langgraph/tree/main/libs/cli"
+    )
+    raise ImportError(error_message)
+
+try:
+    cli()
+except Exception as e:
+    # Catch any exceptions that might occur when running the CLI
+    print(f"\nError occurred while running langgraph CLI: {str(e)}")
+    print(
+        "If this problem persists, please report it at: https://github.com/langchain-ai/langgraph/issues"
+    )
+    sys.exit(1)

libs/langgraph/langgraph/channels/any_value.py

@@ -1,8 +1,9 @@
-from typing import Generic, Optional, Sequence, Type
+from typing import Any, Generic, Optional, Sequence, Type
 
 from typing_extensions import Self
 
 from langgraph.channels.base import BaseChannel, Value
+from langgraph.constants import MISSING
 from langgraph.errors import EmptyChannelError
 
 
@@ -12,6 +13,10 @@ class AnyValue(Generic[Value], BaseChannel[Value, Value, Value]):
 
     __slots__ = ("typ", "value")
 
+    def __init__(self, typ: Any, key: str = "") -> None:
+        super().__init__(typ, key)
+        self.value = MISSING
+
     def __eq__(self, value: object) -> bool:
         return isinstance(value, AnyValue)
 
@@ -34,17 +39,19 @@ class AnyValue(Generic[Value], BaseChannel[Value, Value, Value]):
 
     def update(self, values: Sequence[Value]) -> bool:
         if len(values) == 0:
-            try:
-                del self.value
-                return True
-            except AttributeError:
+            if self.value is MISSING:
                 return False
+            else:
+                self.value = MISSING
+                return True
 
         self.value = values[-1]
         return True
 
     def get(self) -> Value:
-        try:
-            return self.value
-        except AttributeError:
+        if self.value is MISSING:
             raise EmptyChannelError()
+        return self.value
+
+    def is_available(self) -> bool:
+        return self.value is not MISSING

libs/langgraph/langgraph/channels/base.py

@@ -64,6 +64,17 @@ class BaseChannel(Generic[Value, Update, C], ABC):
         """
         return False
 
+    def is_available(self) -> bool:
+        """Return True if the channel is available (not empty), False otherwise.
+        Subclasses should override this method to provide a more efficient
+        implementation than calling get() and catching EmptyChannelError.
+        """
+        try:
+            self.get()
+            return True
+        except EmptyChannelError:
+            return False
+
 
 __all__ = [
     "BaseChannel",

libs/langgraph/langgraph/channels/binop.py

@@ -10,6 +10,7 @@ from typing import (
 from typing_extensions import NotRequired, Required, Self
 
 from langgraph.channels.base import BaseChannel, Value
+from langgraph.constants import MISSING
 from langgraph.errors import EmptyChannelError
 
 
@@ -51,7 +52,7 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
         try:
             self.value = typ()
         except Exception:
-            pass
+            self.value = MISSING
 
     def __eq__(self, value: object) -> bool:
         return isinstance(value, BinaryOperatorAggregate) and (
@@ -81,7 +82,7 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
     def update(self, values: Sequence[Value]) -> bool:
         if not values:
             return False
-        if not hasattr(self, "value"):
+        if self.value is MISSING:
             self.value = values[0]
             values = values[1:]
         for value in values:
@@ -89,7 +90,9 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
         return True
 
     def get(self) -> Value:
-        try:
-            return self.value
-        except AttributeError:
+        if self.value is MISSING:
             raise EmptyChannelError()
+        return self.value
+
+    def is_available(self) -> bool:
+        return self.value is not MISSING

libs/langgraph/langgraph/channels/dynamic_barrier_value.py

@@ -85,6 +85,9 @@ class DynamicBarrierValue(
             raise EmptyChannelError()
         return None
 
+    def is_available(self) -> bool:
+        return self.seen == self.names
+
     def consume(self) -> bool:
         if self.seen == self.names:
             self.seen = set()

libs/langgraph/langgraph/channels/ephemeral_value.py

@@ -3,6 +3,7 @@ from typing import Any, Generic, Optional, Sequence, Type
 from typing_extensions import Self
 
 from langgraph.channels.base import BaseChannel, Value
+from langgraph.constants import MISSING
 from langgraph.errors import EmptyChannelError, InvalidUpdateError
 
 
@@ -14,6 +15,7 @@ class EphemeralValue(Generic[Value], BaseChannel[Value, Value, Value]):
     def __init__(self, typ: Any, guard: bool = True) -> None:
         super().__init__(typ)
         self.guard = guard
+        self.value = MISSING
 
     def __eq__(self, value: object) -> bool:
         return isinstance(value, EphemeralValue) and value.guard == self.guard
@@ -37,10 +39,10 @@ class EphemeralValue(Generic[Value], BaseChannel[Value, Value, Value]):
 
     def update(self, values: Sequence[Value]) -> bool:
         if len(values) == 0:
-            try:
-                del self.value
+            if self.value is not MISSING:
+                self.value = MISSING
                 return True
-            except AttributeError:
+            else:
                 return False
         if len(values) != 1 and self.guard:
             raise InvalidUpdateError(
@@ -51,7 +53,9 @@ class EphemeralValue(Generic[Value], BaseChannel[Value, Value, Value]):
         return True
 
     def get(self) -> Value:
-        try:
-            return self.value
-        except AttributeError:
+        if self.value is MISSING:
             raise EmptyChannelError()
+        return self.value
+
+    def is_available(self) -> bool:
+        return self.value is not MISSING

libs/langgraph/langgraph/channels/last_value.py

@@ -1,8 +1,9 @@
-from typing import Generic, Optional, Sequence, Type
+from typing import Any, Generic, Optional, Sequence, Type
 
 from typing_extensions import Self
 
 from langgraph.channels.base import BaseChannel, Value
+from langgraph.constants import MISSING
 from langgraph.errors import (
     EmptyChannelError,
     ErrorCode,
@@ -16,6 +17,10 @@ class LastValue(Generic[Value], BaseChannel[Value, Value, Value]):
 
     __slots__ = ("value",)
 
+    def __init__(self, typ: Any, key: str = "") -> None:
+        super().__init__(typ, key)
+        self.value = MISSING
+
     def __eq__(self, value: object) -> bool:
         return isinstance(value, LastValue)
 
@@ -50,7 +55,9 @@ class LastValue(Generic[Value], BaseChannel[Value, Value, Value]):
         return True
 
     def get(self) -> Value:
-        try:
-            return self.value
-        except AttributeError:
+        if self.value is MISSING:
             raise EmptyChannelError()
+        return self.value
+
+    def is_available(self) -> bool:
+        return self.value is not MISSING

libs/langgraph/langgraph/channels/named_barrier_value.py

@@ -60,6 +60,9 @@ class NamedBarrierValue(Generic[Value], BaseChannel[Value, Value, set[Value]]):
             raise EmptyChannelError()
         return None
 
+    def is_available(self) -> bool:
+        return self.seen == self.names
+
     def consume(self) -> bool:
         if self.seen == self.names:
             self.seen = set()

libs/langgraph/langgraph/channels/topic.py

@@ -75,3 +75,6 @@ class Topic(
             return list(self.values)
         else:
             raise EmptyChannelError
+
+    def is_available(self) -> bool:
+        return bool(self.values)

libs/langgraph/langgraph/channels/untracked_value.py

@@ -3,6 +3,7 @@ from typing import Generic, Optional, Sequence, Type
 from typing_extensions import Self
 
 from langgraph.channels.base import BaseChannel, Value
+from langgraph.constants import MISSING
 from langgraph.errors import EmptyChannelError, InvalidUpdateError
 
 
@@ -14,6 +15,7 @@ class UntrackedValue(Generic[Value], BaseChannel[Value, Value, Value]):
     def __init__(self, typ: Type[Value], guard: bool = True) -> None:
         super().__init__(typ)
         self.guard = guard
+        self.value = MISSING
 
     def __eq__(self, value: object) -> bool:
         return isinstance(value, UntrackedValue) and value.guard == self.guard
@@ -48,7 +50,9 @@ class UntrackedValue(Generic[Value], BaseChannel[Value, Value, Value]):
         return True
 
     def get(self) -> Value:
-        try:
-            return self.value
-        except AttributeError:
+        if self.value is MISSING:
             raise EmptyChannelError()
+        return self.value
+
+    def is_available(self) -> bool:
+        return self.value is not MISSING

libs/langgraph/langgraph/graph/branch.py

@@ -138,6 +138,7 @@ class Branch(NamedTuple):
                 reader=reader,
                 name=None,
                 trace=False,
+                func_accepts_config=True,
             )
         )
 

libs/langgraph/langgraph/graph/graph.py

@@ -1,3 +1,4 @@
+import asyncio
 import logging
 from collections import defaultdict
 from typing import (
@@ -31,6 +32,7 @@ from langgraph.constants import (
 )
 from langgraph.graph.branch import Branch
 from langgraph.pregel import Channel, Pregel
+from langgraph.pregel.protocol import PregelProtocol
 from langgraph.pregel.read import PregelNode
 from langgraph.pregel.write import ChannelWrite, ChannelWriteEntry
 from langgraph.types import All, Checkpointer
@@ -418,7 +420,38 @@ class CompiledGraph(Pregel):
         *,
         xray: Union[int, bool] = False,
     ) -> DrawableGraph:
-        return self.get_graph(config, xray=xray)
+        """Returns a drawable representation of the computation graph."""
+        from langgraph.pregel.remote import RemoteGraph
+
+        # gather subgraphs
+        if xray:
+            subpregels: dict[str, PregelProtocol] = {
+                k: v
+                async for k, v in self.aget_subgraphs()
+                if isinstance(v, (CompiledGraph, RemoteGraph))
+            }
+            subgraphs = {
+                k: v
+                for k, v in zip(
+                    subpregels,
+                    await asyncio.gather(
+                        *(
+                            p.aget_graph(
+                                config,
+                                xray=xray
+                                if isinstance(xray, bool) or xray <= 0
+                                else xray - 1,
+                            )
+                            for p in subpregels.values()
+                        )
+                    ),
+                )
+            }
+        else:
+            subgraphs = {}
+
+        # draw the graph
+        return self._draw_graph(config, subgraphs=subgraphs)
 
     def get_graph(
         self,
@@ -427,17 +460,36 @@ class CompiledGraph(Pregel):
         xray: Union[int, bool] = False,
     ) -> DrawableGraph:
         """Returns a drawable representation of the computation graph."""
+        from langgraph.pregel.remote import RemoteGraph
+
+        # gather subgraphs
+        if xray:
+            subgraphs = {
+                k: v.get_graph(
+                    config,
+                    xray=xray if isinstance(xray, bool) or xray <= 0 else xray - 1,
+                )
+                for k, v in self.get_subgraphs()
+                if isinstance(v, (CompiledGraph, RemoteGraph))
+            }
+        else:
+            subgraphs = {}
+
+        # draw the graph
+        return self._draw_graph(config, subgraphs=subgraphs)
+
+    def _draw_graph(
+        self,
+        config: Optional[RunnableConfig] = None,
+        *,
+        subgraphs: dict[str, DrawableGraph] = {},
+    ) -> DrawableGraph:
+        # create the graph
         graph = DrawableGraph()
         start_nodes: dict[str, DrawableNode] = {
             START: graph.add_node(self.get_input_schema(config), START)
         }
         end_nodes: dict[str, DrawableNode] = {}
-        if xray:
-            subgraphs = {
-                k: v for k, v in self.get_subgraphs() if isinstance(v, CompiledGraph)
-            }
-        else:
-            subgraphs = {}
 
         def add_edge(
             start: str,
@@ -463,13 +515,8 @@ class CompiledGraph(Pregel):
                 metadata["__interrupt"] = "before"
             elif key in self.interrupt_after_nodes:
                 metadata["__interrupt"] = "after"
-            if xray and key in subgraphs:
-                subgraph = subgraphs[key].get_graph(
-                    config=config,
-                    xray=xray - 1
-                    if isinstance(xray, int) and not isinstance(xray, bool) and xray > 0
-                    else xray,
-                )
+            if key in subgraphs:
+                subgraph = subgraphs[key]
                 subgraph.trim_first_node()
                 subgraph.trim_last_node()
                 if len(subgraph.nodes) >= 1:

libs/langgraph/langgraph/graph/schema_utils.py

@@ -9,6 +9,7 @@ from typing import (
     Union,
     get_args,
     get_origin,
+    get_type_hints,
 )
 
 from pydantic import BaseModel
@@ -18,34 +19,56 @@ from typing_extensions import Annotated
 logger = logging.getLogger(__name__)
 
 
-class SchemaCoercionMapper:
-    _cache: weakref.WeakKeyDictionary[Type[Any], dict[int, "SchemaCoercionMapper"]] = (
-        weakref.WeakKeyDictionary()
-    )
+_cache: weakref.WeakKeyDictionary[Type[Any], dict[int, "SchemaCoercionMapper"]] = (
+    weakref.WeakKeyDictionary()
+)
 
-    def __new__(cls, schema: Type[Any], max_depth: int = 5) -> "SchemaCoercionMapper":
-        if schema not in cls._cache:
-            cls._cache[schema] = {}
-        if max_depth in cls._cache[schema]:
-            return cls._cache[schema][max_depth]
+
+class SchemaCoercionMapper:
+    def __new__(
+        cls,
+        schema: Type[Any],
+        type_hints: Optional[dict[str, Any]] = None,
+        max_depth: int = 12,
+    ) -> "SchemaCoercionMapper":
+        if schema not in _cache:
+            _cache[schema] = {}
+        if max_depth in _cache[schema]:
+            return _cache[schema][max_depth]
 
         inst = super().__new__(cls)
-        cls._cache[schema][max_depth] = inst
+        _cache[schema][max_depth] = inst
         return inst
 
-    def __init__(self, schema: Type[Any], max_depth: int = 5):
+    def __init__(
+        self,
+        schema: Type[Any],
+        type_hints: Optional[dict[str, Any]] = None,
+        max_depth: int = 12,
+    ):
         if hasattr(self, "_inited"):
             return
         self._inited = True
         self.schema = schema
+        self.type_hints = (
+            type_hints
+            if type_hints is not None
+            else get_type_hints(schema, localns={schema.__name__: schema})
+        )
         self.max_depth = max_depth
-        if hasattr(schema, "model_fields") and hasattr(schema, "model_construct"):
-            self._fields = {n: f.annotation for n, f in schema.model_fields.items()}
-            self._construct = schema.model_construct
-        elif hasattr(schema, "__fields__") and callable(
-            getattr(schema, "construct", None)
-        ):
-            self._fields = {n: f.annotation for n, f in schema.__fields__.items()}
+
+        if 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
+
+        elif issubclass(schema, BaseModelV1):
+            self._fields = {
+                n: self.type_hints.get(n, f.annotation)
+                for n, f in schema.__fields__.items()
+            }
             self._construct = schema.construct
         else:
             raise TypeError("Schema is neither valid Pydantic v1 nor v2 model.")
@@ -62,18 +85,23 @@ class SchemaCoercionMapper:
         processed = {}
         if self._field_coercers is None:
             self._field_coercers = {
-                n: self._build_coercer(t) for n, t in self._fields.items()
+                n: self._build_coercer(t, depth - 1) for n, t in self._fields.items()
             }
         for k, v in input_data.items():
             fn = self._field_coercers.get(k)
             processed[k] = fn(v, depth - 1) if fn else v
         return self._construct(**processed)
 
-    def _build_coercer(self, field_type: Any) -> Callable[[Any, Any], Any]:
+    def _build_coercer(
+        self, field_type: Any, depth: int, throw: bool = False
+    ) -> Callable[[Any, Any], Any]:
+        if depth == 0:
+            return self._passthrough
         origin = get_origin(field_type)
+
         if origin is Annotated:
             real_type, *_ = get_args(field_type)
-            sub = self._build_coercer(real_type)
+            sub = self._build_coercer(real_type, depth - 1)
             return lambda v, d: sub(v, d)
         if isclass(field_type):
             is_class_ = True
@@ -84,39 +112,54 @@ class SchemaCoercionMapper:
                 is_base_model = False
 
             if is_base_model:
-                mapper = SchemaCoercionMapper(field_type, self.max_depth)
+                mapper = SchemaCoercionMapper(field_type, max_depth=depth - 1)
                 return lambda v, d: mapper.coerce(v, d) if isinstance(v, dict) else v
             if is_class_ and issubclass(field_type, BaseModelV1):
-                mapper = SchemaCoercionMapper(field_type, self.max_depth)
+                mapper = SchemaCoercionMapper(field_type, max_depth=depth - 1)
                 return lambda v, d: mapper.coerce(v, d) if isinstance(v, dict) else v
         if origin is list or field_type is list:
             args = get_args(field_type)
             if len(args) != 1:
                 return lambda v, d: v
-            sub = self._build_coercer(args[0])
+            sub = self._build_coercer(args[0], depth - 1)
 
             def list_coercer(v: Any, d: Any) -> Any:
                 if not isinstance(v, (list, tuple)):
-                    raise TypeError(f"Expected list, got {type(v).__name__}")
+                    return v
                 return [sub(x, d - 1) for x in v]
 
             return list_coercer
+        if origin is set or field_type is set:
+            args = get_args(field_type)
+            if len(args) != 1:
+                return lambda v, d: v
+            sub = self._build_coercer(args[0], depth - 1)
+
+            def set_coercer(v: Any, d: Any) -> Any:
+                if not isinstance(v, (list, tuple, set)):
+                    return v
+                return {sub(x, d - 1) for x in v}
+
+            return set_coercer
         if origin is dict or field_type is dict:
             args = get_args(field_type)
             if len(args) != 2:
 
-                def plain_dict_coercer(v: Any, d: Any) -> Any:
+                def dict_coercer(v: Any, d: Any) -> Any:
                     if not isinstance(v, dict):
-                        raise TypeError(f"Expected dict, got {type(v).__name__}")
+                        if throw:
+                            raise TypeError("Expected dict, got %s" % type(v))
                     return v
 
-                return plain_dict_coercer
-            k_sub = self._build_coercer(args[0])
-            v_sub = self._build_coercer(args[1])
+                return dict_coercer
+            k_sub = self._build_coercer(args[0], depth - 1)
+            v_sub = self._build_coercer(args[1], depth - 1)
 
             def dict_coercer(v: Any, d: Any) -> Any:
                 if not isinstance(v, dict):
-                    raise TypeError(f"Expected dict, got {type(v).__name__}")
+                    if throw:
+                        raise TypeError("Expected dict, got %s" % type(v))
+                    return v
                 return {k_sub(k, d - 1): v_sub(val, d - 1) for k, val in v.items()}
 
             return dict_coercer
@@ -125,11 +168,11 @@ class SchemaCoercionMapper:
             targs = get_args(field_type)
             if not targs:
                 return lambda v, d: v
-            subs = [self._build_coercer(a) for a in targs]
+            subs = [self._build_coercer(a, depth - 1) for a in targs]
 
             def tuple_coercer(v: Any, d: Any) -> Any:
                 if not isinstance(v, (list, tuple)):
-                    raise TypeError(f"Expected tuple-like, got {type(v).__name__}")
+                    return v
                 out = []
                 for i, sp in enumerate(subs):
                     out.append(sp(v[i] if i < len(v) else None, d - 1))
@@ -139,11 +182,13 @@ class SchemaCoercionMapper:
         if origin is Union:
             uargs = get_args(field_type)
             subs, none_in_union = [], False
-            for arg in uargs:
+            for ix, arg in enumerate(uargs):
                 if arg is type(None):
                     none_in_union = True
                 else:
-                    subs.append(self._build_coercer(arg))
+                    subs.append(
+                        self._build_coercer(arg, depth - 1, throw=ix < len(uargs) - 1)
+                    )
 
             def union_coercer(v: Any, d: Any) -> Any:
                 if v is None and none_in_union:
@@ -152,11 +197,14 @@ class SchemaCoercionMapper:
                 for sp in subs:
                     try:
                         return sp(v, d - 1)
-                    except Exception as e:
+                    except TypeError as e:
                         err = e
                 if err:
                     raise err
                 return v
 
             return union_coercer
-        return lambda v, d: v
+        return self._passthrough
+
+    def _passthrough(self, v: Any, d: Any) -> Any:
+        return v

libs/langgraph/langgraph/graph/state.py

@@ -185,6 +185,7 @@ class StateGraph(Graph):
         self.schemas = {}
         self.channels = {}
         self.managed = {}
+        self.type_hints: dict[Type[Any], dict[str, Any]] = {}
         self.schema = state_schema
         self.input = input
         self.output = output
@@ -203,7 +204,7 @@ class StateGraph(Graph):
     def _add_schema(self, schema: Type[Any], /, allow_managed: bool = True) -> None:
         if schema not in self.schemas:
             _warn_invalid_state_schema(schema)
-            channels, managed = _get_channels(schema)
+            channels, managed, type_hints = _get_channels(schema)
             if managed and not allow_managed:
                 names = ", ".join(managed)
                 schema_name = getattr(schema, "__name__", "")
@@ -212,6 +213,7 @@ class StateGraph(Graph):
                     " Managed channels are not permitted in Input/Output schema."
                 )
             self.schemas[schema] = {**channels, **managed}
+            self.type_hints[schema] = type_hints
             for key, channel in channels.items():
                 if key in self.channels:
                     if self.channels[key] != channel:
@@ -416,7 +418,7 @@ class StateGraph(Graph):
                         and (vals := get_args(rargs[0]))
                     ):
                         ends = vals
-        except (TypeError, StopIteration):
+        except (NameError, TypeError, StopIteration):
             pass
 
         if destinations is not None:
@@ -821,13 +823,19 @@ class CompiledStateGraph(CompiledGraph):
             input_values = {k: k for k in self.builder.schemas[input_schema]}
             is_single_input = len(input_values) == 1 and "__root__" in input_values
 
+            branch_channel = f"branch:to:{key}"
             self.channels[key] = EphemeralValue(Any, guard=False)
+            self.channels[branch_channel] = EphemeralValue(Any, guard=False)
             self.nodes[key] = PregelNode(
-                triggers=[],
+                triggers=[branch_channel],
                 # read state keys and managed values
                 channels=(list(input_values) if is_single_input else input_values),
                 # coerce state dict to schema class (eg. pydantic model)
-                mapper=_pick_mapper(list(input_values), input_schema),
+                mapper=_pick_mapper(
+                    list(input_values),
+                    input_schema,
+                    self.builder.type_hints[input_schema],
+                ),
                 writers=[
                     # publish to this channel and state keys
                     ChannelWrite(
@@ -851,8 +859,10 @@ class CompiledStateGraph(CompiledGraph):
                 # subscribe to channel
                 self.nodes[end].triggers.append(channel_name)
                 # publish to channel
-                self.nodes[START] |= ChannelWrite(
-                    [ChannelWriteEntry(channel_name, START)], tags=[TAG_HIDDEN]
+                self.nodes[START].writers.append(
+                    ChannelWrite(
+                        [ChannelWriteEntry(channel_name, START)], tags=[TAG_HIDDEN]
+                    )
                 )
             elif end != END:
                 # subscribe to start channel
@@ -865,8 +875,10 @@ class CompiledStateGraph(CompiledGraph):
             self.nodes[end].triggers.append(channel_name)
             # publish to channel
             for start in starts:
-                self.nodes[start] |= ChannelWrite(
-                    [ChannelWriteEntry(channel_name, start)], tags=[TAG_HIDDEN]
+                self.nodes[start].writers.append(
+                    ChannelWrite(
+                        [ChannelWriteEntry(channel_name, start)], tags=[TAG_HIDDEN]
+                    )
                 )
 
     def attach_branch(
@@ -878,7 +890,7 @@ class CompiledStateGraph(CompiledGraph):
             if filtered := [p for p in packets if p != END]:
                 writes = [
                     (
-                        ChannelWriteEntry(f"branch:{start}:{name}:{p}", start)
+                        ChannelWriteEntry(f"branch:to:{p}", start)
                         if not isinstance(p, Send)
                         else p
                     )
@@ -902,33 +914,31 @@ class CompiledStateGraph(CompiledGraph):
             if start in self.builder.nodes
             else self.builder.schema
         )
-        # attach branch publisher
-        self.nodes[start] |= branch.run(
-            branch_writer,
-            _get_state_reader(self.builder, schema) if with_reader else None,
-        )
 
-        # attach branch subscribers
-        ends = (
-            branch.ends.values()
-            if branch.ends
-            else [node for node in self.builder.nodes if node != branch.then]
+        # attach branch publisher
+        self.nodes[start].writers.append(
+            branch.run(
+                branch_writer,
+                _get_state_reader(self.builder, schema) if with_reader else None,
+            )
         )
-        for end in ends:
-            if end != END:
-                channel_name = f"branch:{start}:{name}:{end}"
-                self.channels[channel_name] = EphemeralValue(Any, guard=False)
-                self.nodes[end].triggers.append(channel_name)
 
         # attach then subscriber
         if branch.then and branch.then != END:
+            ends = (
+                branch.ends.values()
+                if branch.ends
+                else [node for node in self.builder.nodes if node != branch.then]
+            )
             channel_name = f"branch:{start}:{name}::then"
             self.channels[channel_name] = DynamicBarrierValue(str)
             self.nodes[branch.then].triggers.append(channel_name)
             for end in ends:
                 if end != END:
-                    self.nodes[end] |= ChannelWrite(
-                        [ChannelWriteEntry(channel_name, end)], tags=[TAG_HIDDEN]
+                    self.nodes[end].writers.append(
+                        ChannelWrite(
+                            [ChannelWriteEntry(channel_name, end)], tags=[TAG_HIDDEN]
+                        )
                     )
 
 
@@ -942,12 +952,12 @@ def _get_state_reader(
         select=select[0] if select == ["__root__"] else select,
         fresh=True,
         # coerce state dict to schema class (eg. pydantic model)
-        mapper=_pick_mapper(state_keys, schema),
+        mapper=_pick_mapper(state_keys, schema, builder.type_hints[schema]),
     )
 
 
 def _pick_mapper(
-    state_keys: Sequence[str], schema: Type[Any]
+    state_keys: Sequence[str], schema: Type[Any], type_hints: Optional[dict[str, Any]]
 ) -> Optional[Callable[[Any], Any]]:
     if state_keys == ["__root__"]:
         return None
@@ -955,7 +965,7 @@ def _pick_mapper(
         if issubclass(schema, dict):
             return None
         if issubclass(schema, (BaseModel, BaseModelV1)):
-            return SchemaCoercionMapper(schema)
+            return SchemaCoercionMapper(schema, type_hints)
     return partial(_coerce_state, schema)
 
 
@@ -1010,25 +1020,36 @@ async def _acontrol_branch(value: Any) -> Sequence[Union[str, Send]]:
 
 
 CONTROL_BRANCH_PATH = RunnableCallable(
-    _control_branch, _acontrol_branch, tags=[TAG_HIDDEN], trace=False, recurse=False
+    _control_branch,
+    _acontrol_branch,
+    tags=[TAG_HIDDEN],
+    trace=False,
+    recurse=False,
+    func_accepts_config=False,
 )
 CONTROL_BRANCH = Branch(CONTROL_BRANCH_PATH, None)
 
 
 def _get_channels(
     schema: Type[dict],
-) -> tuple[dict[str, BaseChannel], dict[str, ManagedValueSpec]]:
+) -> tuple[dict[str, BaseChannel], dict[str, ManagedValueSpec], dict[str, Any]]:
     if not hasattr(schema, "__annotations__"):
-        return {"__root__": _get_channel("__root__", schema, allow_managed=False)}, {}
+        return (
+            {"__root__": _get_channel("__root__", schema, allow_managed=False)},
+            {},
+            {},
+        )
 
+    type_hints = get_type_hints(schema, include_extras=True)
     all_keys = {
         name: _get_channel(name, typ)
-        for name, typ in get_type_hints(schema, include_extras=True).items()
+        for name, typ in type_hints.items()
         if name != "__slots__"
     }
     return (
         {k: v for k, v in all_keys.items() if isinstance(v, BaseChannel)},
         {k: v for k, v in all_keys.items() if is_managed_value(v)},
+        type_hints,
     )
 
 

libs/langgraph/langgraph/pregel/algo.py

@@ -1,4 +1,4 @@
-import functools
+import binascii
 import itertools
 import sys
 from collections import defaultdict, deque
@@ -19,15 +19,16 @@ from typing import (
     cast,
     overload,
 )
-from uuid import UUID
 
 from langchain_core.callbacks import Callbacks
 from langchain_core.callbacks.manager import AsyncParentRunManager, ParentRunManager
 from langchain_core.runnables.config import RunnableConfig
+from xxhash import xxh3_64_hexdigest
 
 from langgraph.channels.base import BaseChannel
 from langgraph.checkpoint.base import (
     BaseCheckpointSaver,
+    ChannelVersions,
     Checkpoint,
     PendingWrite,
     V,
@@ -233,10 +234,21 @@ def apply_writes(
     channels: Mapping[str, BaseChannel],
     tasks: Iterable[WritesProtocol],
     get_next_version: Optional[GetNextVersion],
-) -> dict[str, list[Any]]:
+) -> tuple[dict[str, list[Any]], set[str]]:
     """Apply writes from a set of tasks (usually the tasks from a Pregel step)
     to the checkpoint and channels, and return managed values writes to be applied
-    externally."""
+    externally.
+
+    Args:
+        checkpoint: The checkpoint to update.
+        channels: The channels to update.
+        tasks: The tasks to apply writes from.
+        get_next_version: Optional function to determine the next version of a channel.
+
+    Returns:
+        A tuple containing the managed values writes to be applied externally, and
+        the set of channels that were updated in this step.
+    """
     # sort tasks on path, to ensure deterministic order for update application
     # any path parts after the 3rd are ignored for sorting
     # (we use them for eg. task ids which aren't good for sorting)
@@ -312,15 +324,14 @@ def apply_writes(
     # Channels that weren't updated in this step are notified of a new step
     if bump_step:
         for chan in channels:
-            if chan not in updated_channels:
-                if channels[chan].update([]) and get_next_version is not None:
+            if channels[chan].is_available() and chan not in updated_channels:
+                if channels[chan].update(EMPTY_SEQ) and get_next_version is not None:
                     checkpoint["channel_versions"][chan] = get_next_version(
                         max_version,
                         channels[chan],
                     )
-
     # Return managed values writes to be applied externally
-    return pending_writes_by_managed
+    return pending_writes_by_managed, updated_channels
 
 
 @overload
@@ -337,6 +348,8 @@ def prepare_next_tasks(
     store: Literal[None] = None,
     checkpointer: Literal[None] = None,
     manager: Literal[None] = None,
+    trigger_to_nodes: Optional[Mapping[str, Sequence[str]]] = None,
+    updated_channels: Optional[set[str]] = None,
 ) -> dict[str, PregelTask]: ...
 
 
@@ -354,6 +367,8 @@ def prepare_next_tasks(
     store: Optional[BaseStore],
     checkpointer: Optional[BaseCheckpointSaver],
     manager: Union[None, ParentRunManager, AsyncParentRunManager],
+    trigger_to_nodes: Optional[Mapping[str, Sequence[str]]] = None,
+    updated_channels: Optional[set[str]] = None,
 ) -> dict[str, PregelExecutableTask]: ...
 
 
@@ -370,10 +385,37 @@ def prepare_next_tasks(
     store: Optional[BaseStore] = None,
     checkpointer: Optional[BaseCheckpointSaver] = None,
     manager: Union[None, ParentRunManager, AsyncParentRunManager] = None,
+    trigger_to_nodes: Optional[Mapping[str, Sequence[str]]] = None,
+    updated_channels: Optional[set[str]] = None,
 ) -> Union[dict[str, PregelTask], dict[str, PregelExecutableTask]]:
     """Prepare the set of tasks that will make up the next Pregel step.
-    This is the union of all PUSH tasks (Sends) and PULL tasks (nodes triggered
-    by edges)."""
+
+    Args:
+        checkpoint: The current checkpoint.
+        pending_writes: The list of pending writes.
+        processes: The mapping of process names to PregelNode instances.
+        channels: The mapping of channel names to BaseChannel instances.
+        managed: The mapping of managed value names to functions.
+        config: The runnable configuration.
+        step: The current step.
+        for_execution: Whether the tasks are being prepared for execution.
+        store: An instance of BaseStore to make it available for usage within tasks.
+        checkpointer: Checkpointer instance used for saving checkpoints.
+        manager: The parent run manager to use for the tasks.
+        trigger_to_nodes: Optional: Mapping of channel names to the set of nodes
+            that are can be triggered by that channel.
+        updated_channels: Optional. Set of channel names that have been updated during
+            the previous step. Using in conjunction with trigger_to_nodes to speed
+            up the process of determining which nodes should be triggered in the next
+            step.
+
+    Returns:
+        A dictionary of tasks to be executed. The keys are the task ids and the values
+        are the tasks themselves. This is the union of all PUSH tasks (Sends)
+        and PULL tasks (nodes triggered by edges).
+    """
+    checkpoint_id_bytes = binascii.unhexlify(checkpoint["id"].replace("-", ""))
+    null_version = checkpoint_null_version(checkpoint)
     tasks: list[Union[PregelTask, PregelExecutableTask]] = []
     # Consume pending_sends from previous step
     for idx, _ in enumerate(checkpoint["pending_sends"]):
@@ -381,6 +423,8 @@ def prepare_next_tasks(
             (PUSH, idx),
             None,
             checkpoint=checkpoint,
+            checkpoint_id_bytes=checkpoint_id_bytes,
+            checkpoint_null_version=null_version,
             pending_writes=pending_writes,
             processes=processes,
             channels=channels,
@@ -393,13 +437,36 @@ def prepare_next_tasks(
             manager=manager,
         ):
             tasks.append(task)
+
+    # This section is an optimization that allows which nodes will be active
+    # during the next step.
+    # When there's information about:
+    # 1. Which channels were updated in the previous step
+    # 2. Which nodes are triggered by which channels
+    # Then we can determine which nodes should be triggered in the next step
+    # without having to cycle through all nodes.
+    if updated_channels and trigger_to_nodes:
+        triggered_nodes: set[str] = set()
+        # Get all nodes that have triggers associated with an updated channel
+        for channel in updated_channels:
+            if node_ids := trigger_to_nodes.get(channel):
+                triggered_nodes.update(node_ids)
+        # Sort the nodes to ensure deterministic order
+        candidate_nodes: Iterable[str] = sorted(triggered_nodes)
+    elif not checkpoint["channel_versions"]:
+        candidate_nodes = ()
+    else:
+        candidate_nodes = processes.keys()
+
     # Check if any processes should be run in next step
     # If so, prepare the values to be passed to them
-    for name in processes:
+    for name in candidate_nodes:
         if task := prepare_single_task(
             (PULL, name),
             None,
             checkpoint=checkpoint,
+            checkpoint_id_bytes=checkpoint_id_bytes,
+            checkpoint_null_version=null_version,
             pending_writes=pending_writes,
             processes=processes,
             channels=channels,
@@ -415,11 +482,16 @@ def prepare_next_tasks(
     return {t.id: t for t in tasks}
 
 
+PUSH_TRIGGER = (PUSH,)
+
+
 def prepare_single_task(
     task_path: tuple[Any, ...],
     task_id_checksum: Optional[str],
     *,
     checkpoint: Checkpoint,
+    checkpoint_id_bytes: bytes,
+    checkpoint_null_version: Optional[V],
     pending_writes: list[PendingWrite],
     processes: Mapping[str, PregelNode],
     channels: Mapping[str, BaseChannel],
@@ -433,9 +505,9 @@ def prepare_single_task(
 ) -> Union[None, PregelTask, PregelExecutableTask]:
     """Prepares a single task for the next Pregel step, given a task path, which
     uniquely identifies a PUSH or PULL task within the graph."""
-    checkpoint_id = UUID(checkpoint["id"]).bytes
     configurable = config.get(CONF, {})
     parent_ns = configurable.get(CONFIG_KEY_CHECKPOINT_NS, "")
+    task_id_func = _xxhash_str if checkpoint["v"] > 1 else _uuid5_str
 
     if task_path[0] == PUSH and isinstance(task_path[-1], Call):
         # (PUSH, parent task path, idx of PUSH write, id of parent task, Call)
@@ -446,10 +518,10 @@ def prepare_single_task(
         if name is None:
             raise ValueError("`call` functions must have a `__name__` attribute")
         # create task id
-        triggers = [PUSH]
+        triggers: Sequence[str] = PUSH_TRIGGER
         checkpoint_ns = f"{parent_ns}{NS_SEP}{name}" if parent_ns else name
-        task_id = _uuid5_str(
-            checkpoint_id,
+        task_id = task_id_func(
+            checkpoint_id_bytes,
             checkpoint_ns,
             str(step),
             name,
@@ -507,6 +579,7 @@ def prepare_single_task(
                         CONFIG_KEY_CHECKPOINT_ID: None,
                         CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
                         CONFIG_KEY_SCRATCHPAD: _scratchpad(
+                            config[CONF].get(CONFIG_KEY_SCRATCHPAD),
                             pending_writes,
                             task_id,
                         ),
@@ -539,12 +612,12 @@ def prepare_single_task(
                 )
                 return
             # create task id
-            triggers = [PUSH]
+            triggers = PUSH_TRIGGER
             checkpoint_ns = (
                 f"{parent_ns}{NS_SEP}{packet.node}" if parent_ns else packet.node
             )
-            task_id = _uuid5_str(
-                checkpoint_id,
+            task_id = task_id_func(
+                checkpoint_id_bytes,
                 checkpoint_ns,
                 str(step),
                 packet.node,
@@ -616,6 +689,7 @@ def prepare_single_task(
                             CONFIG_KEY_CHECKPOINT_ID: None,
                             CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
                             CONFIG_KEY_SCRATCHPAD: _scratchpad(
+                                config[CONF].get(CONFIG_KEY_SCRATCHPAD),
                                 pending_writes,
                                 task_id,
                             ),
@@ -640,21 +714,17 @@ def prepare_single_task(
         if name not in processes:
             return
         proc = processes[name]
-        version_type = type(next(iter(checkpoint["channel_versions"].values()), None))
-        null_version = version_type()  # type: ignore[misc]
-        if null_version is None:
+        if checkpoint_null_version is None:
             return
-        seen = checkpoint["versions_seen"].get(name, {})
         # If any of the channels read by this process were updated
-        if triggers := sorted(
-            chan
-            for chan in proc.triggers
-            if not isinstance(
-                read_channel(channels, chan, return_exception=True), EmptyChannelError
-            )
-            and checkpoint["channel_versions"].get(chan, null_version)  # type: ignore[operator]
-            > seen.get(chan, null_version)
+        if _triggers(
+            channels,
+            checkpoint["channel_versions"],
+            checkpoint["versions_seen"].get(name),
+            checkpoint_null_version,
+            proc,
         ):
+            triggers = tuple(sorted(proc.triggers))
             try:
                 val = next(
                     _proc_input(proc, managed, channels, for_execution=for_execution)
@@ -670,8 +740,8 @@ def prepare_single_task(
 
             # create task id
             checkpoint_ns = f"{parent_ns}{NS_SEP}{name}" if parent_ns else name
-            task_id = _uuid5_str(
-                checkpoint_id,
+            task_id = task_id_func(
+                checkpoint_id_bytes,
                 checkpoint_ns,
                 str(step),
                 name,
@@ -714,7 +784,7 @@ def prepare_single_task(
                                 CONFIG_KEY_SEND: partial(
                                     local_write,
                                     writes.extend,
-                                    processes.keys(),
+                                    tuple(processes.keys()),
                                 ),
                                 CONFIG_KEY_READ: partial(
                                     local_read,
@@ -723,7 +793,10 @@ def prepare_single_task(
                                     channels,
                                     managed,
                                     PregelTaskWrites(
-                                        task_path[:3], name, writes, triggers
+                                        task_path[:3],
+                                        name,
+                                        writes,
+                                        triggers,
                                     ),
                                     config,
                                 ),
@@ -741,6 +814,7 @@ def prepare_single_task(
                                 CONFIG_KEY_CHECKPOINT_ID: None,
                                 CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
                                 CONFIG_KEY_SCRATCHPAD: _scratchpad(
+                                    config[CONF].get(CONFIG_KEY_SCRATCHPAD),
                                     pending_writes,
                                     task_id,
                                 ),
@@ -761,13 +835,59 @@ def prepare_single_task(
                 return PregelTask(task_id, name, task_path[:3])
 
 
+def checkpoint_null_version(
+    checkpoint: Checkpoint,
+) -> Optional[V]:
+    """Get the null version for the checkpoint, if available."""
+    for version in checkpoint["channel_versions"].values():
+        return type(version)()
+    return None
+
+
+def _triggers(
+    channels: Mapping[str, BaseChannel],
+    versions: ChannelVersions,
+    seen: Optional[ChannelVersions],
+    null_version: V,
+    proc: PregelNode,
+) -> Sequence[str]:
+    if seen is None:
+        for chan in proc.triggers:
+            if channels[chan].is_available():
+                return (chan,)
+    else:
+        for chan in proc.triggers:
+            if channels[chan].is_available() and versions.get(  # type: ignore[operator]
+                chan, null_version
+            ) > seen.get(chan, null_version):
+                return (chan,)
+    return EMPTY_SEQ
+
+
 def _scratchpad(
+    parent_scratchpad: Optional[PregelScratchpad],
     pending_writes: list[PendingWrite],
     task_id: str,
 ) -> PregelScratchpad:
+    # None cannot be used as a resume value, because it would be difficult to
+    # distinguish from missing when used over http
     null_resume_write = next(
         (w for w in pending_writes if w[0] == NULL_TASK_ID and w[1] == RESUME), None
     )
+
+    def get_null_resume(consume: bool = False) -> Any:
+        if null_resume_write is None:
+            if parent_scratchpad is not None:
+                return parent_scratchpad.get_null_resume(consume)
+            return None
+        if consume:
+            try:
+                pending_writes.remove(null_resume_write)
+                return null_resume_write[2]
+            except ValueError:
+                return None
+        return null_resume_write[2]
+
     # using itertools.count as an atomic counter (+= 1 is not thread-safe)
     return PregelScratchpad(
         # call
@@ -777,10 +897,7 @@ def _scratchpad(
         resume=next(
             (w[2] for w in pending_writes if w[0] == task_id and w[1] == RESUME), []
         ),
-        null_resume=null_resume_write[2] if null_resume_write is not None else None,
-        _consume_null_resume=functools.partial(pending_writes.remove, null_resume_write)
-        if null_resume_write is not None
-        else lambda: None,
+        get_null_resume=get_null_resume,
         # subgraph
         subgraph_counter=itertools.count(0).__next__,
     )
@@ -833,7 +950,7 @@ def _proc_input(
 
 
 def _uuid5_str(namespace: bytes, *parts: str) -> str:
-    """Generate a UUID from the SHA-1 hash of a namespace UUID and a name."""
+    """Generate a UUID from the SHA-1 hash of a namespace and str parts."""
 
     sha = sha1(namespace, usedforsecurity=False)
     sha.update(b"".join(p.encode() for p in parts))
@@ -841,6 +958,12 @@ def _uuid5_str(namespace: bytes, *parts: str) -> str:
     return f"{hex[:8]}-{hex[8:12]}-{hex[12:16]}-{hex[16:20]}-{hex[20:32]}"
 
 
+def _xxhash_str(namespace: bytes, *parts: str) -> str:
+    """Generate a UUID from the XXH3 hash of a namespace and str parts."""
+    hex = xxh3_64_hexdigest(namespace + b"".join(p.encode() for p in parts))
+    return f"{hex[:8]}-{hex[8:12]}-{hex[12:16]}-{hex[16:20]}-{hex[20:32]}"
+
+
 def task_path_str(tup: Union[str, int, tuple]) -> str:
     """Generate a string representation of the task path."""
     return (

libs/langgraph/langgraph/pregel/debug.py

@@ -137,7 +137,12 @@ def map_debug_task_results(
             "result": [
                 w for w in writes if w[0] in stream_channels_list or w[0] == RETURN
             ],
-            "interrupts": [asdict(w[1]) for w in writes if w[0] == INTERRUPT],
+            "interrupts": [
+                asdict(v)
+                for w in writes
+                if w[0] == INTERRUPT
+                for v in (w[1] if isinstance(w[1], Sequence) else [w[1]])
+            ],
         },
     }
 
@@ -293,8 +298,9 @@ def tasks_w_writes(
                 ),
                 tuple(
                     v
-                    for tid, n, v in pending_writes
+                    for tid, n, vv in pending_writes
                     if tid == task.id and n == INTERRUPT
+                    for v in (vv if isinstance(vv, Sequence) else [vv])
                 ),
                 states.get(task.id) if states else None,
                 (

libs/langgraph/langgraph/pregel/io.py

@@ -14,7 +14,6 @@ from langgraph.constants import (
     NULL_TASK_ID,
     RESUME,
     RETURN,
-    SELF,
     START,
     TAG_HIDDEN,
     TASKS,
@@ -28,7 +27,7 @@ def is_task_id(task_id: str) -> bool:
     """Check if a string is a valid task id."""
     try:
         UUID(task_id)
-    except ValueError:
+    except Exception:
         return False
     return True
 
@@ -38,14 +37,11 @@ def read_channel(
     chan: str,
     *,
     catch: bool = True,
-    return_exception: bool = False,
 ) -> Any:
     try:
         return channels[chan].get()
-    except EmptyChannelError as exc:
-        if return_exception:
-            return exc
-        elif catch:
+    except EmptyChannelError:
+        if catch:
             return None
         else:
             raise
@@ -84,7 +80,7 @@ def map_command(
             if isinstance(send, Send):
                 yield (NULL_TASK_ID, TASKS, send)
             elif isinstance(send, str):
-                yield (NULL_TASK_ID, f"branch:{START}:{SELF}:{send}", START)
+                yield (NULL_TASK_ID, f"branch:to:{send}", START)
             else:
                 raise TypeError(
                     f"In Command.goto, expected Send/str, got {type(send).__name__}"

libs/langgraph/langgraph/pregel/loop.py

@@ -1,5 +1,7 @@
 import asyncio
+import binascii
 import concurrent.futures
+import dataclasses
 from collections import defaultdict, deque
 from contextlib import AsyncExitStack, ExitStack
 from inspect import signature
@@ -79,6 +81,7 @@ from langgraph.pregel.algo import (
     GetNextVersion,
     PregelTaskWrites,
     apply_writes,
+    checkpoint_null_version,
     increment,
     prepare_next_tasks,
     prepare_single_task,
@@ -207,6 +210,7 @@ class PregelLoop(LoopProtocol):
         manager: Union[None, AsyncParentRunManager, ParentRunManager] = None,
         input_model: Optional[Type[BaseModel]] = None,
         debug: bool = False,
+        trigger_to_nodes: Optional[Mapping[str, Sequence[str]]] = None,
     ) -> None:
         super().__init__(
             step=0,
@@ -230,6 +234,7 @@ class PregelLoop(LoopProtocol):
             CONFIG_KEY_CHECKPOINT_ID not in config[CONF]
             or CONFIG_KEY_DEDUPE_TASKS in config[CONF]
         )
+        self.trigger_to_nodes = trigger_to_nodes
         self.debug = debug
         if self.stream is not None and CONFIG_KEY_STREAM in config[CONF]:
             self.stream = DuplexStream(self.stream, config[CONF][CONFIG_KEY_STREAM])
@@ -264,13 +269,13 @@ class PregelLoop(LoopProtocol):
             self.checkpoint_config = patch_configurable(
                 self.config,
                 {
-                    CONFIG_KEY_CHECKPOINT_ID: config[CONF][CONFIG_KEY_CHECKPOINT_MAP][
-                        self.config[CONF][CONFIG_KEY_CHECKPOINT_NS]
-                    ]
+                    CONFIG_KEY_CHECKPOINT_ID: self.config[CONF][
+                        CONFIG_KEY_CHECKPOINT_MAP
+                    ][self.config[CONF][CONFIG_KEY_CHECKPOINT_NS]]
                 },
             )
         else:
-            self.checkpoint_config = config
+            self.checkpoint_config = self.config
         self.checkpoint_ns = (
             tuple(cast(str, self.config[CONF][CONFIG_KEY_CHECKPOINT_NS]).split(NS_SEP))
             if self.config[CONF].get(CONFIG_KEY_CHECKPOINT_NS)
@@ -347,12 +352,16 @@ class PregelLoop(LoopProtocol):
         ):
             self.to_interrupt.append(task)
             return
+        checkpoint_id_bytes = binascii.unhexlify(self.checkpoint["id"].replace("-", ""))
+        null_version = checkpoint_null_version(self.checkpoint)
         if pushed := cast(
             Optional[PregelExecutableTask],
             prepare_single_task(
                 (PUSH, task.path, write_idx, task.id, call),
                 None,
                 checkpoint=self.checkpoint,
+                checkpoint_id_bytes=checkpoint_id_bytes,
+                checkpoint_null_version=null_version,
                 pending_writes=self.checkpoint_pending_writes,
                 processes=self.nodes,
                 channels=self.channels,
@@ -400,8 +409,10 @@ class PregelLoop(LoopProtocol):
         if self.status != "pending":
             raise RuntimeError("Cannot tick when status is no longer 'pending'")
 
+        updated_channels: set[str] | None = None
+
         if self.input not in (INPUT_DONE, INPUT_RESUMING, INPUT_SHOULD_VALIDATE):
-            self._first(input_keys=input_keys)
+            updated_channels = self._first(input_keys=input_keys)
         elif self.to_interrupt:
             # if we need to interrupt, do so
             self.status = "interrupt_before"
@@ -421,7 +432,7 @@ class PregelLoop(LoopProtocol):
                     ),
                 )
             # all tasks have finished
-            mv_writes = apply_writes(
+            mv_writes, updated_channels = apply_writes(
                 self.checkpoint,
                 self.channels,
                 self.tasks.values(),
@@ -487,6 +498,8 @@ class PregelLoop(LoopProtocol):
             manager=self.manager,
             store=self.store,
             checkpointer=self.checkpointer,
+            trigger_to_nodes=self.trigger_to_nodes,
+            updated_channels=updated_channels,
         )
         self.to_interrupt = []
 
@@ -565,11 +578,11 @@ class PregelLoop(LoopProtocol):
                         self.checkpoint["versions_seen"].get(INTERRUPT, {}).values(),
                         default=None,
                     ):
-                        self.tasks[tid] = task._replace(scheduled=True)
+                        self.tasks[tid] = dataclasses.replace(task, scheduled=True)
                 else:
                     task.writes.append((k, v))
 
-    def _first(self, *, input_keys: Union[str, Sequence[str]]) -> None:
+    def _first(self, *, input_keys: Union[str, Sequence[str]]) -> Optional[set[str]]:
         # resuming from previous checkpoint requires
         # - finding a previous checkpoint
         # - receiving None input (outer graph) or RESUMING flag (subgraph)
@@ -586,16 +599,9 @@ class PregelLoop(LoopProtocol):
                 ),
             )
         )
+        # this can be set only when there are input_writes
+        updated_channels: Optional[set[str]] = None
 
-        # take resume value from parent
-        if scratchpad := cast(
-            Optional[PregelScratchpad], configurable.get(CONFIG_KEY_SCRATCHPAD)
-        ):
-            if (
-                isinstance(scratchpad, PregelScratchpad)
-                and scratchpad.null_resume is not None
-            ):
-                self.put_writes(NULL_TASK_ID, [(RESUME, scratchpad.null_resume)])
         # map command to writes
         if isinstance(self.input, Command):
             if self.input.resume is not None and not self.checkpointer:
@@ -615,7 +621,7 @@ class PregelLoop(LoopProtocol):
         if null_writes := [
             w[1:] for w in self.checkpoint_pending_writes if w[0] == NULL_TASK_ID
         ]:
-            mv_writes = apply_writes(
+            mv_writes, _ = apply_writes(
                 self.checkpoint,
                 self.channels,
                 [PregelTaskWrites((), INPUT, null_writes, [])],
@@ -664,7 +670,7 @@ class PregelLoop(LoopProtocol):
                 manager=None,
             )
             # apply input writes
-            mv_writes = apply_writes(
+            mv_writes, updated_channels = apply_writes(
                 self.checkpoint,
                 self.channels,
                 [
@@ -694,6 +700,7 @@ class PregelLoop(LoopProtocol):
             self.config = patch_configurable(
                 self.config, {CONFIG_KEY_RESUMING: is_resuming}
             )
+        return updated_channels
 
     def _put_checkpoint(self, metadata: CheckpointMetadata) -> None:
         for k, v in self.config["metadata"].items():
@@ -779,7 +786,7 @@ class PregelLoop(LoopProtocol):
                 and self.checkpoint_pending_writes
                 and any(task.writes for task in self.tasks.values())
             ):
-                mv_writes = apply_writes(
+                mv_writes, _ = apply_writes(
                     self.checkpoint,
                     self.channels,
                     self.tasks.values(),
@@ -794,11 +801,14 @@ class PregelLoop(LoopProtocol):
                     [w for t in self.tasks.values() for w in t.writes],
                     self.channels,
                 )
-            # emit INTERRUPT event
-            self._emit(
-                "updates",
-                lambda: iter([{INTERRUPT: cast(GraphInterrupt, exc_value).args[0]}]),
-            )
+            # emit INTERRUPT if exception is empty (otherwise emitted by put_writes)
+            if exc_value is not None and (not exc_value.args or not exc_value.args[0]):
+                self._emit(
+                    "updates",
+                    lambda: iter(
+                        [{INTERRUPT: cast(GraphInterrupt, exc_value).args[0]}]
+                    ),
+                )
             # save final output
             self.output = read_channels(self.channels, self.output_keys)
             # suppress interrupt
@@ -829,7 +839,25 @@ class PregelLoop(LoopProtocol):
                 "tags", EMPTY_SEQ
             ):
                 return
-            if writes[0][0] != ERROR and writes[0][0] != INTERRUPT:
+            if writes[0][0] == INTERRUPT:
+                self._emit(
+                    "updates",
+                    lambda: iter(
+                        [
+                            {
+                                INTERRUPT: tuple(
+                                    v
+                                    for w in writes
+                                    if w[0] == INTERRUPT
+                                    for v in (
+                                        w[1] if isinstance(w[1], Sequence) else (w[1],)
+                                    )
+                                )
+                            }
+                        ]
+                    ),
+                )
+            elif writes[0][0] != ERROR:
                 self._emit(
                     "updates",
                     map_output_updates,
@@ -865,6 +893,7 @@ class SyncPregelLoop(PregelLoop, ContextManager):
         stream_keys: Union[str, Sequence[str]] = EMPTY_SEQ,
         input_model: Optional[Type[BaseModel]] = None,
         debug: bool = False,
+        trigger_to_nodes: Optional[Mapping[str, Sequence[str]]] = None,
     ) -> None:
         super().__init__(
             input,
@@ -881,6 +910,7 @@ class SyncPregelLoop(PregelLoop, ContextManager):
             interrupt_before=interrupt_before,
             manager=manager,
             debug=debug,
+            trigger_to_nodes=trigger_to_nodes,
         )
         self.stack = ExitStack()
         if checkpointer:
@@ -1006,6 +1036,7 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
         stream_keys: Union[str, Sequence[str]] = EMPTY_SEQ,
         input_model: Optional[Type[BaseModel]] = None,
         debug: bool = False,
+        trigger_to_nodes: Optional[Mapping[str, Sequence[str]]] = None,
     ) -> None:
         super().__init__(
             input,
@@ -1022,6 +1053,7 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
             interrupt_before=interrupt_before,
             manager=manager,
             debug=debug,
+            trigger_to_nodes=trigger_to_nodes,
         )
         self.stack = AsyncExitStack()
         if checkpointer:

libs/langgraph/langgraph/pregel/protocol.py

@@ -12,7 +12,7 @@ from langchain_core.runnables import Runnable, RunnableConfig
 from langchain_core.runnables.graph import Graph as DrawableGraph
 from typing_extensions import Self
 
-from langgraph.pregel.types import All, StateSnapshot, StreamMode
+from langgraph.pregel.types import All, StateSnapshot, StateUpdate, StreamMode
 
 
 class PregelProtocol(
@@ -69,6 +69,20 @@ class PregelProtocol(
         limit: Optional[int] = None,
     ) -> AsyncIterator[StateSnapshot]: ...
 
+    @abstractmethod
+    def bulk_update_state(
+        self,
+        config: RunnableConfig,
+        updates: Sequence[Sequence[StateUpdate]],
+    ) -> RunnableConfig: ...
+
+    @abstractmethod
+    async def abulk_update_state(
+        self,
+        config: RunnableConfig,
+        updates: Sequence[Sequence[StateUpdate]],
+    ) -> RunnableConfig: ...
+
     @abstractmethod
     def update_state(
         self,

libs/langgraph/langgraph/pregel/read.py

@@ -62,7 +62,13 @@ class ChannelRead(RunnableCallable):
         mapper: Optional[Callable[[Any], Any]] = None,
         tags: Optional[list[str]] = None,
     ) -> None:
-        super().__init__(func=self._read, afunc=self._aread, tags=tags, name=None)
+        super().__init__(
+            func=self._read,
+            afunc=self._aread,
+            tags=tags,
+            name=None,
+            func_accepts_config=True,
+        )
         self.fresh = fresh
         self.mapper = mapper
         self.channel = channel
@@ -161,6 +167,7 @@ class PregelNode(Runnable):
         metadata: Optional[Mapping[str, Any]] = None,
         bound: Optional[Runnable[Any, Any]] = None,
         retry_policy: Optional[RetryPolicy] = None,
+        subgraphs: Optional[Sequence[PregelProtocol]] = None,
     ) -> None:
         self.channels = channels
         self.triggers = list(triggers)
@@ -170,7 +177,9 @@ class PregelNode(Runnable):
         self.retry_policy = retry_policy
         self.tags = tags
         self.metadata = metadata
-        if self.bound is not DEFAULT_BOUND:
+        if subgraphs is not None:
+            self.subgraphs = subgraphs
+        elif self.bound is not DEFAULT_BOUND:
             try:
                 subgraph = find_subgraph_pregel(self.bound)
             except Exception:
@@ -184,7 +193,6 @@ class PregelNode(Runnable):
 
     def copy(self, update: dict[str, Any]) -> PregelNode:
         attrs = {**self.__dict__, **update}
-        attrs.pop("subgraphs")
         return PregelNode(**attrs)
 
     @cached_property

libs/langgraph/langgraph/pregel/remote.py

@@ -457,6 +457,20 @@ class RemoteGraph(PregelProtocol):
         for state in states:
             yield self._create_state_snapshot(state)
 
+    def bulk_update_state(
+        self,
+        config: RunnableConfig,
+        updates: list[tuple[Optional[dict[str, Any]], Optional[str]]],
+    ) -> RunnableConfig:
+        raise NotImplementedError
+
+    async def abulk_update_state(
+        self,
+        config: RunnableConfig,
+        updates: list[tuple[Optional[dict[str, Any]], Optional[str]]],
+    ) -> RunnableConfig:
+        raise NotImplementedError
+
     def update_state(
         self,
         config: RunnableConfig,

libs/langgraph/langgraph/pregel/runner.py

@@ -2,6 +2,7 @@ import asyncio
 import concurrent.futures
 import threading
 import time
+import weakref
 from functools import partial
 from typing import (
     Any,
@@ -25,12 +26,10 @@ from langgraph.constants import (
     CONF,
     CONFIG_KEY_CALL,
     CONFIG_KEY_SCRATCHPAD,
-    CONFIG_KEY_SEND,
     ERROR,
     INTERRUPT,
     MISSING,
     NO_WRITES,
-    PUSH,
     RESUME,
     RETURN,
     TAG_HIDDEN,
@@ -48,7 +47,9 @@ E = TypeVar("E", threading.Event, asyncio.Event)
 
 class FuturesDict(Generic[F, E], dict[F, Optional[PregelExecutableTask]]):
     event: E
-    callback: Callable[[PregelExecutableTask, Optional[BaseException]], None]
+    callback: weakref.ref[
+        Callable[[PregelExecutableTask, Optional[BaseException]], None]
+    ]
     counter: int
     done: set[F]
     lock: threading.Lock
@@ -56,7 +57,9 @@ class FuturesDict(Generic[F, E], dict[F, Optional[PregelExecutableTask]]):
     def __init__(
         self,
         event: E,
-        callback: Callable[[PregelExecutableTask, Optional[BaseException]], None],
+        callback: weakref.ref[
+            Callable[[PregelExecutableTask, Optional[BaseException]], None]
+        ],
         future_type: Type[F],
         # used for generic typing, newer py supports FutureDict[...](...)
     ) -> None:
@@ -85,7 +88,7 @@ class FuturesDict(Generic[F, E], dict[F, Optional[PregelExecutableTask]]):
         fut: F,
     ) -> None:
         try:
-            self.callback(task, _exception(fut))
+            self.callback()(task, _exception(fut))  # type: ignore[misc]
         finally:
             with self.lock:
                 self.done.add(fut)
@@ -102,10 +105,13 @@ class PregelRunner:
     def __init__(
         self,
         *,
-        submit: Submit,
-        put_writes: Callable[[str, Sequence[tuple[str, Any]]], None],
-        schedule_task: Callable[
-            [PregelExecutableTask, int, Optional[Call]], Optional[PregelExecutableTask]
+        submit: weakref.ref[Submit],
+        put_writes: weakref.ref[Callable[[str, Sequence[tuple[str, Any]]], None]],
+        schedule_task: weakref.ref[
+            Callable[
+                [PregelExecutableTask, int, Optional[Call]],
+                Optional[PregelExecutableTask],
+            ]
         ],
         use_astream: bool = False,
         node_finished: Optional[Callable[[str], None]] = None,
@@ -125,99 +131,9 @@ class PregelRunner:
         retry_policy: Optional[RetryPolicy] = None,
         get_waiter: Optional[Callable[[], concurrent.futures.Future[None]]] = None,
     ) -> Iterator[None]:
-        def writer(
-            task: PregelExecutableTask,
-            writes: Sequence[tuple[str, Any]],
-            *,
-            calls: Optional[Sequence[Call]] = None,
-        ) -> Sequence[Optional[concurrent.futures.Future]]:
-            if all(w[0] != PUSH for w in writes):
-                return task.config[CONF][CONFIG_KEY_SEND](writes)
-
-            # schedule PUSH tasks, collect futures
-            scratchpad: PregelScratchpad = task.config[CONF][CONFIG_KEY_SCRATCHPAD]
-            rtn: dict[int, Optional[concurrent.futures.Future]] = {}
-            for idx, w in enumerate(writes):
-                # bail if not a PUSH write
-                if w[0] != PUSH:
-                    continue
-                # schedule the next task, if the callback returns one
-                wcall = calls[idx] if calls else None
-                if next_task := self.schedule_task(
-                    task, scratchpad.call_counter(), wcall
-                ):
-                    if fut := next(
-                        (
-                            f
-                            for f, t in futures.items()
-                            if t is not None and t == next_task.id
-                        ),
-                        None,
-                    ):
-                        # if the parent task was retried,
-                        # the next task might already be running
-                        rtn[idx] = fut
-                    elif next_task.writes:
-                        # if it already ran, return the result
-                        fut = concurrent.futures.Future()
-                        ret = next(
-                            (v for c, v in next_task.writes if c == RETURN), MISSING
-                        )
-                        if ret is not MISSING:
-                            fut.set_result(ret)
-                        elif exc := next(
-                            (v for c, v in next_task.writes if c == ERROR), None
-                        ):
-                            fut.set_exception(
-                                exc
-                                if isinstance(exc, BaseException)
-                                else Exception(exc)
-                            )
-                        else:
-                            fut.set_result(None)
-                        rtn[idx] = fut
-                    else:
-                        # schedule the next task
-                        fut = self.submit(
-                            run_with_retry,
-                            next_task,
-                            retry_policy,
-                            configurable={
-                                CONFIG_KEY_SEND: partial(writer, next_task),
-                                CONFIG_KEY_CALL: partial(call, next_task),
-                            },
-                            __reraise_on_exit__=reraise,
-                            # starting a new task in the next tick ensures
-                            # updates from this tick are committed/streamed first
-                            __next_tick__=True,
-                        )
-                        futures[fut] = next_task
-                        rtn[idx] = fut
-            return [rtn.get(i) for i in range(len(writes))]
-
-        def call(
-            task: PregelExecutableTask,
-            func: Callable[[Any], Union[Awaitable[Any], Any]],
-            input: Any,
-            *,
-            retry: Optional[RetryPolicy] = None,
-            callbacks: Callbacks = None,
-        ) -> concurrent.futures.Future[Any]:
-            if asyncio.iscoroutinefunction(func):
-                raise RuntimeError("In an sync context async tasks cannot be called")
-            (fut,) = writer(
-                task,
-                [(PUSH, None)],
-                calls=[Call(func, input, retry=retry, callbacks=callbacks)],
-            )
-            assert fut is not None, "writer did not return a future for call"
-            # return a chained future to ensure commit() callback is called
-            # before the returned future is resolved, to ensure stream order etc
-            return chain_future(fut, concurrent.futures.Future())
-
         tasks = tuple(tasks)
         futures = FuturesDict(
-            callback=self.commit,
+            callback=weakref.WeakMethod(self.commit),
             event=threading.Event(),
             future_type=concurrent.futures.Future,
         )
@@ -231,8 +147,15 @@ class PregelRunner:
                     t,
                     retry_policy,
                     configurable={
-                        CONFIG_KEY_SEND: partial(writer, t),
-                        CONFIG_KEY_CALL: partial(call, t),
+                        CONFIG_KEY_CALL: partial(
+                            _call,
+                            weakref.ref(t),
+                            retry=retry_policy,
+                            futures=weakref.ref(futures),
+                            schedule_task=self.schedule_task,
+                            submit=self.submit,
+                            reraise=reraise,
+                        ),
                     },
                 )
                 self.commit(t, None)
@@ -255,13 +178,20 @@ class PregelRunner:
         # schedule tasks
         for t in tasks:
             if not t.writes:
-                fut = self.submit(
+                fut = self.submit()(  # type: ignore[misc]
                     run_with_retry,
                     t,
                     retry_policy,
                     configurable={
-                        CONFIG_KEY_SEND: partial(writer, t),
-                        CONFIG_KEY_CALL: partial(call, t),
+                        CONFIG_KEY_CALL: partial(
+                            _call,
+                            weakref.ref(t),
+                            retry=retry_policy,
+                            futures=weakref.ref(futures),
+                            schedule_task=self.schedule_task,
+                            submit=self.submit,
+                            reraise=reraise,
+                        ),
                     },
                     __reraise_on_exit__=reraise,
                 )
@@ -313,125 +243,10 @@ class PregelRunner:
         retry_policy: Optional[RetryPolicy] = None,
         get_waiter: Optional[Callable[[], asyncio.Future[None]]] = None,
     ) -> AsyncIterator[None]:
-        def writer(
-            task: PregelExecutableTask,
-            writes: Sequence[tuple[str, Any]],
-            *,
-            calls: Optional[Sequence[Call]] = None,
-        ) -> Sequence[Optional[asyncio.Future]]:
-            if all(w[0] != PUSH for w in writes):
-                return task.config[CONF][CONFIG_KEY_SEND](writes)
-
-            # schedule PUSH tasks, collect futures
-            scratchpad: PregelScratchpad = task.config[CONF][CONFIG_KEY_SCRATCHPAD]
-            rtn: dict[int, Optional[asyncio.Future]] = {}
-            for idx, w in enumerate(writes):
-                # bail if not a PUSH write
-                if w[0] != PUSH:
-                    continue
-                # schedule the next task, if the callback returns one
-                wcall = calls[idx] if calls is not None else None
-                if next_task := self.schedule_task(
-                    task, scratchpad.call_counter(), wcall
-                ):
-                    # if the parent task was retried,
-                    # the next task might already be running
-                    if fut := next(
-                        (
-                            f
-                            for f, t in futures.items()
-                            if t is not None and t == next_task.id
-                        ),
-                        None,
-                    ):
-                        # if the parent task was retried,
-                        # the next task might already be running
-                        rtn[idx] = fut
-                    elif next_task.writes:
-                        # if it already ran, return the result
-                        fut = asyncio.Future(loop=loop)
-                        ret = next(
-                            (v for c, v in next_task.writes if c == RETURN), MISSING
-                        )
-                        if ret is not MISSING:
-                            fut.set_result(ret)
-                        elif exc := next(
-                            (v for c, v in next_task.writes if c == ERROR), None
-                        ):
-                            fut.set_exception(
-                                exc
-                                if isinstance(exc, BaseException)
-                                else Exception(exc)
-                            )
-                        else:
-                            fut.set_result(None)
-                        rtn[idx] = fut
-                    else:
-                        # schedule the next task
-                        fut = cast(
-                            asyncio.Future,
-                            self.submit(
-                                arun_with_retry,
-                                next_task,
-                                retry_policy,
-                                stream=self.use_astream,
-                                configurable={
-                                    CONFIG_KEY_SEND: partial(writer, next_task),
-                                    CONFIG_KEY_CALL: partial(call, next_task),
-                                },
-                                __name__=t.name,
-                                __cancel_on_exit__=True,
-                                __reraise_on_exit__=reraise,
-                                # starting a new task in the next tick ensures
-                                # updates from this tick are committed/streamed first
-                                __next_tick__=True,
-                            ),
-                        )
-                        futures[fut] = next_task
-                        rtn[idx] = fut
-            return [rtn.get(i) for i in range(len(writes))]
-
-        def call(
-            task: PregelExecutableTask,
-            func: Callable[[Any], Union[Awaitable[Any], Any]],
-            input: Any,
-            *,
-            retry: Optional[RetryPolicy] = None,
-            callbacks: Callbacks = None,
-        ) -> Union[asyncio.Future[Any], concurrent.futures.Future[Any]]:
-            (fut,) = writer(
-                task,
-                [(PUSH, None)],
-                calls=[Call(func, input, retry=retry, callbacks=callbacks)],
-            )
-            assert fut is not None, "writer did not return a future for call"
-            # return a chained future to ensure commit() callback is called
-            # before the returned future is resolved, to ensure stream order etc
-            try:
-                in_async = asyncio.current_task() is not None
-            except RuntimeError:
-                in_async = False
-            # if in async context return an async future
-            # otherwise return a chained sync future
-            if in_async:
-                if isinstance(fut, asyncio.Task):
-                    sfut: Union[asyncio.Future[Any], concurrent.futures.Future[Any]] = (
-                        asyncio.Future(loop=loop)
-                    )
-                    loop.call_soon_threadsafe(chain_future, fut, sfut)
-                    return sfut
-                else:
-                    # already wrapped in a future
-                    return fut
-            else:
-                sfut = concurrent.futures.Future()
-                loop.call_soon_threadsafe(chain_future, fut, sfut)
-                return sfut
-
         loop = asyncio.get_event_loop()
         tasks = tuple(tasks)
         futures = FuturesDict(
-            callback=self.commit,
+            callback=weakref.WeakMethod(self.commit),
             event=asyncio.Event(),
             future_type=asyncio.Future,
         )
@@ -446,8 +261,17 @@ class PregelRunner:
                     retry_policy,
                     stream=self.use_astream,
                     configurable={
-                        CONFIG_KEY_SEND: partial(writer, t),
-                        CONFIG_KEY_CALL: partial(call, t),
+                        CONFIG_KEY_CALL: partial(
+                            _acall,
+                            weakref.ref(t),
+                            stream=self.use_astream,
+                            retry=retry_policy,
+                            futures=weakref.ref(futures),
+                            schedule_task=self.schedule_task,
+                            submit=self.submit,
+                            reraise=reraise,
+                            loop=loop,
+                        ),
                     },
                 )
                 self.commit(t, None)
@@ -472,14 +296,23 @@ class PregelRunner:
             if not t.writes:
                 fut = cast(
                     asyncio.Future,
-                    self.submit(
+                    self.submit()(  # type: ignore[misc]
                         arun_with_retry,
                         t,
                         retry_policy,
                         stream=self.use_astream,
                         configurable={
-                            CONFIG_KEY_SEND: partial(writer, t),
-                            CONFIG_KEY_CALL: partial(call, t),
+                            CONFIG_KEY_CALL: partial(
+                                _acall,
+                                weakref.ref(t),
+                                retry=retry_policy,
+                                stream=self.use_astream,
+                                futures=weakref.ref(futures),
+                                schedule_task=self.schedule_task,
+                                submit=self.submit,
+                                reraise=reraise,
+                                loop=loop,
+                            ),
                         },
                         __name__=t.name,
                         __cancel_on_exit__=True,
@@ -539,19 +372,20 @@ class PregelRunner:
             # for cancelled tasks, also save error in task,
             # so loop can finish super-step
             task.writes.append((ERROR, exception))
-            self.put_writes(task.id, task.writes)
+            self.put_writes()(task.id, task.writes)  # type: ignore[misc]
         elif exception:
             if isinstance(exception, GraphInterrupt):
                 # save interrupt to checkpointer
-                if interrupts := [(INTERRUPT, i) for i in exception.args[0]]:
+                if exception.args[0]:
+                    writes = [(INTERRUPT, exception.args[0])]
                     if resumes := [w for w in task.writes if w[0] == RESUME]:
-                        interrupts.extend(resumes)
-                    self.put_writes(task.id, interrupts)
+                        writes.extend(resumes)
+                    self.put_writes()(task.id, writes)  # type: ignore[misc]
             elif isinstance(exception, GraphBubbleUp):
                 raise exception
             else:
                 # save error to checkpointer
-                self.put_writes(task.id, [(ERROR, exception)])
+                self.put_writes()(task.id, [(ERROR, exception)])  # type: ignore[misc]
         else:
             if self.node_finished and (
                 task.config is None or TAG_HIDDEN not in task.config.get("tags", [])
@@ -561,7 +395,7 @@ class PregelRunner:
                 # add no writes marker
                 task.writes.append((NO_WRITES, None))
             # save task writes to checkpointer
-            self.put_writes(task.id, task.writes)
+            self.put_writes()(task.id, task.writes)  # type: ignore[misc]
 
 
 def _should_stop_others(
@@ -608,6 +442,7 @@ def _panic_or_proceed(
             done.add(fut)
         else:
             inflight.add(fut)
+    interrupts: list[GraphInterrupt] = []
     while done:
         # if any task failed
         if exc := _exception(done.pop()):
@@ -616,7 +451,14 @@ def _panic_or_proceed(
                 inflight.pop().cancel()
             # raise the exception
             if panic:
-                raise exc
+                if isinstance(exc, GraphInterrupt):
+                    # collect interrupts
+                    interrupts.append(exc)
+                else:
+                    raise exc
+    # raise combined interrupts
+    if interrupts:
+        raise GraphInterrupt(tuple(i for exc in interrupts for i in exc.args[0]))
     if inflight:
         # if we got here means we timed out
         while inflight:
@@ -624,3 +466,193 @@ def _panic_or_proceed(
             inflight.pop().cancel()
         # raise timeout error
         raise timeout_exc_cls("Timed out")
+
+
+def _call(
+    task: weakref.ref[PregelExecutableTask],
+    func: Callable[[Any], Union[Awaitable[Any], Any]],
+    input: Any,
+    *,
+    retry: Optional[RetryPolicy] = None,
+    callbacks: Callbacks = None,
+    futures: weakref.ref[FuturesDict],
+    schedule_task: weakref.ref[
+        Callable[
+            [PregelExecutableTask, int, Optional[Call]], Optional[PregelExecutableTask]
+        ]
+    ],
+    submit: weakref.ref[Submit],
+    reraise: bool,
+) -> concurrent.futures.Future[Any]:
+    if asyncio.iscoroutinefunction(func):
+        raise RuntimeError("In an sync context async tasks cannot be called")
+
+    fut: Optional[concurrent.futures.Future] = None
+    # schedule PUSH tasks, collect futures
+    scratchpad: PregelScratchpad = task().config[CONF][CONFIG_KEY_SCRATCHPAD]  # type: ignore[union-attr]
+    # schedule the next task, if the callback returns one
+    if next_task := schedule_task()(  # type: ignore[misc]
+        task(),  # type: ignore[arg-type]
+        scratchpad.call_counter(),
+        Call(func, input, retry=retry, callbacks=callbacks),
+    ):
+        if fut := next(
+            (
+                f
+                for f, t in futures().items()  # type: ignore[union-attr]
+                if t is not None and t == next_task.id
+            ),
+            None,
+        ):
+            # if the parent task was retried,
+            # the next task might already be running
+            pass
+        elif next_task.writes:
+            # if it already ran, return the result
+            fut = concurrent.futures.Future()
+            ret = next((v for c, v in next_task.writes if c == RETURN), MISSING)
+            if ret is not MISSING:
+                fut.set_result(ret)
+            elif exc := next((v for c, v in next_task.writes if c == ERROR), None):
+                fut.set_exception(
+                    exc if isinstance(exc, BaseException) else Exception(exc)
+                )
+            else:
+                fut.set_result(None)
+        else:
+            # schedule the next task
+            fut = submit()(  # type: ignore[misc]
+                run_with_retry,
+                next_task,
+                retry,
+                configurable={
+                    CONFIG_KEY_CALL: partial(
+                        _call,
+                        weakref.ref(next_task),
+                        futures=futures,
+                        retry=retry,
+                        callbacks=callbacks,
+                        schedule_task=schedule_task,
+                        submit=submit,
+                        reraise=reraise,
+                    ),
+                },
+                __reraise_on_exit__=reraise,
+                # starting a new task in the next tick ensures
+                # updates from this tick are committed/streamed first
+                __next_tick__=True,
+            )
+            futures()[fut] = next_task  # type: ignore[index]
+    fut = cast(Union[asyncio.Future, concurrent.futures.Future], fut)
+    # return a chained future to ensure commit() callback is called
+    # before the returned future is resolved, to ensure stream order etc
+    return chain_future(fut, concurrent.futures.Future())
+
+
+def _acall(
+    task: weakref.ref[PregelExecutableTask],
+    func: Callable[[Any], Union[Awaitable[Any], Any]],
+    input: Any,
+    *,
+    retry: Optional[RetryPolicy] = None,
+    callbacks: Callbacks = None,
+    # injected dependencies
+    futures: weakref.ref[FuturesDict],
+    schedule_task: weakref.ref[
+        Callable[
+            [PregelExecutableTask, int, Optional[Call]], Optional[PregelExecutableTask]
+        ]
+    ],
+    submit: weakref.ref[Submit],
+    loop: asyncio.AbstractEventLoop,
+    reraise: bool = False,
+    stream: bool = False,
+) -> Union[asyncio.Future[Any], concurrent.futures.Future[Any]]:
+    fut: Optional[asyncio.Future] = None
+    # schedule PUSH tasks, collect futures
+    scratchpad: PregelScratchpad = task().config[CONF][CONFIG_KEY_SCRATCHPAD]  # type: ignore[union-attr]
+    # schedule the next task, if the callback returns one
+    if next_task := schedule_task()(  # type: ignore[misc]
+        task(),  # type: ignore[arg-type]
+        scratchpad.call_counter(),
+        Call(func, input, retry=retry, callbacks=callbacks),
+    ):
+        if fut := next(
+            (
+                f
+                for f, t in futures().items()  # type: ignore[union-attr]
+                if t is not None and t == next_task.id
+            ),
+            None,
+        ):
+            # if the parent task was retried,
+            # the next task might already be running
+
+            pass
+        elif next_task.writes:
+            # if it already ran, return the result
+            fut = asyncio.Future(loop=loop)
+            ret = next((v for c, v in next_task.writes if c == RETURN), MISSING)
+            if ret is not MISSING:
+                fut.set_result(ret)
+            elif exc := next((v for c, v in next_task.writes if c == ERROR), None):
+                fut.set_exception(
+                    exc if isinstance(exc, BaseException) else Exception(exc)
+                )
+            else:
+                fut.set_result(None)
+            futures()[fut] = next_task  # type: ignore[index]
+        else:
+            # schedule the next task
+            fut = cast(
+                asyncio.Future,
+                submit()(  # type: ignore[misc]
+                    arun_with_retry,
+                    next_task,
+                    retry,
+                    stream=stream,
+                    configurable={
+                        CONFIG_KEY_CALL: partial(
+                            _acall,
+                            weakref.ref(next_task),
+                            stream=stream,
+                            futures=futures,
+                            schedule_task=schedule_task,
+                            submit=submit,
+                            loop=loop,
+                            reraise=reraise,
+                        ),
+                    },
+                    __name__=task().name,  # type: ignore[union-attr]
+                    __cancel_on_exit__=True,
+                    __reraise_on_exit__=reraise,
+                    # starting a new task in the next tick ensures
+                    # updates from this tick are committed/streamed first
+                    __next_tick__=True,
+                ),
+            )
+            futures()[fut] = next_task  # type: ignore[index]
+
+    fut = cast(Union[asyncio.Future, concurrent.futures.Future], fut)
+    # return a chained future to ensure commit() callback is called
+    # before the returned future is resolved, to ensure stream order etc
+    try:
+        in_async = asyncio.current_task() is not None
+    except RuntimeError:
+        in_async = False
+    # if in async context return an async future
+    # otherwise return a chained sync future
+    if in_async:
+        if isinstance(fut, asyncio.Task):
+            sfut: Union[asyncio.Future[Any], concurrent.futures.Future[Any]] = (
+                asyncio.Future(loop=loop)
+            )
+            loop.call_soon_threadsafe(chain_future, fut, sfut)
+            return sfut
+        else:
+            # already wrapped in a future
+            return fut
+    else:
+        sfut = concurrent.futures.Future()
+        loop.call_soon_threadsafe(chain_future, fut, sfut)
+        return sfut

libs/langgraph/langgraph/pregel/types.py

@@ -7,6 +7,7 @@ from langgraph.types import (
     PregelTask,
     RetryPolicy,
     StateSnapshot,
+    StateUpdate,
     StreamMode,
     StreamWriter,
     default_retry_on,
@@ -14,6 +15,7 @@ from langgraph.types import (
 
 __all__ = [
     "All",
+    "StateUpdate",
     "CachePolicy",
     "PregelExecutableTask",
     "PregelTask",

libs/langgraph/langgraph/pregel/utils.py

@@ -1,7 +1,10 @@
-from typing import Optional
+import ast
+import inspect
+import textwrap
+from typing import Any, Callable, Optional
 
 from langchain_core.runnables import RunnableLambda, RunnableSequence
-from langchain_core.runnables.utils import get_function_nonlocals
+from typing_extensions import override
 
 from langgraph.checkpoint.base import ChannelVersions
 from langgraph.pregel.protocol import PregelProtocol
@@ -55,3 +58,152 @@ def find_subgraph_pregel(candidate: Runnable) -> Optional[PregelProtocol]:
                 )
 
     return None
+
+
+def get_function_nonlocals(func: Callable) -> list[Any]:
+    """Get the nonlocal variables accessed by a function.
+
+    Args:
+        func: The function to check.
+
+    Returns:
+        List[Any]: The nonlocal variables accessed by the function.
+    """
+    try:
+        code = inspect.getsource(func)
+        tree = ast.parse(textwrap.dedent(code))
+        visitor = FunctionNonLocals()
+        visitor.visit(tree)
+        values: list[Any] = []
+        closure = (
+            inspect.getclosurevars(func.__wrapped__)
+            if hasattr(func, "__wrapped__") and callable(func.__wrapped__)
+            else inspect.getclosurevars(func)
+        )
+        candidates = {**closure.globals, **closure.nonlocals}
+        for k, v in candidates.items():
+            if k in visitor.nonlocals:
+                values.append(v)
+            for kk in visitor.nonlocals:
+                if "." in kk and kk.startswith(k):
+                    vv = v
+                    for part in kk.split(".")[1:]:
+                        if vv is None:
+                            break
+                        else:
+                            try:
+                                vv = getattr(vv, part)
+                            except AttributeError:
+                                break
+                    else:
+                        values.append(vv)
+    except (SyntaxError, TypeError, OSError, SystemError):
+        return []
+
+    return values
+
+
+class FunctionNonLocals(ast.NodeVisitor):
+    """Get the nonlocal variables accessed of a function."""
+
+    def __init__(self) -> None:
+        self.nonlocals: set[str] = set()
+
+    @override
+    def visit_FunctionDef(self, node: ast.FunctionDef) -> Any:
+        """Visit a function definition.
+
+        Args:
+            node: The node to visit.
+
+        Returns:
+            Any: The result of the visit.
+        """
+        visitor = NonLocals()
+        visitor.visit(node)
+        self.nonlocals.update(visitor.loads - visitor.stores)
+
+    @override
+    def visit_AsyncFunctionDef(self, node: ast.AsyncFunctionDef) -> Any:
+        """Visit an async function definition.
+
+        Args:
+            node: The node to visit.
+
+        Returns:
+            Any: The result of the visit.
+        """
+        visitor = NonLocals()
+        visitor.visit(node)
+        self.nonlocals.update(visitor.loads - visitor.stores)
+
+    @override
+    def visit_Lambda(self, node: ast.Lambda) -> Any:
+        """Visit a lambda function.
+
+        Args:
+            node: The node to visit.
+
+        Returns:
+            Any: The result of the visit.
+        """
+        visitor = NonLocals()
+        visitor.visit(node)
+        self.nonlocals.update(visitor.loads - visitor.stores)
+
+
+class NonLocals(ast.NodeVisitor):
+    """Get nonlocal variables accessed."""
+
+    def __init__(self) -> None:
+        self.loads: set[str] = set()
+        self.stores: set[str] = set()
+
+    @override
+    def visit_Name(self, node: ast.Name) -> Any:
+        """Visit a name node.
+
+        Args:
+            node: The node to visit.
+
+        Returns:
+            Any: The result of the visit.
+        """
+        if isinstance(node.ctx, ast.Load):
+            self.loads.add(node.id)
+        elif isinstance(node.ctx, ast.Store):
+            self.stores.add(node.id)
+
+    @override
+    def visit_Attribute(self, node: ast.Attribute) -> Any:
+        """Visit an attribute node.
+
+        Args:
+            node: The node to visit.
+
+        Returns:
+            Any: The result of the visit.
+        """
+        if isinstance(node.ctx, ast.Load):
+            parent = node.value
+            attr_expr = node.attr
+            while isinstance(parent, ast.Attribute):
+                attr_expr = parent.attr + "." + attr_expr
+                parent = parent.value
+            if isinstance(parent, ast.Name):
+                self.loads.add(parent.id + "." + attr_expr)
+                self.loads.discard(parent.id)
+            elif isinstance(parent, ast.Call):
+                if isinstance(parent.func, ast.Name):
+                    self.loads.add(parent.func.id)
+                else:
+                    parent = parent.func
+                    attr_expr = ""
+                    while isinstance(parent, ast.Attribute):
+                        if attr_expr:
+                            attr_expr = parent.attr + "." + attr_expr
+                        else:
+                            attr_expr = parent.attr
+                        parent = parent.value
+                    if isinstance(parent, ast.Name):
+                        self.loads.add(parent.id + "." + attr_expr)

libs/langgraph/langgraph/pregel/write.py

@@ -57,7 +57,13 @@ class ChannelWrite(RunnableCallable):
         tags: Optional[Sequence[str]] = None,
         require_at_least_one_of: Optional[Sequence[str]] = None,  # ignored
     ):
-        super().__init__(func=self._write, afunc=self._awrite, name=None, tags=tags)
+        super().__init__(
+            func=self._write,
+            afunc=self._awrite,
+            name=None,
+            tags=tags,
+            func_accepts_config=True,
+        )
         self.writes = cast(
             list[Union[ChannelWriteEntry, ChannelWriteTupleEntry, Send]], writes
         )

libs/langgraph/langgraph/types.py

@@ -130,7 +130,12 @@ class Interrupt:
     value: Any
     resumable: bool = False
     ns: Optional[Sequence[str]] = None
-    when: Literal["during"] = "during"
+    when: Literal["during"] = dataclasses.field(default="during", repr=False)
+
+
+class StateUpdate(NamedTuple):
+    values: Optional[dict[str, Any]]
+    as_node: Optional[str] = None
 
 
 class PregelTask(NamedTuple):
@@ -143,13 +148,20 @@ class PregelTask(NamedTuple):
     result: Optional[Any] = None
 
 
-class PregelExecutableTask(NamedTuple):
+if sys.version_info > (3, 11):
+    _T_DC_KWARGS = {"weakref_slot": True, "slots": True, "frozen": True}
+else:
+    _T_DC_KWARGS = {"frozen": True}
+
+
+@dataclasses.dataclass(**_T_DC_KWARGS)
+class PregelExecutableTask:
     name: str
     input: Any
     proc: Runnable
     writes: deque[tuple[str, Any]]
     config: RunnableConfig
-    triggers: list[str]
+    triggers: Sequence[str]
     retry_policy: Optional[RetryPolicy]
     cache_policy: Optional[CachePolicy]
     id: str
@@ -351,20 +363,11 @@ class PregelScratchpad:
     call_counter: Callable[[], int]
     # interrupt
     interrupt_counter: Callable[[], int]
+    get_null_resume: Callable[[bool], Any]
     resume: list[Any]
-    null_resume: Optional[Any]
-    _consume_null_resume: Callable[[], None]
     # subgraph
     subgraph_counter: Callable[[], int]
 
-    def consume_null_resume(self) -> Any:
-        if self.null_resume is not None:
-            value = self.null_resume
-            self._consume_null_resume()
-            self.null_resume = None
-            return value
-        raise ValueError("No null resume to consume")
-
 
 def interrupt(value: Any) -> Any:
     """Interrupt the graph with a resumable exception from within a node.
@@ -480,9 +483,9 @@ def interrupt(value: Any) -> Any:
         if idx < len(scratchpad.resume):
             return scratchpad.resume[idx]
     # find current resume value
-    if scratchpad.null_resume is not None:
+    v = scratchpad.get_null_resume(True)
+    if v is not None:
         assert len(scratchpad.resume) == idx, (scratchpad.resume, idx)
-        v = scratchpad.consume_null_resume()
         scratchpad.resume.append(v)
         conf[CONFIG_KEY_SEND]([(RESUME, scratchpad.resume)])
         return v

libs/langgraph/langgraph/utils/runnable.py

@@ -2,8 +2,8 @@ import asyncio
 import enum
 import inspect
 import sys
-from contextlib import AsyncExitStack
-from contextvars import copy_context
+from contextlib import AsyncExitStack, contextmanager
+from contextvars import Context, Token, copy_context
 from functools import partial, wraps
 from typing import (
     Any,
@@ -11,6 +11,7 @@ from typing import (
     Awaitable,
     Callable,
     Coroutine,
+    Generator,
     Iterator,
     Optional,
     Protocol,
@@ -53,13 +54,69 @@ from langgraph.utils.config import (
     patch_config,
 )
 
-try:
-    from langchain_core.runnables.config import _set_config_context
-except ImportError:
-    # For forwards compatibility
-    def _set_config_context(context: RunnableConfig) -> None:  # type: ignore
-        """Set the context for the current thread."""
-        var_child_runnable_config.set(context)
+
+def _set_config_context(
+    config: RunnableConfig,
+) -> tuple[Token[Optional[RunnableConfig]], Optional[dict[str, Any]]]:
+    """Set the child Runnable config + tracing context.
+
+    Args:
+        config (RunnableConfig): The config to set.
+    """
+    from langchain_core.tracers.langchain import LangChainTracer
+
+    config_token = var_child_runnable_config.set(config)
+    current_context = None
+    if (
+        (callbacks := config.get("callbacks"))
+        and (
+            parent_run_id := getattr(callbacks, "parent_run_id", None)
+        )  # Is callback manager
+        and (
+            tracer := next(
+                (
+                    handler
+                    for handler in getattr(callbacks, "handlers", [])
+                    if isinstance(handler, LangChainTracer)
+                ),
+                None,
+            )
+        )
+        and (run := tracer.run_map.get(str(parent_run_id)))
+    ):
+        from langsmith.run_helpers import _set_tracing_context, get_tracing_context
+
+        current_context = get_tracing_context()
+        _set_tracing_context({"parent": run})
+    return config_token, current_context
+
+
+@contextmanager
+def set_config_context(config: RunnableConfig) -> Generator[Context, None, None]:
+    """Set the child Runnable config + tracing context.
+
+    Args:
+        config (RunnableConfig): The config to set.
+    """
+    from langsmith.run_helpers import _set_tracing_context
+
+    ctx = copy_context()
+    config_token, _ = ctx.run(_set_config_context, config)
+    try:
+        yield ctx
+    finally:
+        ctx.run(var_child_runnable_config.reset, config_token)
+        ctx.run(
+            _set_tracing_context,
+            {
+                "parent": None,
+                "project_name": None,
+                "tags": None,
+                "metadata": None,
+                "enabled": None,
+                "client": None,
+            },
+        )
 
 
 # Before Python 3.11 native StrEnum is not available
@@ -194,6 +251,7 @@ class RunnableCallable(Runnable):
         trace: bool = True,
         recurse: bool = True,
         explode_args: bool = False,
+        func_accepts_config: Optional[bool] = None,
         **kwargs: Any,
     ) -> None:
         self.name = name
@@ -219,27 +277,32 @@ class RunnableCallable(Runnable):
         # check signature
         if func is None and afunc is None:
             raise ValueError("At least one of func or afunc must be provided.")
-        params = inspect.signature(cast(Callable, func or afunc)).parameters
 
-        self.func_accepts_config = "config" in params
-        # Mapping from kwarg name to (config key, default value) to be used.
-        # The default value is used if the config key is not found in the config.
-        self.func_accepts: dict[str, Tuple[str, Any]] = {}
+        if func_accepts_config is not None:
+            self.func_accepts_config = func_accepts_config
+            self.func_accepts: dict[str, Tuple[str, Any]] = {}
+        else:
+            params = inspect.signature(cast(Callable, func or afunc)).parameters
 
-        for kw, typ, config_key, default in KWARGS_CONFIG_KEYS:
-            p = params.get(kw)
+            self.func_accepts_config = "config" in params
+            # Mapping from kwarg name to (config key, default value) to be used.
+            # The default value is used if the config key is not found in the config.
+            self.func_accepts = {}
 
-            if p is None or p.kind not in VALID_KINDS:
-                # If parameter is not found or is not a valid kind, skip
-                continue
+            for kw, typ, config_key, default in KWARGS_CONFIG_KEYS:
+                p = params.get(kw)
 
-            if typ != (ANY_TYPE,) and p.annotation not in typ:
-                # A specific type is required, but the function annotation does
-                # not match the expected type.
-                continue
+                if p is None or p.kind not in VALID_KINDS:
+                    # If parameter is not found or is not a valid kind, skip
+                    continue
 
-            # If the kwarg is accepted by the function, store the default value
-            self.func_accepts[kw] = (config_key, default)
+                if typ != (ANY_TYPE,) and p.annotation not in typ:
+                    # A specific type is required, but the function annotation does
+                    # not match the expected type.
+                    continue
+
+                # If the kwarg is accepted by the function, store the default value
+                self.func_accepts[kw] = (config_key, default)
 
     def __repr__(self) -> str:
         repr_args = {
@@ -286,7 +349,6 @@ class RunnableCallable(Runnable):
 
             kwargs[kw] = _conf.get(config_key, default_value)
 
-        context = copy_context()
         if self.trace:
             callback_manager = get_callback_manager_for_config(config, self.tags)
             run_manager = callback_manager.on_chain_start(
@@ -297,17 +359,16 @@ class RunnableCallable(Runnable):
             )
             try:
                 child_config = patch_config(config, callbacks=run_manager.get_child())
-                context = copy_context()
-                context.run(_set_config_context, child_config)
-                ret = context.run(self.func, *args, **kwargs)
+                with set_config_context(child_config) as context:
+                    ret = context.run(self.func, *args, **kwargs)
             except BaseException as e:
                 run_manager.on_chain_error(e)
                 raise
             else:
                 run_manager.on_chain_end(ret)
         else:
-            context.run(_set_config_context, config)
-            ret = context.run(self.func, *args, **kwargs)
+            with set_config_context(config) as context:
+                ret = context.run(self.func, *args, **kwargs)
         if isinstance(ret, Runnable) and self.recurse:
             return ret.invoke(input, config)
         return ret
@@ -342,7 +403,6 @@ class RunnableCallable(Runnable):
                     f"Missing required config key '{config_key}' for '{self.name}'."
                 )
             kwargs[kw] = _conf.get(config_key, default_value)
-        context = copy_context()
         if self.trace:
             callback_manager = get_async_callback_manager_for_config(config, self.tags)
             run_manager = await callback_manager.on_chain_start(
@@ -353,24 +413,24 @@ class RunnableCallable(Runnable):
             )
             try:
                 child_config = patch_config(config, callbacks=run_manager.get_child())
-                context.run(_set_config_context, child_config)
-                coro = cast(Coroutine[None, None, Any], self.afunc(*args, **kwargs))
-                if ASYNCIO_ACCEPTS_CONTEXT:
-                    ret = await asyncio.create_task(coro, context=context)
-                else:
-                    ret = await coro
+                with set_config_context(child_config) as context:
+                    coro = cast(Coroutine[None, None, Any], self.afunc(*args, **kwargs))
+                    if ASYNCIO_ACCEPTS_CONTEXT:
+                        ret = await asyncio.create_task(coro, context=context)
+                    else:
+                        ret = await coro
             except BaseException as e:
                 await run_manager.on_chain_error(e)
                 raise
             else:
                 await run_manager.on_chain_end(ret)
         else:
-            context.run(_set_config_context, config)
-            if ASYNCIO_ACCEPTS_CONTEXT:
-                coro = cast(Coroutine[None, None, Any], self.afunc(*args, **kwargs))
-                ret = await asyncio.create_task(coro, context=context)
-            else:
-                ret = await self.afunc(*args, **kwargs)
+            with set_config_context(config) as context:
+                if ASYNCIO_ACCEPTS_CONTEXT:
+                    coro = cast(Coroutine[None, None, Any], self.afunc(*args, **kwargs))
+                    ret = await asyncio.create_task(coro, context=context)
+                else:
+                    ret = await self.afunc(*args, **kwargs)
         if isinstance(ret, Runnable) and self.recurse:
             return await ret.ainvoke(input, config)
         return ret

libs/langgraph/poetry.lock

@@ -1324,14 +1324,14 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.44"
+version = "0.3.46"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main", "dev"]
 files = [
-    {file = "langchain_core-0.3.44-py3-none-any.whl", hash = "sha256:d989ce8bd62f1d07765acd575e6ec1254aec0cf7775aaea39fe4af8102377459"},
-    {file = "langchain_core-0.3.44.tar.gz", hash = "sha256:7c0a01e78360f007cbca448178fe7e032404068e6431dbe8ce905f84febbdfa5"},
+    {file = "langchain_core-0.3.46-py3-none-any.whl", hash = "sha256:28b5689fc347975ea520b5364ab4aee5567e661553bbee5e97cabf4596c28ce0"},
+    {file = "langchain_core-0.3.46.tar.gz", hash = "sha256:5fca010eeb0a427be5aa8a8525e2112995dde790c584cef165be7c5e0ee1c2b5"},
 ]
 
 [package.dependencies]
@@ -1348,7 +1348,7 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.18"
+version = "2.0.21"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -1366,7 +1366,7 @@ url = "../checkpoint"
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.16"
+version = "2.0.19"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -1375,7 +1375,7 @@ files = []
 develop = true
 
 [package.dependencies]
-langgraph-checkpoint = "^2.0.15"
+langgraph-checkpoint = "^2.0.21"
 orjson = ">=3.10.1"
 psycopg = "^3.2.0"
 psycopg-pool = "^3.2.0"
@@ -1404,7 +1404,7 @@ url = "../checkpoint-sqlite"
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.1.2"
+version = "0.1.4"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -1422,7 +1422,7 @@ url = "../prebuilt"
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.55"
+version = "0.1.58"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -2238,6 +2238,48 @@ files = [
     {file = "pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6"},
 ]
 
+[[package]]
+name = "pycryptodome"
+version = "3.21.0"
+description = "Cryptographic library for Python"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+groups = ["dev"]
+files = [
+    {file = "pycryptodome-3.21.0-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:dad9bf36eda068e89059d1f07408e397856be9511d7113ea4b586642a429a4fd"},
+    {file = "pycryptodome-3.21.0-cp27-cp27m-manylinux2010_i686.whl", hash = "sha256:a1752eca64c60852f38bb29e2c86fca30d7672c024128ef5d70cc15868fa10f4"},
+    {file = "pycryptodome-3.21.0-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:3ba4cc304eac4d4d458f508d4955a88ba25026890e8abff9b60404f76a62c55e"},
+    {file = "pycryptodome-3.21.0-cp27-cp27m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7cb087b8612c8a1a14cf37dd754685be9a8d9869bed2ffaaceb04850a8aeef7e"},
+    {file = "pycryptodome-3.21.0-cp27-cp27m-musllinux_1_1_aarch64.whl", hash = "sha256:26412b21df30b2861424a6c6d5b1d8ca8107612a4cfa4d0183e71c5d200fb34a"},
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+    {file = "pycryptodome-3.21.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f35e442630bc4bc2e1878482d6f59ea22e280d7121d7adeaedba58c23ab6386b"},
+    {file = "pycryptodome-3.21.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ff99f952db3db2fbe98a0b355175f93ec334ba3d01bbde25ad3a5a33abc02b58"},
+    {file = "pycryptodome-3.21.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:8acd7d34af70ee63f9a849f957558e49a98f8f1634f86a59d2be62bb8e93f71c"},
+    {file = "pycryptodome-3.21.0.tar.gz", hash = "sha256:f7787e0d469bdae763b876174cf2e6c0f7be79808af26b1da96f1a64bcf47297"},
+]
+
 [[package]]
 name = "pydantic"
 version = "2.9.2"
@@ -3485,6 +3527,139 @@ files = [
     {file = "widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6"},
 ]
 
+[[package]]
+name = "xxhash"
+version = "3.5.0"
+description = "Python binding for xxHash"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "xxhash-3.5.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ece616532c499ee9afbb83078b1b952beffef121d989841f7f4b3dc5ac0fd212"},
+    {file = "xxhash-3.5.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3171f693dbc2cef6477054a665dc255d996646b4023fe56cb4db80e26f4cc520"},
+    {file = "xxhash-3.5.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7c5d3e570ef46adaf93fc81b44aca6002b5a4d8ca11bd0580c07eac537f36680"},
+    {file = "xxhash-3.5.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:7cb29a034301e2982df8b1fe6328a84f4b676106a13e9135a0d7e0c3e9f806da"},
+    {file = "xxhash-3.5.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5d0d307d27099bb0cbeea7260eb39ed4fdb99c5542e21e94bb6fd29e49c57a23"},
+    {file = "xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c0342aafd421795d740e514bc9858ebddfc705a75a8c5046ac56d85fe97bf196"},
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+    {file = "xxhash-3.5.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:4cc2d67fdb4d057730c75a64c5923abfa17775ae234a71b0200346bfb0a7f482"},
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+    {file = "xxhash-3.5.0-cp310-cp310-win_amd64.whl", hash = "sha256:9bed5144c6923cc902cd14bb8963f2d5e034def4486ab0bbe1f58f03f042f9a9"},
+    {file = "xxhash-3.5.0-cp310-cp310-win_arm64.whl", hash = "sha256:893074d651cf25c1cc14e3bea4fceefd67f2921b1bb8e40fcfeba56820de80c6"},
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+    {file = "xxhash-3.5.0-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:13de2b76c1835399b2e419a296d5b38dc4855385d9e96916299170085ef72f57"},
+    {file = "xxhash-3.5.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:0691bfcc4f9c656bcb96cc5db94b4d75980b9d5589f2e59de790091028580837"},
+    {file = "xxhash-3.5.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:297595fe6138d4da2c8ce9e72a04d73e58725bb60f3a19048bc96ab2ff31c692"},
+    {file = "xxhash-3.5.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cc1276d369452040cbb943300dc8abeedab14245ea44056a2943183822513a18"},
+    {file = "xxhash-3.5.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2061188a1ba352fc699c82bff722f4baacb4b4b8b2f0c745d2001e56d0dfb514"},
+    {file = "xxhash-3.5.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:38c384c434021e4f62b8d9ba0bc9467e14d394893077e2c66d826243025e1f81"},
+    {file = "xxhash-3.5.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:e6a4dd644d72ab316b580a1c120b375890e4c52ec392d4aef3c63361ec4d77d1"},
+    {file = "xxhash-3.5.0-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:531af8845aaadcadf951b7e0c1345c6b9c68a990eeb74ff9acd8501a0ad6a1c9"},
+    {file = "xxhash-3.5.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ce379bcaa9fcc00f19affa7773084dd09f5b59947b3fb47a1ceb0179f91aaa1"},
+    {file = "xxhash-3.5.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd1b2281d01723f076df3c8188f43f2472248a6b63118b036e641243656b1b0f"},
+    {file = "xxhash-3.5.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9c770750cc80e8694492244bca7251385188bc5597b6a39d98a9f30e8da984e0"},
+    {file = "xxhash-3.5.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:b150b8467852e1bd844387459aa6fbe11d7f38b56e901f9f3b3e6aba0d660240"},
+    {file = "xxhash-3.5.0.tar.gz", hash = "sha256:84f2caddf951c9cbf8dc2e22a89d4ccf5d86391ac6418fe81e3c67d0cf60b45f"},
+]
+
 [[package]]
 name = "zipp"
 version = "3.20.2"
@@ -3509,4 +3684,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9.0,<4.0"
-content-hash = "eb85f0bcc0e8a715ef38afb58cf888f7c2ee8579ea6ed94900244365f24cddd9"
+content-hash = "b03760d1062e13e4df0b4052a194bedb8abb3baf80da0c036b39d2ebe26b0b5c"

libs/langgraph/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.3.10"
+version = "0.3.19"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"
@@ -13,6 +13,7 @@ langchain-core = ">=0.1,<0.4"
 langgraph-checkpoint = "^2.0.10"
 langgraph-sdk = "^0.1.42"
 langgraph-prebuilt = ">=0.1.1,<0.2"
+xxhash = "^3.5.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.3.2"
@@ -37,6 +38,7 @@ uvloop = "0.21.0beta1"
 pyperf = "^2.7.0"
 py-spy = "^0.3.14"
 types-requests = "^2.32.0.20240914"
+pycryptodome = "^3.21.0"
 
 [tool.ruff]
 lint.select = [ "E", "F", "I", "TID251" ]

libs/langgraph/tests/__snapshots__/test_pregel.ambr

@@ -377,6 +377,19 @@
   
   '''
 # ---
+# name: test_in_one_fan_out_state_graph_waiting_edge[sqlite_aes]
+  '''
+  graph TD;
+  	__start__ --> rewrite_query;
+  	analyzer_one --> retriever_one;
+  	qa --> __end__;
+  	retriever_one --> qa;
+  	retriever_two --> qa;
+  	rewrite_query --> analyzer_one;
+  	rewrite_query --> retriever_two;
+  
+  '''
+# ---
 # name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1[memory]
   '''
   graph TD;
@@ -797,6 +810,76 @@
     'type': 'object',
   })
 # ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1[sqlite_aes]
+  '''
+  graph TD;
+  	__start__ --> rewrite_query;
+  	analyzer_one --> retriever_one;
+  	qa --> __end__;
+  	retriever_one --> qa;
+  	retriever_two --> qa;
+  	rewrite_query --> analyzer_one;
+  	rewrite_query -.-> retriever_two;
+  
+  '''
+# ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1[sqlite_aes].1
+  dict({
+    'definitions': dict({
+      'InnerObject': dict({
+        'properties': dict({
+          'yo': dict({
+            'title': 'Yo',
+            'type': 'integer',
+          }),
+        }),
+        'required': list([
+          'yo',
+        ]),
+        'title': 'InnerObject',
+        'type': 'object',
+      }),
+    }),
+    'properties': dict({
+      'inner': dict({
+        '$ref': '#/definitions/InnerObject',
+      }),
+      'query': dict({
+        'title': 'Query',
+        'type': 'string',
+      }),
+    }),
+    'required': list([
+      'query',
+      'inner',
+    ]),
+    'title': 'Input',
+    'type': 'object',
+  })
+# ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1[sqlite_aes].2
+  dict({
+    'properties': dict({
+      'answer': dict({
+        'title': 'Answer',
+        'type': 'string',
+      }),
+      'docs': dict({
+        'items': dict({
+          'type': 'string',
+        }),
+        'title': 'Docs',
+        'type': 'array',
+      }),
+    }),
+    'required': list([
+      'answer',
+      'docs',
+    ]),
+    'title': 'Output',
+    'type': 'object',
+  })
+# ---
 # name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2[memory]
   '''
   graph TD;
@@ -1217,6 +1300,76 @@
     'type': 'object',
   })
 # ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2[sqlite_aes]
+  '''
+  graph TD;
+  	__start__ --> rewrite_query;
+  	analyzer_one --> retriever_one;
+  	qa --> __end__;
+  	retriever_one --> qa;
+  	retriever_two --> qa;
+  	rewrite_query --> analyzer_one;
+  	rewrite_query -.-> retriever_two;
+  
+  '''
+# ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2[sqlite_aes].1
+  dict({
+    '$defs': dict({
+      'InnerObject': dict({
+        'properties': dict({
+          'yo': dict({
+            'title': 'Yo',
+            'type': 'integer',
+          }),
+        }),
+        'required': list([
+          'yo',
+        ]),
+        'title': 'InnerObject',
+        'type': 'object',
+      }),
+    }),
+    'properties': dict({
+      'inner': dict({
+        '$ref': '#/$defs/InnerObject',
+      }),
+      'query': dict({
+        'title': 'Query',
+        'type': 'string',
+      }),
+    }),
+    'required': list([
+      'query',
+      'inner',
+    ]),
+    'title': 'Input',
+    'type': 'object',
+  })
+# ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2[sqlite_aes].2
+  dict({
+    'properties': dict({
+      'answer': dict({
+        'title': 'Answer',
+        'type': 'string',
+      }),
+      'docs': dict({
+        'items': dict({
+          'type': 'string',
+        }),
+        'title': 'Docs',
+        'type': 'array',
+      }),
+    }),
+    'required': list([
+      'answer',
+      'docs',
+    ]),
+    'title': 'Output',
+    'type': 'object',
+  })
+# ---
 # name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[memory]
   '''
   graph TD;
@@ -1715,6 +1868,19 @@
   
   '''
 # ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_via_branch[sqlite_aes]
+  '''
+  graph TD;
+  	__start__ --> rewrite_query;
+  	analyzer_one --> retriever_one;
+  	qa --> __end__;
+  	retriever_one --> qa;
+  	retriever_two --> qa;
+  	rewrite_query --> analyzer_one;
+  	rewrite_query -.-> retriever_two;
+  
+  '''
+# ---
 # name: test_multiple_sinks_subgraphs
   '''
   %%{init: {'flowchart': {'curve': 'linear'}}}%%

libs/langgraph/tests/conftest.py

@@ -16,6 +16,7 @@ from langgraph.checkpoint.postgres.aio import (
     AsyncPostgresSaver,
     AsyncShallowPostgresSaver,
 )
+from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
 from langgraph.checkpoint.sqlite import SqliteSaver
 from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
 from langgraph.store.base import BaseStore
@@ -61,6 +62,15 @@ def checkpointer_sqlite():
         yield checkpointer
 
 
+@pytest.fixture(scope="function")
+def checkpointer_sqlite_aes():
+    with SqliteSaver.from_conn_string(":memory:") as checkpointer:
+        checkpointer.serde = EncryptedSerializer.from_pycryptodome_aes(
+            key=b"1234567890123456"
+        )
+        yield checkpointer
+
+
 @asynccontextmanager
 async def _checkpointer_sqlite_aio():
     async with AsyncSqliteSaver.from_conn_string(":memory:") as checkpointer:
@@ -437,6 +447,7 @@ REGULAR_CHECKPOINTERS_SYNC = [
     "postgres",
     "postgres_pipe",
     "postgres_pool",
+    "sqlite_aes",
 ]
 ALL_CHECKPOINTERS_SYNC = [
     *REGULAR_CHECKPOINTERS_SYNC,

libs/langgraph/tests/memory_assert.py

@@ -1,4 +1,3 @@
-import asyncio
 import os
 import tempfile
 from collections import defaultdict
@@ -13,7 +12,6 @@ from langgraph.checkpoint.base import (
     CheckpointMetadata,
     CheckpointTuple,
     SerializerProtocol,
-    copy_checkpoint,
 )
 from langgraph.checkpoint.memory import InMemorySaver, PersistentDict
 
@@ -63,69 +61,14 @@ class MemorySaverAssertImmutable(InMemorySaver):
                     self.storage_for_copies[thread_id][checkpoint_ns][saved["id"]]
                 )
                 == saved
-            )
+            ), config["configurable"]["checkpoint_ns"]
         self.storage_for_copies[thread_id][checkpoint_ns][checkpoint["id"]] = (
-            self.serde.dumps_typed(copy_checkpoint(checkpoint))
+            self.serde.dumps_typed(checkpoint)
         )
         # call super to write checkpoint
         return super().put(config, checkpoint, metadata, new_versions)
 
 
-class MemorySaverAssertCheckpointMetadata(InMemorySaver):
-    """This custom checkpointer is for verifying that a run's configurable
-    fields are merged with the previous checkpoint config for each step in
-    the run. This is the desired behavior. Because the checkpointer's (a)put()
-    method is called for each step, the implementation of this checkpointer
-    should produce a side effect that can be asserted.
-    """
-
-    def put(
-        self,
-        config: RunnableConfig,
-        checkpoint: Checkpoint,
-        metadata: CheckpointMetadata,
-        new_versions: ChannelVersions,
-    ) -> None:
-        """The implementation of put() merges config["configurable"] (a run's
-        configurable fields) with the metadata field. The state of the
-        checkpoint metadata can be asserted to confirm that the run's
-        configurable fields were merged with the previous checkpoint config.
-        """
-        configurable = config["configurable"].copy()
-
-        # remove checkpoint_id to make testing simpler
-        checkpoint_id = configurable.pop("checkpoint_id", None)
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"]["checkpoint_ns"]
-        self.storage[thread_id][checkpoint_ns].update(
-            {
-                checkpoint["id"]: (
-                    self.serde.dumps_typed(checkpoint),
-                    # merge configurable fields and metadata
-                    self.serde.dumps_typed({**configurable, **metadata}),
-                    checkpoint_id,
-                )
-            }
-        )
-        return {
-            "configurable": {
-                "thread_id": config["configurable"]["thread_id"],
-                "checkpoint_id": checkpoint["id"],
-            }
-        }
-
-    async def aput(
-        self,
-        config: RunnableConfig,
-        checkpoint: Checkpoint,
-        metadata: CheckpointMetadata,
-        new_versions: ChannelVersions,
-    ) -> RunnableConfig:
-        return await asyncio.get_running_loop().run_in_executor(
-            None, self.put, config, checkpoint, metadata, new_versions
-        )
-
-
 class MemorySaverNoPending(InMemorySaver):
     def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
         result = super().get_tuple(config)

libs/langgraph/tests/test_large_cases.py

@@ -2483,7 +2483,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
             {
                 "langgraph_step": 1,
                 "langgraph_node": "agent",
-                "langgraph_triggers": ["start:agent"],
+                "langgraph_triggers": ("branch:to:agent", "start:agent", "tools"),
                 "langgraph_path": (PULL, "agent"),
                 "langgraph_checkpoint_ns": AnyStr("agent:"),
                 "checkpoint_ns": AnyStr("agent:"),
@@ -2500,7 +2500,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
             {
                 "langgraph_step": 2,
                 "langgraph_node": "tools",
-                "langgraph_triggers": ["branch:agent:should_continue:tools"],
+                "langgraph_triggers": ("branch:to:tools",),
                 "langgraph_path": (PULL, "tools"),
                 "langgraph_checkpoint_ns": AnyStr("tools:"),
             },
@@ -2542,7 +2542,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
             {
                 "langgraph_step": 3,
                 "langgraph_node": "agent",
-                "langgraph_triggers": ["tools"],
+                "langgraph_triggers": ("branch:to:agent", "start:agent", "tools"),
                 "langgraph_path": (PULL, "agent"),
                 "langgraph_checkpoint_ns": AnyStr("agent:"),
                 "checkpoint_ns": AnyStr("agent:"),
@@ -2559,7 +2559,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
             {
                 "langgraph_step": 4,
                 "langgraph_node": "tools",
-                "langgraph_triggers": ["branch:agent:should_continue:tools"],
+                "langgraph_triggers": ("branch:to:tools",),
                 "langgraph_path": (PULL, "tools"),
                 "langgraph_checkpoint_ns": AnyStr("tools:"),
             },
@@ -2573,7 +2573,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
             {
                 "langgraph_step": 4,
                 "langgraph_node": "tools",
-                "langgraph_triggers": ["branch:agent:should_continue:tools"],
+                "langgraph_triggers": ("branch:to:tools",),
                 "langgraph_path": (PULL, "tools"),
                 "langgraph_checkpoint_ns": AnyStr("tools:"),
             },
@@ -2585,7 +2585,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
             {
                 "langgraph_step": 5,
                 "langgraph_node": "agent",
-                "langgraph_triggers": ["tools"],
+                "langgraph_triggers": ("branch:to:agent", "start:agent", "tools"),
                 "langgraph_path": (PULL, "agent"),
                 "langgraph_checkpoint_ns": AnyStr("agent:"),
                 "checkpoint_ns": AnyStr("agent:"),
@@ -5501,7 +5501,10 @@ def test_in_one_fan_out_out_one_graph_state() -> None:
                     "id": AnyStr(),
                     "name": "rewrite_query",
                     "input": {"query": "what is weather in sf", "docs": []},
-                    "triggers": ["start:rewrite_query"],
+                    "triggers": (
+                        "branch:to:rewrite_query",
+                        "start:rewrite_query",
+                    ),
                 },
             },
         ),
@@ -5532,7 +5535,10 @@ def test_in_one_fan_out_out_one_graph_state() -> None:
                     "id": AnyStr(),
                     "name": "retriever_one",
                     "input": {"query": "query: what is weather in sf", "docs": []},
-                    "triggers": ["rewrite_query"],
+                    "triggers": (
+                        "branch:to:retriever_one",
+                        "rewrite_query",
+                    ),
                 },
             },
         ),
@@ -5546,7 +5552,10 @@ def test_in_one_fan_out_out_one_graph_state() -> None:
                     "id": AnyStr(),
                     "name": "retriever_two",
                     "input": {"query": "query: what is weather in sf", "docs": []},
-                    "triggers": ["rewrite_query"],
+                    "triggers": (
+                        "branch:to:retriever_two",
+                        "rewrite_query",
+                    ),
                 },
             },
         ),
@@ -5608,7 +5617,7 @@ def test_in_one_fan_out_out_one_graph_state() -> None:
                         "query": "query: what is weather in sf",
                         "docs": ["doc1", "doc2", "doc3", "doc4"],
                     },
-                    "triggers": ["retriever_one", "retriever_two"],
+                    "triggers": ("branch:to:qa", "retriever_one", "retriever_two"),
                 },
             },
         ),
@@ -6634,7 +6643,7 @@ def test_branch_then(
                 "id": AnyStr(),
                 "name": "prepare",
                 "input": {"my_key": "value", "market": "DE"},
-                "triggers": ["start:prepare"],
+                "triggers": ("branch:to:prepare", "start:prepare"),
             },
         },
         {
@@ -6706,7 +6715,7 @@ def test_branch_then(
                 "id": AnyStr(),
                 "name": "tool_two_slow",
                 "input": {"my_key": "value prepared", "market": "DE"},
-                "triggers": ["branch:prepare:condition:tool_two_slow"],
+                "triggers": ("branch:to:tool_two_slow",),
             },
         },
         {
@@ -6773,7 +6782,10 @@ def test_branch_then(
                 "id": AnyStr(),
                 "name": "finish",
                 "input": {"my_key": "value prepared slow", "market": "DE"},
-                "triggers": ["branch:prepare:condition::then"],
+                "triggers": (
+                    "branch:prepare:condition::then",
+                    "branch:to:finish",
+                ),
             },
         },
         {
@@ -7783,7 +7795,7 @@ def test_nested_graph_state(
                         "langgraph_node": "inner",
                         "langgraph_path": [PULL, "inner"],
                         "langgraph_step": 2,
-                        "langgraph_triggers": ["outer_1"],
+                        "langgraph_triggers": ["branch:to:inner", "outer_1"],
                         "langgraph_checkpoint_ns": AnyStr("inner:"),
                     },
                     created_at=AnyStr(),
@@ -7978,7 +7990,7 @@ def test_nested_graph_state(
                 "langgraph_node": "inner",
                 "langgraph_path": [PULL, "inner"],
                 "langgraph_step": 2,
-                "langgraph_triggers": ["outer_1"],
+                "langgraph_triggers": ["branch:to:inner", "outer_1"],
                 "langgraph_checkpoint_ns": AnyStr("inner:"),
             },
             created_at=AnyStr(),
@@ -8021,7 +8033,7 @@ def test_nested_graph_state(
                 "langgraph_node": "inner",
                 "langgraph_path": [PULL, "inner"],
                 "langgraph_step": 2,
-                "langgraph_triggers": ["outer_1"],
+                "langgraph_triggers": ["branch:to:inner", "outer_1"],
                 "langgraph_checkpoint_ns": AnyStr("inner:"),
             },
             created_at=AnyStr(),
@@ -8070,7 +8082,7 @@ def test_nested_graph_state(
                 "langgraph_node": "inner",
                 "langgraph_path": [PULL, "inner"],
                 "langgraph_step": 2,
-                "langgraph_triggers": ["outer_1"],
+                "langgraph_triggers": ["branch:to:inner", "outer_1"],
                 "langgraph_checkpoint_ns": AnyStr("inner:"),
             },
             created_at=AnyStr(),
@@ -8504,7 +8516,7 @@ def test_doubly_nested_graph_state(
             "langgraph_node": "child_1",
             "langgraph_path": [PULL, AnyStr("child_1")],
             "langgraph_step": 1,
-            "langgraph_triggers": [AnyStr("start:child_1")],
+            "langgraph_triggers": ["branch:to:child_1", AnyStr("start:child_1")],
         },
         created_at=AnyStr(),
         parent_config=(
@@ -8588,7 +8600,10 @@ def test_doubly_nested_graph_state(
                                         AnyStr("child_1"),
                                     ],
                                     "langgraph_step": 1,
-                                    "langgraph_triggers": [AnyStr("start:child_1")],
+                                    "langgraph_triggers": [
+                                        "branch:to:child_1",
+                                        AnyStr("start:child_1"),
+                                    ],
                                 },
                                 created_at=AnyStr(),
                                 parent_config=(
@@ -8635,7 +8650,7 @@ def test_doubly_nested_graph_state(
                         "langgraph_node": "child",
                         "langgraph_path": [PULL, AnyStr("child")],
                         "langgraph_step": 2,
-                        "langgraph_triggers": [AnyStr("parent_1")],
+                        "langgraph_triggers": ["branch:to:child", AnyStr("parent_1")],
                         "langgraph_checkpoint_ns": AnyStr("child:"),
                     },
                     created_at=AnyStr(),
@@ -8931,7 +8946,7 @@ def test_doubly_nested_graph_state(
                 "langgraph_node": "child",
                 "langgraph_path": [PULL, AnyStr("child")],
                 "langgraph_step": 2,
-                "langgraph_triggers": [AnyStr("parent_1")],
+                "langgraph_triggers": ["branch:to:child", AnyStr("parent_1")],
                 "langgraph_checkpoint_ns": AnyStr("child:"),
             },
             created_at=AnyStr(),
@@ -8970,7 +8985,7 @@ def test_doubly_nested_graph_state(
                 "langgraph_node": "child",
                 "langgraph_path": [PULL, AnyStr("child")],
                 "langgraph_step": 2,
-                "langgraph_triggers": [AnyStr("parent_1")],
+                "langgraph_triggers": ["branch:to:child", AnyStr("parent_1")],
                 "langgraph_checkpoint_ns": AnyStr("child:"),
             },
             created_at=AnyStr(),
@@ -9022,7 +9037,7 @@ def test_doubly_nested_graph_state(
                 "langgraph_node": "child",
                 "langgraph_path": [PULL, AnyStr("child")],
                 "langgraph_step": 2,
-                "langgraph_triggers": [AnyStr("parent_1")],
+                "langgraph_triggers": ["branch:to:child", AnyStr("parent_1")],
                 "langgraph_checkpoint_ns": AnyStr("child:"),
             },
             created_at=AnyStr(),
@@ -9076,7 +9091,7 @@ def test_doubly_nested_graph_state(
                     AnyStr("child_1"),
                 ],
                 "langgraph_step": 1,
-                "langgraph_triggers": [AnyStr("start:child_1")],
+                "langgraph_triggers": ["branch:to:child_1", AnyStr("start:child_1")],
             },
             created_at=AnyStr(),
             parent_config={
@@ -9131,7 +9146,7 @@ def test_doubly_nested_graph_state(
                     AnyStr("child_1"),
                 ],
                 "langgraph_step": 1,
-                "langgraph_triggers": [AnyStr("start:child_1")],
+                "langgraph_triggers": ["branch:to:child_1", AnyStr("start:child_1")],
             },
             created_at=AnyStr(),
             parent_config={
@@ -9193,7 +9208,7 @@ def test_doubly_nested_graph_state(
                     AnyStr("child_1"),
                 ],
                 "langgraph_step": 1,
-                "langgraph_triggers": [AnyStr("start:child_1")],
+                "langgraph_triggers": ["branch:to:child_1", AnyStr("start:child_1")],
             },
             created_at=AnyStr(),
             parent_config={
@@ -9255,7 +9270,7 @@ def test_doubly_nested_graph_state(
                     AnyStr("child_1"),
                 ],
                 "langgraph_step": 1,
-                "langgraph_triggers": [AnyStr("start:child_1")],
+                "langgraph_triggers": ["branch:to:child_1", AnyStr("start:child_1")],
             },
             created_at=AnyStr(),
             parent_config=None,
@@ -10378,9 +10393,7 @@ def test_weather_subgraph(
                         "langgraph_node": "weather_graph",
                         "langgraph_path": [PULL, "weather_graph"],
                         "langgraph_step": 2,
-                        "langgraph_triggers": [
-                            "branch:router_node:route_after_prediction:weather_graph"
-                        ],
+                        "langgraph_triggers": ["branch:to:weather_graph"],
                         "langgraph_checkpoint_ns": AnyStr("weather_graph:"),
                     },
                     created_at=AnyStr(),
@@ -10492,9 +10505,7 @@ def test_weather_subgraph(
                         "langgraph_node": "weather_graph",
                         "langgraph_path": [PULL, "weather_graph"],
                         "langgraph_step": 2,
-                        "langgraph_triggers": [
-                            "branch:router_node:route_after_prediction:weather_graph"
-                        ],
+                        "langgraph_triggers": ["branch:to:weather_graph"],
                         "langgraph_checkpoint_ns": AnyStr("weather_graph:"),
                     },
                     created_at=AnyStr(),

libs/langgraph/tests/test_large_cases_async.py

@@ -2300,7 +2300,11 @@ async def test_prebuilt_tool_chat() -> None:
             {
                 "langgraph_step": 1,
                 "langgraph_node": "agent",
-                "langgraph_triggers": ["start:agent"],
+                "langgraph_triggers": (
+                    "branch:to:agent",
+                    "start:agent",
+                    "tools",
+                ),
                 "langgraph_path": ("__pregel_pull", "agent"),
                 "langgraph_checkpoint_ns": AnyStr("agent:"),
                 "checkpoint_ns": AnyStr("agent:"),
@@ -2317,7 +2321,7 @@ async def test_prebuilt_tool_chat() -> None:
             {
                 "langgraph_step": 2,
                 "langgraph_node": "tools",
-                "langgraph_triggers": ["branch:agent:should_continue:tools"],
+                "langgraph_triggers": ("branch:to:tools",),
                 "langgraph_path": ("__pregel_pull", "tools"),
                 "langgraph_checkpoint_ns": AnyStr("tools:"),
             },
@@ -2359,7 +2363,11 @@ async def test_prebuilt_tool_chat() -> None:
             {
                 "langgraph_step": 3,
                 "langgraph_node": "agent",
-                "langgraph_triggers": ["tools"],
+                "langgraph_triggers": (
+                    "branch:to:agent",
+                    "start:agent",
+                    "tools",
+                ),
                 "langgraph_path": ("__pregel_pull", "agent"),
                 "langgraph_checkpoint_ns": AnyStr("agent:"),
                 "checkpoint_ns": AnyStr("agent:"),
@@ -2376,7 +2384,7 @@ async def test_prebuilt_tool_chat() -> None:
             {
                 "langgraph_step": 4,
                 "langgraph_node": "tools",
-                "langgraph_triggers": ["branch:agent:should_continue:tools"],
+                "langgraph_triggers": ("branch:to:tools",),
                 "langgraph_path": ("__pregel_pull", "tools"),
                 "langgraph_checkpoint_ns": AnyStr("tools:"),
             },
@@ -2390,7 +2398,7 @@ async def test_prebuilt_tool_chat() -> None:
             {
                 "langgraph_step": 4,
                 "langgraph_node": "tools",
-                "langgraph_triggers": ["branch:agent:should_continue:tools"],
+                "langgraph_triggers": ("branch:to:tools",),
                 "langgraph_path": ("__pregel_pull", "tools"),
                 "langgraph_checkpoint_ns": AnyStr("tools:"),
             },
@@ -2402,7 +2410,11 @@ async def test_prebuilt_tool_chat() -> None:
             {
                 "langgraph_step": 5,
                 "langgraph_node": "agent",
-                "langgraph_triggers": ["tools"],
+                "langgraph_triggers": (
+                    "branch:to:agent",
+                    "start:agent",
+                    "tools",
+                ),
                 "langgraph_path": ("__pregel_pull", "agent"),
                 "langgraph_checkpoint_ns": AnyStr("agent:"),
                 "checkpoint_ns": AnyStr("agent:"),
@@ -3883,7 +3895,10 @@ async def test_in_one_fan_out_out_one_graph_state() -> None:
                     "id": AnyStr(),
                     "name": "rewrite_query",
                     "input": {"query": "what is weather in sf", "docs": []},
-                    "triggers": ["start:rewrite_query"],
+                    "triggers": (
+                        "branch:to:rewrite_query",
+                        "start:rewrite_query",
+                    ),
                 },
             },
         ),
@@ -3914,7 +3929,10 @@ async def test_in_one_fan_out_out_one_graph_state() -> None:
                     "id": AnyStr(),
                     "name": "retriever_one",
                     "input": {"query": "query: what is weather in sf", "docs": []},
-                    "triggers": ["rewrite_query"],
+                    "triggers": (
+                        "branch:to:retriever_one",
+                        "rewrite_query",
+                    ),
                 },
             },
         ),
@@ -3928,7 +3946,10 @@ async def test_in_one_fan_out_out_one_graph_state() -> None:
                     "id": AnyStr(),
                     "name": "retriever_two",
                     "input": {"query": "query: what is weather in sf", "docs": []},
-                    "triggers": ["rewrite_query"],
+                    "triggers": (
+                        "branch:to:retriever_two",
+                        "rewrite_query",
+                    ),
                 },
             },
         ),
@@ -3990,7 +4011,7 @@ async def test_in_one_fan_out_out_one_graph_state() -> None:
                         "query": "query: what is weather in sf",
                         "docs": ["doc1", "doc2", "doc3", "doc4"],
                     },
-                    "triggers": ["retriever_one", "retriever_two"],
+                    "triggers": ("branch:to:qa", "retriever_one", "retriever_two"),
                 },
             },
         ),
@@ -4465,7 +4486,10 @@ async def test_branch_then(checkpointer_name: str) -> None:
                     "id": AnyStr(),
                     "name": "prepare",
                     "input": {"my_key": "value", "market": "DE"},
-                    "triggers": ["start:prepare"],
+                    "triggers": (
+                        "branch:to:prepare",
+                        "start:prepare",
+                    ),
                 },
             },
             {
@@ -4537,7 +4561,7 @@ async def test_branch_then(checkpointer_name: str) -> None:
                     "id": AnyStr(),
                     "name": "tool_two_slow",
                     "input": {"my_key": "value prepared", "market": "DE"},
-                    "triggers": ["branch:prepare:condition:tool_two_slow"],
+                    "triggers": ("branch:to:tool_two_slow",),
                 },
             },
             {
@@ -4609,7 +4633,10 @@ async def test_branch_then(checkpointer_name: str) -> None:
                     "id": AnyStr(),
                     "name": "finish",
                     "input": {"my_key": "value prepared slow", "market": "DE"},
-                    "triggers": ["branch:prepare:condition::then"],
+                    "triggers": (
+                        "branch:prepare:condition::then",
+                        "branch:to:finish",
+                    ),
                 },
             },
             {
@@ -4778,7 +4805,10 @@ async def test_branch_then(checkpointer_name: str) -> None:
                     "id": AnyStr(),
                     "name": "prepare",
                     "input": {"my_key": "value", "market": "DE"},
-                    "triggers": ["start:prepare"],
+                    "triggers": (
+                        "branch:to:prepare",
+                        "start:prepare",
+                    ),
                 },
             },
             {
@@ -5333,7 +5363,7 @@ async def test_nested_graph_state(checkpointer_name: str) -> None:
                             "langgraph_node": "inner",
                             "langgraph_path": [PULL, "inner"],
                             "langgraph_step": 2,
-                            "langgraph_triggers": ["outer_1"],
+                            "langgraph_triggers": ["branch:to:inner", "outer_1"],
                             "langgraph_checkpoint_ns": AnyStr("inner:"),
                         },
                         created_at=AnyStr(),
@@ -5530,7 +5560,7 @@ async def test_nested_graph_state(checkpointer_name: str) -> None:
                     "langgraph_node": "inner",
                     "langgraph_path": [PULL, "inner"],
                     "langgraph_step": 2,
-                    "langgraph_triggers": ["outer_1"],
+                    "langgraph_triggers": ["branch:to:inner", "outer_1"],
                     "langgraph_checkpoint_ns": AnyStr("inner:"),
                 },
                 created_at=AnyStr(),
@@ -5573,7 +5603,7 @@ async def test_nested_graph_state(checkpointer_name: str) -> None:
                     "langgraph_node": "inner",
                     "langgraph_path": [PULL, "inner"],
                     "langgraph_step": 2,
-                    "langgraph_triggers": ["outer_1"],
+                    "langgraph_triggers": ["branch:to:inner", "outer_1"],
                     "langgraph_checkpoint_ns": AnyStr("inner:"),
                 },
                 created_at=AnyStr(),
@@ -5622,7 +5652,7 @@ async def test_nested_graph_state(checkpointer_name: str) -> None:
                     "langgraph_node": "inner",
                     "langgraph_path": [PULL, "inner"],
                     "langgraph_step": 2,
-                    "langgraph_triggers": ["outer_1"],
+                    "langgraph_triggers": ["branch:to:inner", "outer_1"],
                     "langgraph_checkpoint_ns": AnyStr("inner:"),
                 },
                 created_at=AnyStr(),
@@ -6060,7 +6090,7 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                 "langgraph_node": "child_1",
                 "langgraph_path": [PULL, AnyStr("child_1")],
                 "langgraph_step": 1,
-                "langgraph_triggers": [AnyStr("start:child_1")],
+                "langgraph_triggers": ["branch:to:child_1", "start:child_1"],
             },
             created_at=AnyStr(),
             parent_config=(
@@ -6146,7 +6176,10 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                                             AnyStr("child_1"),
                                         ],
                                         "langgraph_step": 1,
-                                        "langgraph_triggers": [AnyStr("start:child_1")],
+                                        "langgraph_triggers": [
+                                            "branch:to:child_1",
+                                            "start:child_1",
+                                        ],
                                     },
                                     created_at=AnyStr(),
                                     parent_config=(
@@ -6195,7 +6228,10 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                             "langgraph_node": "child",
                             "langgraph_path": [PULL, AnyStr("child")],
                             "langgraph_step": 2,
-                            "langgraph_triggers": [AnyStr("parent_1")],
+                            "langgraph_triggers": [
+                                "branch:to:child",
+                                AnyStr("parent_1"),
+                            ],
                             "langgraph_checkpoint_ns": AnyStr("child:"),
                         },
                         created_at=AnyStr(),
@@ -6493,7 +6529,7 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                     "langgraph_node": "child",
                     "langgraph_path": [PULL, AnyStr("child")],
                     "langgraph_step": 2,
-                    "langgraph_triggers": [AnyStr("parent_1")],
+                    "langgraph_triggers": ["branch:to:child", AnyStr("parent_1")],
                     "langgraph_checkpoint_ns": AnyStr("child:"),
                 },
                 created_at=AnyStr(),
@@ -6532,7 +6568,7 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                     "langgraph_node": "child",
                     "langgraph_path": [PULL, AnyStr("child")],
                     "langgraph_step": 2,
-                    "langgraph_triggers": [AnyStr("parent_1")],
+                    "langgraph_triggers": ["branch:to:child", AnyStr("parent_1")],
                     "langgraph_checkpoint_ns": AnyStr("child:"),
                 },
                 created_at=AnyStr(),
@@ -6584,7 +6620,7 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                     "langgraph_node": "child",
                     "langgraph_path": [PULL, AnyStr("child")],
                     "langgraph_step": 2,
-                    "langgraph_triggers": [AnyStr("parent_1")],
+                    "langgraph_triggers": ["branch:to:child", AnyStr("parent_1")],
                     "langgraph_checkpoint_ns": AnyStr("child:"),
                 },
                 created_at=AnyStr(),
@@ -6642,7 +6678,10 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                         AnyStr("child_1"),
                     ],
                     "langgraph_step": 1,
-                    "langgraph_triggers": [AnyStr("start:child_1")],
+                    "langgraph_triggers": [
+                        "branch:to:child_1",
+                        AnyStr("start:child_1"),
+                    ],
                 },
                 created_at=AnyStr(),
                 parent_config={
@@ -6697,7 +6736,10 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                         AnyStr("child_1"),
                     ],
                     "langgraph_step": 1,
-                    "langgraph_triggers": [AnyStr("start:child_1")],
+                    "langgraph_triggers": [
+                        "branch:to:child_1",
+                        AnyStr("start:child_1"),
+                    ],
                 },
                 created_at=AnyStr(),
                 parent_config={
@@ -6759,7 +6801,10 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                         AnyStr("child_1"),
                     ],
                     "langgraph_step": 1,
-                    "langgraph_triggers": [AnyStr("start:child_1")],
+                    "langgraph_triggers": [
+                        "branch:to:child_1",
+                        AnyStr("start:child_1"),
+                    ],
                 },
                 created_at=AnyStr(),
                 parent_config={
@@ -6821,7 +6866,10 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                         AnyStr("child_1"),
                     ],
                     "langgraph_step": 1,
-                    "langgraph_triggers": [AnyStr("start:child_1")],
+                    "langgraph_triggers": [
+                        "branch:to:child_1",
+                        AnyStr("start:child_1"),
+                    ],
                 },
                 created_at=AnyStr(),
                 parent_config=None,
@@ -7231,9 +7279,7 @@ async def test_weather_subgraph(
                             "langgraph_node": "weather_graph",
                             "langgraph_path": [PULL, "weather_graph"],
                             "langgraph_step": 2,
-                            "langgraph_triggers": [
-                                "branch:router_node:route_after_prediction:weather_graph"
-                            ],
+                            "langgraph_triggers": ["branch:to:weather_graph"],
                             "langgraph_checkpoint_ns": AnyStr("weather_graph:"),
                         },
                         created_at=AnyStr(),
@@ -7347,9 +7393,7 @@ async def test_weather_subgraph(
                             "langgraph_node": "weather_graph",
                             "langgraph_path": [PULL, "weather_graph"],
                             "langgraph_step": 2,
-                            "langgraph_triggers": [
-                                "branch:router_node:route_after_prediction:weather_graph"
-                            ],
+                            "langgraph_triggers": ["branch:to:weather_graph"],
                             "langgraph_checkpoint_ns": AnyStr("weather_graph:"),
                         },
                         created_at=AnyStr(),

libs/langgraph/tests/test_pregel.py

@@ -1,5 +1,6 @@
 import enum
 import functools
+import gc
 import json
 import logging
 import operator
@@ -62,13 +63,16 @@ from langgraph.graph import END, Graph, StateGraph
 from langgraph.graph.message import MessageGraph, MessagesState, add_messages
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.pregel import Channel, GraphRecursionError, Pregel, StateSnapshot
+from langgraph.pregel.loop import SyncPregelLoop
 from langgraph.pregel.retry import RetryPolicy
+from langgraph.pregel.runner import PregelRunner
 from langgraph.store.base import BaseStore
 from langgraph.types import (
     Command,
     Interrupt,
     PregelTask,
     Send,
+    StateUpdate,
     StreamWriter,
     interrupt,
 )
@@ -80,7 +84,6 @@ from tests.conftest import (
     REGULAR_CHECKPOINTERS_SYNC,
     SHOULD_CHECK_SNAPSHOTS,
 )
-from tests.memory_assert import MemorySaverAssertCheckpointMetadata
 from tests.messages import (
     _AnyIdAIMessage,
     _AnyIdAIMessageChunk,
@@ -817,7 +820,7 @@ def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None:
                 "id": AnyStr(),
                 "name": "one",
                 "input": 2,
-                "triggers": ["input"],
+                "triggers": ("input",),
             },
         },
         {
@@ -828,7 +831,7 @@ def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None:
                 "id": AnyStr(),
                 "name": "two",
                 "input": [12],
-                "triggers": ["inbox"],
+                "triggers": ("inbox",),
             },
         },
         {
@@ -863,7 +866,7 @@ def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None:
                 "id": AnyStr(),
                 "name": "two",
                 "input": [3],
-                "triggers": ["inbox"],
+                "triggers": ("inbox",),
             },
         },
         {
@@ -1233,7 +1236,7 @@ def test_pending_writes_resume(
             }
         },
         checkpoint={
-            "v": 1,
+            "v": 2,
             "id": AnyStr(),
             "ts": AnyStr(),
             "pending_sends": [],
@@ -1293,7 +1296,7 @@ def test_pending_writes_resume(
             }
         },
         checkpoint={
-            "v": 1,
+            "v": 2,
             "id": AnyStr(),
             "ts": AnyStr(),
             "pending_sends": [],
@@ -1346,7 +1349,7 @@ def test_pending_writes_resume(
             }
         },
         checkpoint={
-            "v": 1,
+            "v": 2,
             "id": AnyStr(),
             "ts": AnyStr(),
             "pending_sends": [],
@@ -3247,14 +3250,24 @@ def test_in_one_fan_out_state_graph_waiting_edge_plus_regular(
 
     assert [
         c for c in app_w_interrupt.stream({"query": "what is weather in sf"}, config)
-    ] == [
-        {"rewrite_query": {"query": "query: what is weather in sf"}},
-        {"qa": {"answer": ""}},
-        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
-        {"retriever_two": {"docs": ["doc3", "doc4"]}},
-        {"retriever_one": {"docs": ["doc1", "doc2"]}},
-        {"__interrupt__": ()},
-    ]
+    ] in (
+        [
+            {"rewrite_query": {"query": "query: what is weather in sf"}},
+            {"qa": {"answer": ""}},
+            {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
+            {"retriever_two": {"docs": ["doc3", "doc4"]}},
+            {"retriever_one": {"docs": ["doc1", "doc2"]}},
+            {"__interrupt__": ()},
+        ],
+        [
+            {"rewrite_query": {"query": "query: what is weather in sf"}},
+            {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
+            {"qa": {"answer": ""}},
+            {"retriever_two": {"docs": ["doc3", "doc4"]}},
+            {"retriever_one": {"docs": ["doc1", "doc2"]}},
+            {"__interrupt__": ()},
+        ],
+    )
 
     assert [c for c in app_w_interrupt.stream(None, config)] == [
         {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
@@ -4199,11 +4212,11 @@ def test_checkpoint_metadata() -> None:
     workflow.add_edge("tools", "agent")
 
     # graph w/o interrupt
-    checkpointer_1 = MemorySaverAssertCheckpointMetadata()
+    checkpointer_1 = InMemorySaver()
     app = workflow.compile(checkpointer=checkpointer_1)
 
     # graph w/ interrupt
-    checkpointer_2 = MemorySaverAssertCheckpointMetadata()
+    checkpointer_2 = InMemorySaver()
     app_w_interrupt = workflow.compile(
         checkpointer=checkpointer_2, interrupt_before=["tools"]
     )
@@ -4621,59 +4634,6 @@ def test_multiple_sinks_subgraphs(snapshot: SnapshotAssertion) -> None:
     assert app.get_graph(xray=True).draw_mermaid() == snapshot
 
 
-def test_subgraph_retries():
-    class State(TypedDict):
-        count: int
-
-    class ChildState(State):
-        some_list: Annotated[list, operator.add]
-
-    called_times = 0
-
-    class RandomError(ValueError):
-        """This will be retried on."""
-
-    def parent_node(state: State):
-        return {"count": state["count"] + 1}
-
-    def child_node_a(state: ChildState):
-        nonlocal called_times
-        # We want it to retry only on node_b
-        # NOT re-compute the whole graph.
-        assert not called_times
-        called_times += 1
-        return {"some_list": ["val"]}
-
-    def child_node_b(state: ChildState):
-        raise RandomError("First attempt fails")
-
-    child = StateGraph(ChildState)
-    child.add_node(child_node_a)
-    child.add_node(child_node_b)
-    child.add_edge("__start__", "child_node_a")
-    child.add_edge("child_node_a", "child_node_b")
-
-    parent = StateGraph(State)
-    parent.add_node("parent_node", parent_node)
-    parent.add_node(
-        "child_graph",
-        child.compile(),
-        retry=RetryPolicy(
-            max_attempts=3,
-            retry_on=(RandomError,),
-            backoff_factor=0.0001,
-            initial_interval=0.0001,
-        ),
-    )
-    parent.add_edge("parent_node", "child_graph")
-    parent.set_entry_point("parent_node")
-
-    checkpointer = InMemorySaver()
-    app = parent.compile(checkpointer=checkpointer)
-    with pytest.raises(RandomError):
-        app.invoke({"count": 0}, {"configurable": {"thread_id": "foo"}})
-
-
 @pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
 @pytest.mark.parametrize("store_name", ALL_STORES_SYNC)
 def test_store_injected(
@@ -5969,9 +5929,7 @@ def test_falsy_return_from_task(
                     "a": 5,
                 },
                 "name": "graph",
-                "triggers": [
-                    "__start__",
-                ],
+                "triggers": ("__start__",),
             },
             "step": 0,
             "timestamp": AnyStr(),
@@ -5985,9 +5943,7 @@ def test_falsy_return_from_task(
                     {},
                 ),
                 "name": "falsy_task",
-                "triggers": [
-                    "__pregel_push",
-                ],
+                "triggers": ("__pregel_push",),
             },
             "step": 0,
             "timestamp": AnyStr(),
@@ -6094,9 +6050,7 @@ def test_falsy_return_from_task(
                     "a": 5,
                 },
                 "name": "graph",
-                "triggers": [
-                    "__start__",
-                ],
+                "triggers": ("__start__",),
             },
             "step": 0,
             "timestamp": AnyStr(),
@@ -6110,9 +6064,7 @@ def test_falsy_return_from_task(
                     {},
                 ),
                 "name": "falsy_task",
-                "triggers": [
-                    "__pregel_push",
-                ],
+                "triggers": ("__pregel_push",),
             },
             "step": 0,
             "timestamp": AnyStr(),
@@ -6288,6 +6240,7 @@ def test_double_interrupt_subgraph(
     def invoke_sub_agent(state: AgentState):
         return subgraph.invoke(state)
 
+    thread = {"configurable": {"thread_id": str(uuid.uuid4())}}
     parent_agent = (
         StateGraph(AgentState)
         .add_node("invoke_sub_agent", invoke_sub_agent)
@@ -6923,7 +6876,10 @@ def test_tags_stream_mode_messages() -> None:
             {
                 "langgraph_step": 1,
                 "langgraph_node": "call_model",
-                "langgraph_triggers": ["start:call_model"],
+                "langgraph_triggers": (
+                    "branch:to:call_model",
+                    "start:call_model",
+                ),
                 "langgraph_path": ("__pregel_pull", "call_model"),
                 "langgraph_checkpoint_ns": AnyStr("call_model:"),
                 "checkpoint_ns": AnyStr("call_model:"),
@@ -7317,3 +7273,614 @@ def test_empty_invoke() -> None:
         "111": 111,
         "222": 222,
     }
+
+
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
+def test_parallel_interrupts(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    from pydantic import BaseModel, Field
+
+    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
+
+    # --- CHILD GRAPH ---
+
+    class ChildState(BaseModel):
+        prompt: str = Field(..., description="What is going to be asked to the user?")
+        human_input: Optional[str] = Field(None, description="What the human said")
+        human_inputs: Annotated[List[str], operator.add] = Field(
+            default_factory=list, description="All of my messages"
+        )
+
+    def get_human_input(state: ChildState):
+        human_input = interrupt(state.prompt)
+
+        return dict(
+            human_input=human_input,  # update child state
+            human_inputs=[human_input],  # update parent state
+        )
+
+    child_graph_builder = StateGraph(ChildState)
+    child_graph_builder.add_node("get_human_input", get_human_input)
+    child_graph_builder.add_edge(START, "get_human_input")
+    child_graph_builder.add_edge("get_human_input", END)
+    child_graph = child_graph_builder.compile()
+
+    # --- PARENT GRAPH ---
+
+    class ParentState(BaseModel):
+        prompts: List[str] = Field(
+            ..., description="What is going to be asked to the user?"
+        )
+        human_inputs: Annotated[List[str], operator.add] = Field(
+            default_factory=list, description="All of my messages"
+        )
+
+    def assign_workers(state: ParentState):
+        return [
+            Send(
+                "child_graph",
+                dict(
+                    prompt=prompt,
+                ),
+            )
+            for prompt in state.prompts
+        ]
+
+    def cleanup(state: ParentState):
+        assert len(state.human_inputs) == len(state.prompts)
+
+    parent_graph_builder = StateGraph(ParentState)
+    parent_graph_builder.add_node("child_graph", child_graph)
+    parent_graph_builder.add_node("cleanup", cleanup)
+
+    parent_graph_builder.add_conditional_edges(START, assign_workers, ["child_graph"])
+    parent_graph_builder.add_edge("child_graph", "cleanup")
+    parent_graph_builder.add_edge("cleanup", END)
+
+    parent_graph = parent_graph_builder.compile(checkpointer=checkpointer)
+
+    # --- CLIENT INVOCATION ---
+
+    thread_config = dict(
+        configurable=dict(
+            thread_id=str(uuid.uuid4()),
+        )
+    )
+    current_input = dict(
+        prompts=["a", "b"],
+    )
+
+    invokes = 0
+    events: dict[int, list[dict]] = {}
+    while invokes < 10:
+        # reset interrupt
+        invokes += 1
+        events[invokes] = []
+        current_interrupts: list[Interrupt] = []
+
+        # start / resume the graph
+        for event in parent_graph.stream(
+            input=current_input,
+            config=thread_config,
+            stream_mode="updates",
+        ):
+            events[invokes].append(event)
+            # handle the interrupt
+            if "__interrupt__" in event:
+                current_interrupts.extend(event["__interrupt__"])
+                # assume that it breaks here, because it is an interrupt
+
+        # get human input and resume
+        if any(i.resumable for i in current_interrupts):
+            current_input = Command(resume=f"Resume #{invokes}")
+
+        # not more human input required, must be completed
+        else:
+            break
+    else:
+        assert False, "Detected infinite loop"
+
+    assert invokes == 3
+    assert len(events) == 3
+
+    assert events[1] == UnsortedSequence(
+        {
+            "__interrupt__": (
+                Interrupt(
+                    value="a",
+                    resumable=True,
+                    ns=[
+                        AnyStr("child_graph:"),
+                        AnyStr("get_human_input:"),
+                    ],
+                ),
+            )
+        },
+        {
+            "__interrupt__": (
+                Interrupt(
+                    value="b",
+                    resumable=True,
+                    ns=[
+                        AnyStr("child_graph:"),
+                        AnyStr("get_human_input:"),
+                    ],
+                ),
+            )
+        },
+    )
+    assert events[2] in (
+        UnsortedSequence(
+            {
+                "__interrupt__": (
+                    Interrupt(
+                        value="a",
+                        resumable=True,
+                        ns=[
+                            AnyStr("child_graph:"),
+                            AnyStr("get_human_input:"),
+                        ],
+                    ),
+                )
+            },
+            {"child_graph": {"human_inputs": ["Resume #1"]}},
+        ),
+        UnsortedSequence(
+            {
+                "__interrupt__": (
+                    Interrupt(
+                        value="b",
+                        resumable=True,
+                        ns=[
+                            AnyStr("child_graph:"),
+                            AnyStr("get_human_input:"),
+                        ],
+                    ),
+                )
+            },
+            {"child_graph": {"human_inputs": ["Resume #1"]}},
+        ),
+    )
+    assert events[3] == UnsortedSequence(
+        {
+            "child_graph": {"human_inputs": ["Resume #1"]},
+            "__metadata__": {"cached": True},
+        },
+        {"child_graph": {"human_inputs": ["Resume #2"]}},
+        {"cleanup": None},
+    )
+
+
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
+def test_parallel_interrupts_double(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    from pydantic import BaseModel, Field
+
+    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
+
+    # --- CHILD GRAPH ---
+
+    class ChildState(BaseModel):
+        prompt: str = Field(..., description="What is going to be asked to the user?")
+        human_input: Optional[str] = Field(None, description="What the human said")
+        human_inputs: Annotated[List[str], operator.add] = Field(
+            default_factory=list, description="All of my messages"
+        )
+
+    def get_human_input(state: ChildState):
+        human_input = interrupt(state.prompt)
+
+        return dict(
+            human_inputs=[human_input],  # update parent state
+        )
+
+    def get_dolphin_input(state: ChildState):
+        human_input = interrupt(state.prompt)
+
+        return dict(
+            human_inputs=[human_input],  # update parent state
+        )
+
+    child_graph_builder = StateGraph(ChildState)
+    child_graph_builder.add_node("get_human_input", get_human_input)
+    child_graph_builder.add_node("get_dolphin_input", get_dolphin_input)
+    child_graph_builder.add_edge(START, "get_human_input")
+    child_graph_builder.add_edge(START, "get_dolphin_input")
+    child_graph = child_graph_builder.compile()
+
+    # --- PARENT GRAPH ---
+
+    class ParentState(BaseModel):
+        prompts: List[str] = Field(
+            ..., description="What is going to be asked to the user?"
+        )
+        human_inputs: Annotated[List[str], operator.add] = Field(
+            default_factory=list, description="All of my messages"
+        )
+
+    def assign_workers(state: ParentState):
+        return [
+            Send(
+                "child_graph",
+                dict(
+                    prompt=prompt,
+                ),
+            )
+            for prompt in state.prompts
+        ]
+
+    def cleanup(state: ParentState):
+        assert len(state.human_inputs) == len(state.prompts) * 2
+
+    parent_graph_builder = StateGraph(ParentState)
+    parent_graph_builder.add_node("child_graph", child_graph)
+    parent_graph_builder.add_node("cleanup", cleanup)
+
+    parent_graph_builder.add_conditional_edges(START, assign_workers, ["child_graph"])
+    parent_graph_builder.add_edge("child_graph", "cleanup")
+    parent_graph_builder.add_edge("cleanup", END)
+
+    parent_graph = parent_graph_builder.compile(checkpointer=checkpointer)
+
+    # --- CLIENT INVOCATION ---
+
+    thread_config = dict(
+        configurable=dict(
+            thread_id=str(uuid.uuid4()),
+        )
+    )
+    current_input = dict(
+        prompts=["a", "b"],
+    )
+
+    invokes = 0
+    events: dict[int, list[dict]] = {}
+    while invokes < 10:
+        # reset interrupt
+        invokes += 1
+        events[invokes] = []
+        current_interrupts: list[Interrupt] = []
+
+        # start / resume the graph
+        for event in parent_graph.stream(
+            input=current_input,
+            config=thread_config,
+            stream_mode="updates",
+        ):
+            events[invokes].append(event)
+            # handle the interrupt
+            if "__interrupt__" in event:
+                current_interrupts.extend(event["__interrupt__"])
+                # assume that it breaks here, because it is an interrupt
+
+        # get human input and resume
+        if any(i.resumable for i in current_interrupts):
+            current_input = Command(resume=f"Resume #{invokes}")
+
+        # not more human input required, must be completed
+        else:
+            break
+    else:
+        assert False, "Detected infinite loop"
+
+    assert invokes == 5
+    assert len(events) == 5
+
+
+def test_pregel_loop_refcount():
+    gc.collect()
+    try:
+        gc.disable()
+
+        class State(TypedDict):
+            messages: Annotated[list, add_messages]
+
+        graph_builder = StateGraph(State)
+
+        def chatbot(state: State):
+            return {"messages": [("ai", "HIYA")]}
+
+        graph_builder.add_node("chatbot", chatbot)
+        graph_builder.set_entry_point("chatbot")
+        graph_builder.set_finish_point("chatbot")
+        graph = graph_builder.compile()
+
+        for _ in range(5):
+            graph.invoke({"messages": [{"role": "user", "content": "hi"}]})
+            assert (
+                len(
+                    [obj for obj in gc.get_objects() if isinstance(obj, SyncPregelLoop)]
+                )
+                == 0
+            )
+            assert (
+                len([obj for obj in gc.get_objects() if isinstance(obj, PregelRunner)])
+                == 0
+            )
+    finally:
+        gc.enable()
+
+
+@pytest.mark.parametrize("checkpointer_name", REGULAR_CHECKPOINTERS_SYNC)
+def test_bulk_state_updates(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
+
+    class State(TypedDict):
+        foo: str
+        baz: str
+
+    def node_a(state: State) -> State:
+        return {"foo": "bar"}
+
+    def node_b(state: State) -> State:
+        return {"baz": "qux"}
+
+    graph = (
+        StateGraph(State)
+        .add_node("node_a", node_a)
+        .add_node("node_b", node_b)
+        .add_edge(START, "node_a")
+        .add_edge("node_a", "node_b")
+        .compile(checkpointer=checkpointer)
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # First update with node_a
+    graph.bulk_update_state(
+        config,
+        [
+            [
+                StateUpdate(values={"foo": "bar"}, as_node="node_a"),
+            ]
+        ],
+    )
+
+    # Then bulk update with both nodes
+    graph.bulk_update_state(
+        config,
+        [
+            [
+                StateUpdate(values={"foo": "updated"}, as_node="node_a"),
+                StateUpdate(values={"baz": "new"}, as_node="node_b"),
+            ]
+        ],
+    )
+
+    state = graph.get_state(config)
+    assert state.values == {"foo": "updated", "baz": "new"}
+
+    # Check if there are only two checkpoints
+    checkpoints = list(checkpointer.list(config))
+    assert len(checkpoints) == 2
+    assert checkpoints[0].metadata["writes"] == {
+        "node_a": {"foo": "updated"},
+        "node_b": {"baz": "new"},
+    }
+    assert checkpoints[1].metadata["writes"] == {"node_a": {"foo": "bar"}}
+
+    # perform multiple steps at the same time
+    config = {"configurable": {"thread_id": "2"}}
+
+    graph.bulk_update_state(
+        config,
+        [
+            [
+                StateUpdate(values={"foo": "bar"}, as_node="node_a"),
+            ],
+            [
+                StateUpdate(values={"foo": "updated"}, as_node="node_a"),
+                StateUpdate(values={"baz": "new"}, as_node="node_b"),
+            ],
+        ],
+    )
+
+    state = graph.get_state(config)
+    assert state.values == {"foo": "updated", "baz": "new"}
+
+    checkpoints = list(checkpointer.list(config))
+    assert len(checkpoints) == 2
+    assert checkpoints[0].metadata["writes"] == {
+        "node_a": {"foo": "updated"},
+        "node_b": {"baz": "new"},
+    }
+    assert checkpoints[1].metadata["writes"] == {"node_a": {"foo": "bar"}}
+
+    # Should raise error if updating without as_node
+    with pytest.raises(InvalidUpdateError):
+        graph.bulk_update_state(
+            config,
+            [
+                [
+                    StateUpdate(values={"foo": "error"}, as_node=None),
+                    StateUpdate(values={"bar": "error"}, as_node=None),
+                ]
+            ],
+        )
+
+    # Should raise if no updates are provided
+    with pytest.raises(ValueError, match="No supersteps provided"):
+        graph.bulk_update_state(config, [])
+
+    # Should raise if no updates are provided
+    with pytest.raises(ValueError, match="No updates provided"):
+        graph.bulk_update_state(config, [[], []])
+
+    # Should raise if __end__ or __copy__ update is applied in bulk
+    with pytest.raises(InvalidUpdateError):
+        graph.bulk_update_state(
+            config,
+            [
+                [
+                    StateUpdate(values=None, as_node="__end__"),
+                    StateUpdate(values=None, as_node="__copy__"),
+                ],
+            ],
+        )
+
+
+@pytest.mark.parametrize("checkpointer_name", REGULAR_CHECKPOINTERS_SYNC)
+def test_update_as_input(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
+
+    class State(TypedDict):
+        foo: str
+
+    def agent(state: State) -> State:
+        return {"foo": "agent"}
+
+    def tool(state: State) -> State:
+        return {"foo": "tool"}
+
+    graph = (
+        StateGraph(State)
+        .add_node("agent", agent)
+        .add_node("tool", tool)
+        .add_edge(START, "agent")
+        .add_edge("agent", "tool")
+        .compile(checkpointer=checkpointer)
+    )
+
+    assert graph.invoke({"foo": "input"}, {"configurable": {"thread_id": "1"}}) == {
+        "foo": "tool"
+    }
+
+    assert graph.invoke({"foo": "input"}, {"configurable": {"thread_id": "1"}}) == {
+        "foo": "tool"
+    }
+
+    def map_snapshot(i: StateSnapshot) -> dict:
+        return {
+            "values": i.values,
+            "next": i.next,
+            "step": i.metadata.get("step"),
+        }
+
+    history = [
+        map_snapshot(s)
+        for s in graph.get_state_history({"configurable": {"thread_id": "1"}})
+    ]
+
+    graph.bulk_update_state(
+        {"configurable": {"thread_id": "2"}},
+        [
+            # First turn
+            [StateUpdate({"foo": "input"}, "__input__")],
+            [StateUpdate({"foo": "input"}, "__start__")],
+            [StateUpdate({"foo": "agent"}, "agent")],
+            [StateUpdate({"foo": "tool"}, "tool")],
+            # Second turn
+            [StateUpdate({"foo": "input"}, "__input__")],
+            [StateUpdate({"foo": "input"}, "__start__")],
+            [StateUpdate({"foo": "agent"}, "agent")],
+            [StateUpdate({"foo": "tool"}, "tool")],
+        ],
+    )
+
+    state = graph.get_state({"configurable": {"thread_id": "2"}})
+    assert state.values == {"foo": "tool"}
+
+    new_history = [
+        map_snapshot(s)
+        for s in graph.get_state_history({"configurable": {"thread_id": "2"}})
+    ]
+
+    assert new_history == history
+
+
+@pytest.mark.parametrize("checkpointer_name", REGULAR_CHECKPOINTERS_SYNC)
+def test_batch_update_as_input(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
+
+    class State(TypedDict):
+        foo: str
+        tasks: Annotated[list[int], operator.add]
+
+    def agent(state: State) -> State:
+        return {"foo": "agent"}
+
+    def map(state: State) -> Command["task"]:
+        return Command(
+            goto=[
+                Send("task", {"index": 0}),
+                Send("task", {"index": 1}),
+                Send("task", {"index": 2}),
+            ],
+            update={"foo": "map"},
+        )
+
+    def task(state: dict) -> State:
+        return {"tasks": [state["index"]]}
+
+    graph = (
+        StateGraph(State)
+        .add_node("agent", agent)
+        .add_node("map", map)
+        .add_node("task", task)
+        .add_edge(START, "agent")
+        .add_edge("agent", "map")
+        .compile(checkpointer=checkpointer)
+    )
+
+    assert graph.invoke({"foo": "input"}, {"configurable": {"thread_id": "1"}}) == {
+        "foo": "map",
+        "tasks": [0, 1, 2],
+    }
+
+    def map_snapshot(i: StateSnapshot) -> dict:
+        return {
+            "values": i.values,
+            "next": i.next,
+            "step": i.metadata.get("step"),
+            "tasks": [t.name for t in i.tasks],
+        }
+
+    history = [
+        map_snapshot(s)
+        for s in graph.get_state_history({"configurable": {"thread_id": "1"}})
+    ]
+
+    graph.bulk_update_state(
+        {"configurable": {"thread_id": "2"}},
+        [
+            [StateUpdate({"foo": "input"}, "__input__")],
+            [StateUpdate({"foo": "input"}, "__start__")],
+            [StateUpdate({"foo": "agent", "tasks": []}, "agent")],
+            [
+                StateUpdate(
+                    Command(
+                        goto=[
+                            Send("task", {"index": 0}),
+                            Send("task", {"index": 1}),
+                            Send("task", {"index": 2}),
+                        ],
+                        update={"foo": "map"},
+                    ),
+                    "map",
+                )
+            ],
+            [
+                StateUpdate({"tasks": [0]}, "task"),
+                StateUpdate({"tasks": [1]}, "task"),
+                StateUpdate({"tasks": [2]}, "task"),
+            ],
+        ],
+    )
+
+    state = graph.get_state({"configurable": {"thread_id": "2"}})
+    assert state.values == {"foo": "map", "tasks": [0, 1, 2]}
+
+    new_history = [
+        map_snapshot(s)
+        for s in graph.get_state_history({"configurable": {"thread_id": "2"}})
+    ]
+
+    assert new_history == history

libs/langgraph/tests/test_pregel_async.py

@@ -1,5 +1,7 @@
 import asyncio
+import enum
 import functools
+import gc
 import logging
 import operator
 import random
@@ -27,11 +29,7 @@ from uuid import UUID
 import httpx
 import pytest
 from langchain_core.language_models import GenericFakeChatModel
-from langchain_core.runnables import (
-    RunnableConfig,
-    RunnableLambda,
-    RunnablePassthrough,
-)
+from langchain_core.runnables import RunnableConfig, RunnableLambda, RunnablePassthrough
 from langchain_core.utils.aiter import aclosing
 from pytest_mock import MockerFixture
 from syrupy import SnapshotAssertion
@@ -56,13 +54,16 @@ from langgraph.graph import END, Graph, StateGraph
 from langgraph.graph.message import MessagesState, add_messages
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.pregel import Channel, GraphRecursionError, Pregel, StateSnapshot
+from langgraph.pregel.loop import AsyncPregelLoop
 from langgraph.pregel.retry import RetryPolicy
+from langgraph.pregel.runner import PregelRunner
 from langgraph.store.base import BaseStore
 from langgraph.types import (
     Command,
     Interrupt,
     PregelTask,
     Send,
+    StateUpdate,
     StreamWriter,
     interrupt,
 )
@@ -77,10 +78,7 @@ from tests.conftest import (
     awith_store,
 )
 from tests.fake_tracer import FakeTracer
-from tests.memory_assert import (
-    MemorySaverAssertCheckpointMetadata,
-    MemorySaverNoPending,
-)
+from tests.memory_assert import MemorySaverNoPending
 from tests.messages import (
     _AnyIdAIMessage,
     _AnyIdAIMessageChunk,
@@ -938,10 +936,7 @@ async def test_copy_checkpoint(checkpointer_name: str) -> None:
             async for c in tool_two.astream(
                 {"my_key": "value ⛰️", "market": "DE"}, thread2
             )
-        ] == [
-            {
-                "tool_one": {"my_key": " one"},
-            },
+        ] == UnsortedSequence(
             {
                 "__interrupt__": (
                     Interrupt(
@@ -951,7 +946,10 @@ async def test_copy_checkpoint(checkpointer_name: str) -> None:
                     ),
                 )
             },
-        ]
+            {
+                "tool_one": {"my_key": " one"},
+            },
+        )
         # resume with answer
         assert [
             c async for c in tool_two.astream(Command(resume=" my answer"), thread2)
@@ -1672,7 +1670,7 @@ async def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None:
                 "id": AnyStr(),
                 "name": "one",
                 "input": 2,
-                "triggers": ["input"],
+                "triggers": ("input",),
             },
         },
         {
@@ -1683,7 +1681,7 @@ async def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None:
                 "id": AnyStr(),
                 "name": "two",
                 "input": [12],
-                "triggers": ["inbox"],
+                "triggers": ("inbox",),
             },
         },
         {
@@ -1718,7 +1716,7 @@ async def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None:
                 "id": AnyStr(),
                 "name": "two",
                 "input": [3],
-                "triggers": ["inbox"],
+                "triggers": ("inbox",),
             },
         },
         {
@@ -2072,7 +2070,7 @@ async def test_pending_writes_resume(
                 }
             },
             checkpoint={
-                "v": 1,
+                "v": 2,
                 "id": AnyStr(),
                 "ts": AnyStr(),
                 "pending_sends": [],
@@ -2134,7 +2132,7 @@ async def test_pending_writes_resume(
                 }
             },
             checkpoint={
-                "v": 1,
+                "v": 2,
                 "id": AnyStr(),
                 "ts": AnyStr(),
                 "pending_sends": [],
@@ -2189,7 +2187,7 @@ async def test_pending_writes_resume(
                 }
             },
             checkpoint={
-                "v": 1,
+                "v": 2,
                 "id": AnyStr(),
                 "ts": AnyStr(),
                 "pending_sends": [],
@@ -4524,6 +4522,7 @@ async def test_nested_pydantic_models(version: str) -> None:
     class NestedModel(BaseModel):
         value: int
         name: str
+        something: Optional[str] = None
 
     # Forward reference model
     class RecursiveModel(BaseModel):
@@ -4545,18 +4544,33 @@ async def test_nested_pydantic_models(version: str) -> None:
         name: str
         friends: list[str] = Field(default_factory=list)  # IDs of friends
 
+    class MyEnum(enum.Enum):
+        A = 1
+        B = 2
+
+    class MyTypedDict(TypedDict):
+        x: int
+        my_enum: MyEnum
+
     class State(BaseModel):
         # Basic nested model tests
         top_level: str
         nested: NestedModel
         optional_nested: Optional[NestedModel] = None
         dict_nested: dict[str, NestedModel]
+        my_set: set[int]
+        my_enum: MyEnum
         list_nested: Annotated[
             Union[dict, list[dict[str, NestedModel]]], lambda x, y: (x or []) + [y]
         ]
+        list_nested_reversed: Annotated[
+            Union[list[dict[str, NestedModel]], NestedModel, dict, list],
+            lambda x, y: (x or []) + [y],
+        ]
         tuple_nested: tuple[str, NestedModel]
         tuple_list_nested: list[tuple[int, NestedModel]]
         complex_tuple: tuple[str, dict[str, tuple[int, NestedModel]]]
+        my_typed_dict: MyTypedDict
 
         # Forward reference test
         recursive: RecursiveModel
@@ -4572,8 +4586,12 @@ async def test_nested_pydantic_models(version: str) -> None:
         "top_level": "initial",
         "nested": {"value": 42, "name": "test"},
         "optional_nested": {"value": 10, "name": "optional"},
+        "my_set": [1, 2, 7],
+        "my_enum": MyEnum.B,
+        "my_typed_dict": {"x": 1, "my_enum": MyEnum.A},
         "dict_nested": {"a": {"value": 5, "name": "a"}},
         "list_nested": [{"a": {"value": 6, "name": "b"}}],
+        "list_nested_reversed": ["foo", "bar"],
         "tuple_nested": ["tuple-key", {"value": 7, "name": "tuple-value"}],
         "tuple_list_nested": [[1, {"value": 8, "name": "tuple-in-list"}]],
         "complex_tuple": [
@@ -5749,11 +5767,11 @@ async def test_checkpoint_metadata() -> None:
     workflow.add_edge("tools", "agent")
 
     # graph w/o interrupt
-    checkpointer_1 = MemorySaverAssertCheckpointMetadata()
+    checkpointer_1 = InMemorySaver()
     app = workflow.compile(checkpointer=checkpointer_1)
 
     # graph w/ interrupt
-    checkpointer_2 = MemorySaverAssertCheckpointMetadata()
+    checkpointer_2 = InMemorySaver()
     app_w_interrupt = workflow.compile(
         checkpointer=checkpointer_2, interrupt_before=["tools"]
     )
@@ -5882,10 +5900,12 @@ async def test_store_injected_async(checkpointer_name: str, store_name: str) ->
         ):
             assert isinstance(store, BaseStore)
             await store.aput(
-                namespace
-                if self.i is not None
-                and config["configurable"]["thread_id"] in (thread_1, thread_2)
-                else (f"foo_{self.i}", "bar"),
+                (
+                    namespace
+                    if self.i is not None
+                    and config["configurable"]["thread_id"] in (thread_1, thread_2)
+                    else (f"foo_{self.i}", "bar")
+                ),
                 doc_id,
                 {
                     **doc,
@@ -6997,6 +7017,8 @@ async def test_double_interrupt_subgraph(checkpointer_name: str) -> None:
         def invoke_sub_agent(state: AgentState):
             return subgraph.invoke(state)
 
+        thread = {"configurable": {"thread_id": str(uuid.uuid4())}}
+
         parent_agent = (
             StateGraph(AgentState)
             .add_node("invoke_sub_agent", invoke_sub_agent)
@@ -7571,7 +7593,10 @@ async def test_tags_stream_mode_messages() -> None:
             {
                 "langgraph_step": 1,
                 "langgraph_node": "call_model",
-                "langgraph_triggers": ["start:call_model"],
+                "langgraph_triggers": (
+                    "branch:to:call_model",
+                    "start:call_model",
+                ),
                 "langgraph_path": ("__pregel_pull", "call_model"),
                 "langgraph_checkpoint_ns": AnyStr("call_model:"),
                 "checkpoint_ns": AnyStr("call_model:"),
@@ -7827,3 +7852,329 @@ async def test_handles_multiple_interrupts_from_tasks() -> None:
     assert len(result) == 2
     assert result[0] == "Added James!"
     assert result[1] == "Added Will!"
+
+
+async def test_pregel_loop_refcount():
+    gc.collect()
+    try:
+        gc.disable()
+
+        class State(TypedDict):
+            messages: Annotated[list, add_messages]
+
+        graph_builder = StateGraph(State)
+
+        async def chatbot(state: State):
+            return {"messages": [("ai", "HIYA")]}
+
+        graph_builder.add_node("chatbot", chatbot)
+        graph_builder.set_entry_point("chatbot")
+        graph_builder.set_finish_point("chatbot")
+        graph = graph_builder.compile()
+
+        for _ in range(5):
+            await graph.ainvoke({"messages": [{"role": "user", "content": "hi"}]})
+            assert (
+                len(
+                    [
+                        obj
+                        for obj in gc.get_objects()
+                        if isinstance(obj, AsyncPregelLoop)
+                    ]
+                )
+                == 0
+            )
+            assert (
+                len([obj for obj in gc.get_objects() if isinstance(obj, PregelRunner)])
+                == 0
+            )
+    finally:
+        gc.enable()
+
+
+@pytest.mark.parametrize("checkpointer_name", REGULAR_CHECKPOINTERS_ASYNC)
+async def test_bulk_state_updates(checkpointer_name: str) -> None:
+    async with awith_checkpointer(checkpointer_name) as checkpointer:
+
+        class State(TypedDict):
+            foo: str
+            baz: str
+
+        def node_a(state: State) -> State:
+            return {"foo": "bar"}
+
+        def node_b(state: State) -> State:
+            return {"baz": "qux"}
+
+        graph = (
+            StateGraph(State)
+            .add_node("node_a", node_a)
+            .add_node("node_b", node_b)
+            .add_edge(START, "node_a")
+            .add_edge("node_a", "node_b")
+            .compile(checkpointer=checkpointer)
+        )
+
+        config = {"configurable": {"thread_id": "1"}}
+
+        # First update with node_a
+        await graph.abulk_update_state(
+            config,
+            [
+                [
+                    StateUpdate({"foo": "bar"}, "node_a"),
+                ]
+            ],
+        )
+
+        # Then bulk update with both nodes
+        await graph.abulk_update_state(
+            config,
+            [
+                [
+                    StateUpdate({"foo": "updated"}, "node_a"),
+                    StateUpdate({"baz": "new"}, "node_b"),
+                ]
+            ],
+        )
+
+        state = await graph.aget_state(config)
+        assert state.values == {"foo": "updated", "baz": "new"}
+
+        # Check if there are only two checkpoints
+        checkpoints = [
+            c async for c in checkpointer.alist({"configurable": {"thread_id": "1"}})
+        ]
+        assert len(checkpoints) == 2
+        assert checkpoints[0].metadata["writes"] == {
+            "node_a": {"foo": "updated"},
+            "node_b": {"baz": "new"},
+        }
+        assert checkpoints[1].metadata["writes"] == {"node_a": {"foo": "bar"}}
+
+        # perform multiple steps at the same time
+        config = {"configurable": {"thread_id": "2"}}
+
+        await graph.abulk_update_state(
+            config,
+            [
+                [
+                    StateUpdate({"foo": "bar"}, "node_a"),
+                ],
+                [
+                    StateUpdate({"foo": "updated"}, "node_a"),
+                    StateUpdate({"baz": "new"}, "node_b"),
+                ],
+            ],
+        )
+
+        state = await graph.aget_state(config)
+        assert state.values == {"foo": "updated", "baz": "new"}
+
+        checkpoints = [
+            c async for c in checkpointer.alist({"configurable": {"thread_id": "1"}})
+        ]
+        assert len(checkpoints) == 2
+        assert checkpoints[0].metadata["writes"] == {
+            "node_a": {"foo": "updated"},
+            "node_b": {"baz": "new"},
+        }
+        assert checkpoints[1].metadata["writes"] == {"node_a": {"foo": "bar"}}
+
+        # Should raise error if updating without as_node
+        with pytest.raises(InvalidUpdateError):
+            await graph.abulk_update_state(
+                config,
+                [
+                    [
+                        StateUpdate(values={"foo": "error"}, as_node=None),
+                        StateUpdate(values={"bar": "error"}, as_node=None),
+                    ]
+                ],
+            )
+
+        # Should raise if no updates are provided
+        with pytest.raises(ValueError, match="No supersteps provided"):
+            await graph.abulk_update_state(config, [])
+
+        # Should raise if no updates are provided
+        with pytest.raises(ValueError, match="No updates provided"):
+            await graph.abulk_update_state(config, [[], []])
+
+        # Should raise if __end__ or __copy__ update is applied in bulk
+        with pytest.raises(InvalidUpdateError):
+            await graph.abulk_update_state(
+                config,
+                [
+                    [
+                        StateUpdate(values=None, as_node="__end__"),
+                        StateUpdate(values=None, as_node="__copy__"),
+                    ],
+                ],
+            )
+
+
+@pytest.mark.parametrize("checkpointer_name", REGULAR_CHECKPOINTERS_ASYNC)
+async def test_update_as_input(checkpointer_name: str) -> None:
+    async with awith_checkpointer(checkpointer_name) as checkpointer:
+
+        class State(TypedDict):
+            foo: str
+
+        def agent(state: State) -> State:
+            return {"foo": "agent"}
+
+        def tool(state: State) -> State:
+            return {"foo": "tool"}
+
+        graph = (
+            StateGraph(State)
+            .add_node("agent", agent)
+            .add_node("tool", tool)
+            .add_edge(START, "agent")
+            .add_edge("agent", "tool")
+            .compile(checkpointer=checkpointer)
+        )
+
+        assert await graph.ainvoke(
+            {"foo": "input"}, {"configurable": {"thread_id": "1"}}
+        ) == {"foo": "tool"}
+
+        assert await graph.ainvoke(
+            {"foo": "input"}, {"configurable": {"thread_id": "1"}}
+        ) == {"foo": "tool"}
+
+        def map_snapshot(i: StateSnapshot) -> dict:
+            return {
+                "values": i.values,
+                "next": i.next,
+                "step": i.metadata.get("step"),
+            }
+
+        history = [
+            map_snapshot(s)
+            async for s in graph.aget_state_history(
+                {"configurable": {"thread_id": "1"}}
+            )
+        ]
+
+        await graph.abulk_update_state(
+            {"configurable": {"thread_id": "2"}},
+            [
+                # First turn
+                [StateUpdate({"foo": "input"}, "__input__")],
+                [StateUpdate({"foo": "input"}, "__start__")],
+                [StateUpdate({"foo": "agent"}, "agent")],
+                [StateUpdate({"foo": "tool"}, "tool")],
+                # Second turn
+                [StateUpdate({"foo": "input"}, "__input__")],
+                [StateUpdate({"foo": "input"}, "__start__")],
+                [StateUpdate({"foo": "agent"}, "agent")],
+                [StateUpdate({"foo": "tool"}, "tool")],
+            ],
+        )
+
+        state = await graph.aget_state({"configurable": {"thread_id": "2"}})
+        assert state.values == {"foo": "tool"}
+
+        new_history = [
+            map_snapshot(s)
+            async for s in graph.aget_state_history(
+                {"configurable": {"thread_id": "2"}}
+            )
+        ]
+
+        assert new_history == history
+
+
+@pytest.mark.parametrize("checkpointer_name", REGULAR_CHECKPOINTERS_ASYNC)
+async def test_batch_update_as_input(checkpointer_name: str) -> None:
+    async with awith_checkpointer(checkpointer_name) as checkpointer:
+
+        class State(TypedDict):
+            foo: str
+            tasks: Annotated[list[int], operator.add]
+
+        def agent(state: State) -> State:
+            return {"foo": "agent"}
+
+        def map(state: State) -> Command["task"]:
+            return Command(
+                goto=[
+                    Send("task", {"index": 0}),
+                    Send("task", {"index": 1}),
+                    Send("task", {"index": 2}),
+                ],
+                update={"foo": "map"},
+            )
+
+        def task(state: dict) -> State:
+            return {"tasks": [state["index"]]}
+
+        graph = (
+            StateGraph(State)
+            .add_node("agent", agent)
+            .add_node("map", map)
+            .add_node("task", task)
+            .add_edge(START, "agent")
+            .add_edge("agent", "map")
+            .compile(checkpointer=checkpointer)
+        )
+
+        assert await graph.ainvoke(
+            {"foo": "input"}, {"configurable": {"thread_id": "1"}}
+        ) == {"foo": "map", "tasks": [0, 1, 2]}
+
+        def map_snapshot(i: StateSnapshot) -> dict:
+            return {
+                "values": i.values,
+                "next": i.next,
+                "step": i.metadata.get("step"),
+                "tasks": [t.name for t in i.tasks],
+            }
+
+        history = [
+            map_snapshot(s)
+            async for s in graph.aget_state_history(
+                {"configurable": {"thread_id": "1"}}
+            )
+        ]
+
+        await graph.abulk_update_state(
+            {"configurable": {"thread_id": "2"}},
+            [
+                [StateUpdate({"foo": "input"}, "__input__")],
+                [StateUpdate({"foo": "input"}, "__start__")],
+                [StateUpdate({"foo": "agent", "tasks": []}, "agent")],
+                [
+                    StateUpdate(
+                        Command(
+                            goto=[
+                                Send("task", {"index": 0}),
+                                Send("task", {"index": 1}),
+                                Send("task", {"index": 2}),
+                            ],
+                            update={"foo": "map"},
+                        ),
+                        "map",
+                    )
+                ],
+                [
+                    StateUpdate({"tasks": [0]}, "task"),
+                    StateUpdate({"tasks": [1]}, "task"),
+                    StateUpdate({"tasks": [2]}, "task"),
+                ],
+            ],
+        )
+
+        state = await graph.aget_state({"configurable": {"thread_id": "2"}})
+        assert state.values == {"foo": "map", "tasks": [0, 1, 2]}
+
+        new_history = [
+            map_snapshot(s)
+            async for s in graph.aget_state_history(
+                {"configurable": {"thread_id": "2"}}
+            )
+        ]
+
+        assert new_history == history