prebuilt: release 0.1.4 (#3977)

- This makes our checkpoint benchmarks more closely resemble the
behavior of our prod checkpointers
- Also found and fixed a bug w multiple subgraphs in same node
accidentally sharing checkpoints

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: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
-  <source media="(prefers-color-scheme: dark)" srcset="docs/docs/static/wordmark_light.svg">
+  <source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
-  <img alt="LangGraph Logo" src="docs/docs/static/wordmark_dark.svg" width="80%">
+  <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/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/langgraph_platform.md

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

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/state-model.ipynb

@@ -463,12 +463,12 @@
    "source": [
     "from langgraph.graph import StateGraph, START, END\n",
     "from pydantic import BaseModel\n",
-    "from langchain_core.messages import HumanMessage, AIMessage, BaseMessage\n",
+    "from langchain_core.messages import HumanMessage, AIMessage, AnyMessage\n",
     "from typing import List\n",
     "\n",
     "\n",
     "class ChatState(BaseModel):\n",
-    "    messages: List[BaseMessage]\n",
+    "    messages: List[AnyMessage]\n",
     "    context: str\n",
     "\n",
     "\n",

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/tutorials/deployment.md

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

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

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 = "2.0.18"
+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/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"]
+        thread_id: str = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        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/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.memory_saver.put(self.config_2, self.chkpnt_2, self.metadata_2, {})
+            self.config_1,
-        self.memory_saver.put(self.config_3, self.chkpnt_3, self.metadata_3, {})
+            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.memory_saver.put(self.config_2, self.chkpnt_2, self.metadata_2, {})
+            self.config_1,
-        self.memory_saver.put(self.config_3, self.chkpnt_3, self.metadata_3, {})
+            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-0.0.32-py3-none-any.whl", hash = "sha256:7990cedc65f784813aba867c5bde3fdfae3fa4588baef1aa346cbeac7c3aebf1"},
-    {file = "langgraph_api-0.0.27.tar.gz", hash = "sha256:c21eb2b7fe3b93998379f7b13ad7d23b3ef06ab821b008c6b12b954acfb587ec"},
+    {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-checkpoint = ">=2.0.21,<3.0"
-langgraph-sdk = ">=0.1.53,<0.2.0"
+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-2.0.21-py3-none-any.whl", hash = "sha256:ca89c2090cd9729f83f9782226935dc5ff9fe7756c24936f484ccb0ce367f87b"},
-    {file = "langgraph_checkpoint-2.0.16.tar.gz", hash = "sha256:49ba8cfa12b2aae845ccc3b1fbd1d7a8d3a6c4a2e387ab3a92fca40dd3d4baa5"},
+    {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-0.1.58-py3-none-any.whl", hash = "sha256:65f88cf5582da0c316714dc475126fa03c5f74d72bc0b9221dd42649de8e23d4"},
-    {file = "langgraph_sdk-0.1.53.tar.gz", hash = "sha256:12906ed965905fa27e0c28d9fa07dc6fd89e6895ff321ff049fdf3965d057cc4"},
+    {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-25.2.0-py3-none-any.whl", hash = "sha256:0fecea2e345d5d491b72f3db2e5fcd6393abfc8cd06a4851f21fcd4d1a99f437"},
-    {file = "structlog-23.3.0.tar.gz", hash = "sha256:24b42b914ac6bc4a4e6f716e82ac70d7fb1e8c3b1035a765591953bfc37101a5"},
+    {file = "structlog-25.2.0.tar.gz", hash = "sha256:d9f9776944207d1035b8b26072b9b140c63702fd7aa57c2f85d28ab701bd8e92"},
 ]
 
 [package.extras]
-dev = ["structlog[tests,typing]"]
+dev = ["freezegun (>=0.2.8)", "mypy (>=1.4)", "pretend", "pytest (>=6.0)", "pytest-asyncio (>=0.17)", "rich", "simplejson", "twisted"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-mermaid", "sphinxext-opengraph", "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 = "0.1.78"
 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/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: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
-  <source media="(prefers-color-scheme: dark)" srcset="docs/docs/static/wordmark_light.svg">
+  <source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
-  <img alt="LangGraph Logo" src="docs/docs/static/wordmark_dark.svg" width="80%">
+  <img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="80%">
 </picture>
 
 <div>

libs/langgraph/bench/__main__.py

@@ -6,10 +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
 
 
@@ -43,6 +45,11 @@ def run(graph: Pregel, input: dict):
     )
 
 
+def compile_graph(graph: StateGraph) -> None:
+    """Compile the graph."""
+    graph.compile()
+
+
 benchmarks = (
     (
         "fanout_to_subgraph_10x",
@@ -228,12 +235,144 @@ benchmarks = (
         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)
+
+# 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/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/state.py

@@ -859,8 +859,10 @@ class CompiledStateGraph(CompiledGraph):
                 # subscribe to channel
                 self.nodes[end].triggers.append(channel_name)
                 # publish to channel
-                self.nodes[START] |= ChannelWrite(
+                self.nodes[START].writers.append(
-                    [ChannelWriteEntry(channel_name, START)], tags=[TAG_HIDDEN]
+                    ChannelWrite(
+                        [ChannelWriteEntry(channel_name, START)], tags=[TAG_HIDDEN]
+                    )
                 )
             elif end != END:
                 # subscribe to start channel
@@ -873,8 +875,10 @@ class CompiledStateGraph(CompiledGraph):
             self.nodes[end].triggers.append(channel_name)
             # publish to channel
             for start in starts:
-                self.nodes[start] |= ChannelWrite(
+                self.nodes[start].writers.append(
-                    [ChannelWriteEntry(channel_name, start)], tags=[TAG_HIDDEN]
+                    ChannelWrite(
+                        [ChannelWriteEntry(channel_name, start)], tags=[TAG_HIDDEN]
+                    )
                 )
 
     def attach_branch(
@@ -910,28 +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
+        # attach branch publisher
-        ends = (
+        self.nodes[start].writers.append(
-            branch.ends.values()
+            branch.run(
-            if branch.ends
+                branch_writer,
-            else [node for node in self.builder.nodes if node != branch.then]
+                _get_state_reader(self.builder, schema) if with_reader else None,
+            )
         )
 
         # 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(
+                    self.nodes[end].writers.append(
-                        [ChannelWriteEntry(channel_name, end)], tags=[TAG_HIDDEN]
+                        ChannelWrite(
+                            [ChannelWriteEntry(channel_name, end)], tags=[TAG_HIDDEN]
+                        )
                     )
 
 
@@ -1013,7 +1020,12 @@ 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)
 

libs/langgraph/langgraph/pregel/algo.py

@@ -233,10 +233,21 @@ 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 +323,14 @@ 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].is_available() and chan not in updated_channels:
-                if channels[chan].update([]) and get_next_version is not None:
+                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 +347,8 @@ 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 +366,8 @@ 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 +384,35 @@ 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]] = []
@@ -397,9 +436,30 @@ 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,
@@ -517,7 +577,7 @@ def prepare_single_task(
                         CONFIG_KEY_CHECKPOINT_ID: None,
                         CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
                         CONFIG_KEY_SCRATCHPAD: _scratchpad(
-                            config,
+                            config[CONF].get(CONFIG_KEY_SCRATCHPAD),
                             pending_writes,
                             task_id,
                         ),
@@ -627,7 +687,7 @@ def prepare_single_task(
                             CONFIG_KEY_CHECKPOINT_ID: None,
                             CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
                             CONFIG_KEY_SCRATCHPAD: _scratchpad(
-                                config,
+                                config[CONF].get(CONFIG_KEY_SCRATCHPAD),
                                 pending_writes,
                                 task_id,
                             ),
@@ -655,13 +715,14 @@ def prepare_single_task(
         if checkpoint_null_version is None:
             return
         # If any of the channels read by this process were updated
-        if triggers := _triggers(
+        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)
@@ -721,7 +782,7 @@ def prepare_single_task(
                                 CONFIG_KEY_SEND: partial(
                                     local_write,
                                     writes.extend,
-                                    processes.keys(),
+                                    tuple(processes.keys()),
                                 ),
                                 CONFIG_KEY_READ: partial(
                                     local_read,
@@ -730,7 +791,10 @@ def prepare_single_task(
                                     channels,
                                     managed,
                                     PregelTaskWrites(
-                                        task_path[:3], name, writes, triggers
+                                        task_path[:3],
+                                        name,
+                                        writes,
+                                        triggers,
                                     ),
                                     config,
                                 ),
@@ -748,7 +812,7 @@ def prepare_single_task(
                                 CONFIG_KEY_CHECKPOINT_ID: None,
                                 CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
                                 CONFIG_KEY_SCRATCHPAD: _scratchpad(
-                                    config,
+                                    config[CONF].get(CONFIG_KEY_SCRATCHPAD),
                                     pending_writes,
                                     task_id,
                                 ),
@@ -799,7 +863,7 @@ def _triggers(
 
 
 def _scratchpad(
-    config: RunnableConfig,
+    parent_scratchpad: Optional[PregelScratchpad],
     pending_writes: list[PendingWrite],
     task_id: str,
 ) -> PregelScratchpad:
@@ -808,9 +872,6 @@ def _scratchpad(
     null_resume_write = next(
         (w for w in pending_writes if w[0] == NULL_TASK_ID and w[1] == RESUME), None
     )
-    parent_scratchpad: Optional[PregelScratchpad] = config[CONF].get(
-        CONFIG_KEY_SCRATCHPAD
-    )
 
     def get_null_resume(consume: bool = False) -> Any:
         if null_resume_write is None:

libs/langgraph/langgraph/pregel/loop.py

@@ -1,6 +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
@@ -209,6 +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,
@@ -232,6 +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])
@@ -266,13 +269,13 @@ class PregelLoop(LoopProtocol):
             self.checkpoint_config = patch_configurable(
                 self.config,
                 {
-                    CONFIG_KEY_CHECKPOINT_ID: config[CONF][CONFIG_KEY_CHECKPOINT_MAP][
+                    CONFIG_KEY_CHECKPOINT_ID: self.config[CONF][
-                        self.config[CONF][CONFIG_KEY_CHECKPOINT_NS]
+                        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)
@@ -406,8 +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"
@@ -427,7 +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(),
@@ -493,6 +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 = []
 
@@ -571,11 +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)
@@ -592,6 +599,8 @@ class PregelLoop(LoopProtocol):
                 ),
             )
         )
+        # this can be set only when there are input_writes
+        updated_channels: Optional[set[str]] = None
 
         # map command to writes
         if isinstance(self.input, Command):
@@ -612,7 +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, [])],
@@ -661,7 +670,7 @@ class PregelLoop(LoopProtocol):
                 manager=None,
             )
             # apply input writes
-            mv_writes = apply_writes(
+            mv_writes, updated_channels = apply_writes(
                 self.checkpoint,
                 self.channels,
                 [
@@ -691,6 +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():
@@ -776,7 +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(),
@@ -883,6 +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,
@@ -899,6 +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:
@@ -1024,6 +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,
@@ -1040,6 +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

@@ -149,7 +149,7 @@ class PregelRunner:
                     configurable={
                         CONFIG_KEY_CALL: partial(
                             _call,
-                            t,
+                            weakref.ref(t),
                             retry=retry_policy,
                             futures=weakref.ref(futures),
                             schedule_task=self.schedule_task,
@@ -185,7 +185,7 @@ class PregelRunner:
                     configurable={
                         CONFIG_KEY_CALL: partial(
                             _call,
-                            t,
+                            weakref.ref(t),
                             retry=retry_policy,
                             futures=weakref.ref(futures),
                             schedule_task=self.schedule_task,
@@ -263,7 +263,7 @@ class PregelRunner:
                     configurable={
                         CONFIG_KEY_CALL: partial(
                             _acall,
-                            t,
+                            weakref.ref(t),
                             stream=self.use_astream,
                             retry=retry_policy,
                             futures=weakref.ref(futures),
@@ -304,7 +304,7 @@ class PregelRunner:
                         configurable={
                             CONFIG_KEY_CALL: partial(
                                 _acall,
-                                t,
+                                weakref.ref(t),
                                 retry=retry_policy,
                                 stream=self.use_astream,
                                 futures=weakref.ref(futures),
@@ -469,7 +469,7 @@ def _panic_or_proceed(
 
 
 def _call(
-    task: PregelExecutableTask,
+    task: weakref.ref[PregelExecutableTask],
     func: Callable[[Any], Union[Awaitable[Any], Any]],
     input: Any,
     *,
@@ -489,10 +489,10 @@ def _call(
 
     fut: Optional[concurrent.futures.Future] = None
     # schedule PUSH tasks, collect futures
-    scratchpad: PregelScratchpad = task.config[CONF][CONFIG_KEY_SCRATCHPAD]
+    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,
+        task(),  # type: ignore[arg-type]
         scratchpad.call_counter(),
         Call(func, input, retry=retry, callbacks=callbacks),
     ):
@@ -528,7 +528,7 @@ def _call(
                 configurable={
                     CONFIG_KEY_CALL: partial(
                         _call,
-                        next_task,
+                        weakref.ref(next_task),
                         futures=futures,
                         retry=retry,
                         callbacks=callbacks,
@@ -550,7 +550,7 @@ def _call(
 
 
 def _acall(
-    task: PregelExecutableTask,
+    task: weakref.ref[PregelExecutableTask],
     func: Callable[[Any], Union[Awaitable[Any], Any]],
     input: Any,
     *,
@@ -570,10 +570,10 @@ def _acall(
 ) -> 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]
+    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,
+        task(),  # type: ignore[arg-type]
         scratchpad.call_counter(),
         Call(func, input, retry=retry, callbacks=callbacks),
     ):
@@ -614,7 +614,7 @@ def _acall(
                     configurable={
                         CONFIG_KEY_CALL: partial(
                             _acall,
-                            next_task,
+                            weakref.ref(next_task),
                             stream=stream,
                             futures=futures,
                             schedule_task=schedule_task,
@@ -623,7 +623,7 @@ def _acall(
                             reraise=reraise,
                         ),
                     },
-                    __name__=task.name,
+                    __name__=task().name,  # type: ignore[union-attr]
                     __cancel_on_exit__=True,
                     __reraise_on_exit__=reraise,
                     # starting a new task in the next tick ensures

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/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

@@ -133,6 +133,11 @@ class Interrupt:
     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):
     id: str
     name: str
@@ -143,7 +148,14 @@ 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

libs/langgraph/langgraph/utils/runnable.py

@@ -2,8 +2,8 @@ import asyncio
 import enum
 import inspect
 import sys
-from contextlib import AsyncExitStack
+from contextlib import AsyncExitStack, contextmanager
-from contextvars import copy_context
+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
+def _set_config_context(
-except ImportError:
+    config: RunnableConfig,
-    # For forwards compatibility
+) -> tuple[Token[Optional[RunnableConfig]], Optional[dict[str, Any]]]:
-    def _set_config_context(context: RunnableConfig) -> None:  # type: ignore
+    """Set the child Runnable config + tracing context.
-        """Set the context for the current thread."""
+
-        var_child_runnable_config.set(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
+        if func_accepts_config is not None:
-        # Mapping from kwarg name to (config key, default value) to be used.
+            self.func_accepts_config = func_accepts_config
-        # The default value is used if the config key is not found in the config.
+            self.func_accepts: dict[str, Tuple[str, Any]] = {}
-        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:
+            self.func_accepts_config = "config" in params
-            p = params.get(kw)
+            # 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:
+            for kw, typ, config_key, default in KWARGS_CONFIG_KEYS:
-                # If parameter is not found or is not a valid kind, skip
+                p = params.get(kw)
-                continue
 
-            if typ != (ANY_TYPE,) and p.annotation not in typ:
+                if p is None or p.kind not in VALID_KINDS:
-                # A specific type is required, but the function annotation does
+                    # If parameter is not found or is not a valid kind, skip
-                # not match the expected type.
+                    continue
-                continue
 
-            # If the kwarg is accepted by the function, store the default value
+                if typ != (ANY_TYPE,) and p.annotation not in typ:
-            self.func_accepts[kw] = (config_key, default)
+                    # 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()
+                with set_config_context(child_config) as context:
-                context.run(_set_config_context, child_config)
+                    ret = context.run(self.func, *args, **kwargs)
-                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)
+            with set_config_context(config) as context:
-            ret = context.run(self.func, *args, **kwargs)
+                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)
+                with set_config_context(child_config) as context:
-                coro = cast(Coroutine[None, None, Any], self.afunc(*args, **kwargs))
+                    coro = cast(Coroutine[None, None, Any], self.afunc(*args, **kwargs))
-                if ASYNCIO_ACCEPTS_CONTEXT:
+                    if ASYNCIO_ACCEPTS_CONTEXT:
-                    ret = await asyncio.create_task(coro, context=context)
+                        ret = await asyncio.create_task(coro, context=context)
-                else:
+                    else:
-                    ret = await coro
+                        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)
+            with set_config_context(config) as context:
-            if ASYNCIO_ACCEPTS_CONTEXT:
+                if ASYNCIO_ACCEPTS_CONTEXT:
-                coro = cast(Coroutine[None, None, Any], self.afunc(*args, **kwargs))
+                    coro = cast(Coroutine[None, None, Any], self.afunc(*args, **kwargs))
-                ret = await asyncio.create_task(coro, context=context)
+                    ret = await asyncio.create_task(coro, context=context)
-            else:
+                else:
-                ret = await self.afunc(*args, **kwargs)
+                    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

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
 
 [[package]]
 name = "aiosqlite"
@@ -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-0.3.46-py3-none-any.whl", hash = "sha256:28b5689fc347975ea520b5364ab4aee5567e661553bbee5e97cabf4596c28ce0"},
-    {file = "langchain_core-0.3.44.tar.gz", hash = "sha256:7c0a01e78360f007cbca448178fe7e032404068e6431dbe8ce905f84febbdfa5"},
+    {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"

libs/langgraph/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.3.16"
+version = "0.3.18"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"

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:"),
@@ -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:"),
@@ -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": (AnyStr("retriever_"),),
+                    "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"),
             },
         },
         {
@@ -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,

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:"),
@@ -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:"),
@@ -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": (AnyStr("retriever_"),),
+                    "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",
+                    ),
                 },
             },
             {
@@ -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,

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,
@@ -4209,11 +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"]
     )
@@ -4631,59 +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(
@@ -6290,6 +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)
@@ -6925,7 +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:"),
@@ -7613,3 +7567,320 @@ def test_parallel_interrupts_double(
 
     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
@@ -52,13 +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,
 )
@@ -73,10 +78,7 @@ from tests.conftest import (
     awith_store,
 )
 from tests.fake_tracer import FakeTracer
-from tests.memory_assert import (
+from tests.memory_assert import MemorySaverNoPending
-    MemorySaverAssertCheckpointMetadata,
-    MemorySaverNoPending,
-)
 from tests.messages import (
     _AnyIdAIMessage,
     _AnyIdAIMessageChunk,
@@ -4542,8 +4544,13 @@ 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
@@ -4552,6 +4559,7 @@ async def test_nested_pydantic_models(version: str) -> None:
         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]
         ]
@@ -4579,7 +4587,8 @@ async def test_nested_pydantic_models(version: str) -> None:
         "nested": {"value": 42, "name": "test"},
         "optional_nested": {"value": 10, "name": "optional"},
         "my_set": [1, 2, 7],
-        "my_typed_dict": {"x": 1},
+        "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"],
@@ -5758,11 +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"]
     )
@@ -7008,6 +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)
@@ -7582,7 +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:"),
@@ -7838,3 +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

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -719,9 +719,7 @@ def create_react_agent(
     def generate_structured_response(
         state: StateSchema, config: RunnableConfig
     ) -> StateSchema:
-        # NOTE: we exclude the last message because there is enough information
+        messages = _get_state_value(state, "messages")
-        # for the LLM to generate the structured response
-        messages = _get_state_value(state, "messages")[:-1]
         structured_response_schema = response_format
         if isinstance(response_format, tuple):
             system_prompt, structured_response_schema = response_format
@@ -736,9 +734,7 @@ def create_react_agent(
     async def agenerate_structured_response(
         state: StateSchema, config: RunnableConfig
     ) -> StateSchema:
-        # NOTE: we exclude the last message because there is enough information
+        messages = _get_state_value(state, "messages")
-        # for the LLM to generate the structured response
-        messages = _get_state_value(state, "messages")[:-1]
         structured_response_schema = response_format
         if isinstance(response_format, tuple):
             system_prompt, structured_response_schema = response_format

libs/prebuilt/poetry.lock

@@ -435,7 +435,7 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langgraph"
-version = "0.3.0"
+version = "0.3.18"
 description = "Building stateful, multi-actor applications with LLMs"
 optional = false
 python-versions = ">=3.9.0,<4.0"
@@ -446,6 +446,7 @@ develop = true
 [package.dependencies]
 langchain-core = ">=0.1,<0.4"
 langgraph-checkpoint = "^2.0.10"
+langgraph-prebuilt = ">=0.1.1,<0.2"
 langgraph-sdk = "^0.1.42"
 
 [package.source]
@@ -454,7 +455,7 @@ url = "../langgraph"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.16"
+version = "2.0.21"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -472,7 +473,7 @@ url = "../checkpoint"
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.15"
+version = "2.0.19"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -481,7 +482,7 @@ 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"
@@ -492,7 +493,7 @@ url = "../checkpoint-postgres"
 
 [[package]]
 name = "langgraph-checkpoint-sqlite"
-version = "2.0.5"
+version = "2.0.6"
 description = "Library with a SQLite implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0"

libs/prebuilt/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-prebuilt"
-version = "0.1.3"
+version = "0.1.4"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 license = "MIT"

libs/sdk-js/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/langgraph-sdk",
-  "version": "0.0.57",
+  "version": "0.0.60",
   "description": "Client library for interacting with the LangGraph API",
   "type": "module",
   "packageManager": "yarn@1.22.19",

libs/sdk-js/src/client.ts

@@ -30,6 +30,7 @@ import type {
   StreamEvent,
   CronsCreatePayload,
   OnConflictBehavior,
+  Command,
 } from "./types.js";
 import { mergeSignals } from "./utils/signals.js";
 import { getEnvironmentVariable } from "./utils/env.js";
@@ -481,15 +482,47 @@ export class ThreadsClient<
      * Metadata for the thread.
      */
     metadata?: Metadata;
+    /**
+     * ID of the thread to create.
+     *
+     * If not provided, a random UUID will be generated.
+     */
     threadId?: string;
+    /**
+     * How to handle duplicate creation.
+     *
+     * @default "raise"
+     */
     ifExists?: OnConflictBehavior;
+    /**
+     * Graph ID to associate with the thread.
+     */
+    graphId?: string;
+    /**
+     * Apply a list of supersteps when creating a thread, each containing a sequence of updates.
+     *
+     * Used for copying a thread between deployments.
+     */
+    supersteps?: Array<{
+      updates: Array<{ values: unknown; command?: Command; asNode: string }>;
+    }>;
   }): Promise<Thread<TStateType>> {
     return this.fetch<Thread<TStateType>>(`/threads`, {
       method: "POST",
       json: {
-        metadata: payload?.metadata,
+        metadata: {
+          ...payload?.metadata,
+          graph_id: payload?.graphId,
+        },
         thread_id: payload?.threadId,
         if_exists: payload?.ifExists,
+        supersteps: payload?.supersteps?.map((s) => ({
+          updates: s.updates.map((u) => ({
+            values: u.values,
+            command: u.command,
+            as_node: u.asNode,
+          })),
+        })),
       },
     });
   }

libs/sdk-py/langgraph_sdk/client.py

@@ -839,6 +839,8 @@ class ThreadsClient:
         metadata: Json = None,
         thread_id: Optional[str] = None,
         if_exists: Optional[OnConflictBehavior] = None,
+        supersteps: Optional[Sequence[dict[str, Sequence[dict[str, Any]]]]] = None,
+        graph_id: Optional[str] = None,
     ) -> Thread:
         """Create a new thread.
 
@@ -848,6 +850,9 @@ class ThreadsClient:
                 If None, ID will be a randomly generated UUID.
             if_exists: How to handle duplicate creation. Defaults to 'raise' under the hood.
                 Must be either 'raise' (raise error if duplicate), or 'do_nothing' (return existing thread).
+            supersteps: Apply a list of supersteps when creating a thread, each containing a sequence of updates.
+                Each update has `values` or `command` and `as_node`. Used for copying a thread between deployments.
+            graph_id: Optional graph ID to associate with the thread.
 
         Returns:
             Thread: The created thread.
@@ -863,10 +868,28 @@ class ThreadsClient:
         payload: Dict[str, Any] = {}
         if thread_id:
             payload["thread_id"] = thread_id
-        if metadata:
+        if metadata or graph_id:
-            payload["metadata"] = metadata
+            payload["metadata"] = {
+                **(metadata or {}),
+                **({"graph_id": graph_id} if graph_id else {}),
+            }
         if if_exists:
             payload["if_exists"] = if_exists
+        if supersteps:
+            payload["supersteps"] = [
+                {
+                    "updates": [
+                        {
+                            "values": u["values"],
+                            "command": u.get("command"),
+                            "as_node": u["as_node"],
+                        }
+                        for u in s["updates"]
+                    ]
+                }
+                for s in supersteps
+            ]
+
         return await self.http.post("/threads", json=payload)
 
     async def update(self, thread_id: str, *, metadata: dict[str, Any]) -> Thread:
@@ -3036,6 +3059,8 @@ class SyncThreadsClient:
         metadata: Json = None,
         thread_id: Optional[str] = None,
         if_exists: Optional[OnConflictBehavior] = None,
+        supersteps: Optional[Sequence[dict[str, Sequence[dict[str, Any]]]]] = None,
+        graph_id: Optional[str] = None,
     ) -> Thread:
         """Create a new thread.
 
@@ -3045,6 +3070,9 @@ class SyncThreadsClient:
                 If None, ID will be a randomly generated UUID.
             if_exists: How to handle duplicate creation. Defaults to 'raise' under the hood.
                 Must be either 'raise' (raise error if duplicate), or 'do_nothing' (return existing thread).
+            supersteps: Apply a list of supersteps when creating a thread, each containing a sequence of updates.
+                Each update has `values` or `command` and `as_node`. Used for copying a thread between deployments.
+            graph_id: Optional graph ID to associate with the thread.
 
         Returns:
             Thread: The created thread.
@@ -3060,10 +3088,28 @@ class SyncThreadsClient:
         payload: Dict[str, Any] = {}
         if thread_id:
             payload["thread_id"] = thread_id
-        if metadata:
+        if metadata or graph_id:
-            payload["metadata"] = metadata
+            payload["metadata"] = {
+                **(metadata or {}),
+                **({"graph_id": graph_id} if graph_id else {}),
+            }
         if if_exists:
             payload["if_exists"] = if_exists
+        if supersteps:
+            payload["supersteps"] = [
+                {
+                    "updates": [
+                        {
+                            "values": u["values"],
+                            "command": u.get("command"),
+                            "as_node": u["as_node"],
+                        }
+                        for u in s["updates"]
+                    ]
+                }
+                for s in supersteps
+            ]
+
         return self.http.post("/threads", json=payload)
 
     def update(self, thread_id: str, *, metadata: dict[str, Any]) -> Thread:
@@ -3307,7 +3353,7 @@ class SyncThreadsClient:
 
         Example Usage:
 
-            response = client.threads.update_state(
+            response = await client.threads.update_state(
                 thread_id="my_thread_id",
                 values={"messages":[{"role": "user", "content": "hello!"}]},
                 as_node="my_node",

libs/sdk-py/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-sdk"
-version = "0.1.57"
+version = "0.1.58"
 description = "SDK for interacting with LangGraph API"
 authors = []
 license = "MIT"