lib0.1.15

lib: Improve compat with pydantic 2 models

.github/workflows/size.yml

@@ -0,0 +1,28 @@
+name: Check File Size
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:
+  file-size-check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Get changed files
+        id: changed-files
+        uses: tj-actions/changed-files@v44
+      - name: Filter by size
+        run: |
+          large_added_files=$(find ${{ steps.changed-files.outputs.added_files }} -maxdepth 0 -size +1M)
+          if [ -n "$large_added_files" ]; then
+            echo "Large files added: $large_added_files"
+            echo "# Large files added:" >> $GITHUB_STEP_SUMMARY
+            echo "$large_added_files" >> $GITHUB_STEP_SUMMARY
+            exit 1
+          fi

Makefile

@@ -1,6 +1,14 @@
-.PHONY: build-docs serve-docs serve-clean-docs clean-docs codespell
+.PHONY: build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc
 
-build-docs:
+build-typedoc:
+	cd libs/sdk-js && yarn install --include-dev && yarn typedoc
+	cd libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
+	#  Add links to the monorepo
+	sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md
+
+
+
+build-docs: build-typedoc
 	poetry run python docs/_scripts/copy_notebooks.py
 	poetry run python -m mkdocs build --clean -f docs/mkdocs.yml --strict
 
@@ -8,7 +16,7 @@ serve-clean-docs: clean-docs
 	poetry run python docs/_scripts/copy_notebooks.py
 	poetry run python -m mkdocs serve -c -f docs/mkdocs.yml --strict -w ./libs/langgraph
 
-serve-docs:
+serve-docs: build-typedoc
 	poetry run python docs/_scripts/copy_notebooks.py
 	poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph --dirty
 

README.md

@@ -3,7 +3,6 @@
 ![Version](https://img.shields.io/pypi/v/langgraph)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
-[![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.com/channels/1038097195422978059/1170024642245832774)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
 
 ⚡ Building language agents as graphs ⚡

docs/.gitignore

@@ -1,3 +1,4 @@
 *.ipynb
 site/
 docs/tutorials/**/*.png
+docs/cloud/reference/sdk/js_ts_sdk_ref.md

docs/_scripts/copy_notebooks.py

@@ -101,6 +101,7 @@ _HIDE = set(
         "docs/quickstart.ipynb",
         "tutorials/rag-agent-testing.ipynb",
         "tutorials/rag-agent-testing-local.ipynb",
+        "tutorials/tool-calling-agent-local.ipynb",
         "time-travel.ipynb",
         "code_assistant/langgraph_code_assistant_mistral.ipynb",
     ]

docs/docs/cloud/concepts/api.md

@@ -12,6 +12,10 @@ An assistant is a configured instance of a [`CompiledGraph`][compiledgraph]. It
 
 The LangGraph Cloud API provides several endpoints for creating and managing assistants. See the <a href="../reference/api/api_ref.html#tag/assistantscreate" target="_blank">API reference</a> for more details.
 
+#### Configuring Assistants
+
+You can save custom assistants from the same graph to set different default prompts, models, and other configurations without changing a line of code in your graph. This allows you the ability to quickly test out different configurations without having to rewrite your graph every time, and also give users the flexibility to select different configurations when using your LangGraph application. See <a href="https://langchain-ai.github.io/langgraph/cloud/how-tos/cloud_examples/configuration_cloud/">this</a> how-to for information on how to configure a deployed graph. 
+
 ### Threads
 
 A thread contains the accumulated state of a group of runs. If a run is executed on a thread, then the [state][state] of the underlying graph of the assistant will be persisted to the thread. A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.

docs/docs/cloud/deployment/graph_rebuild.md

@@ -0,0 +1,146 @@
+# Rebuild Graph at Runtime
+
+You might need to rebuild your graph with a different configuration for a new run. For example, you might need to use a different graph state or graph structure depending on the config. This guide shows how you can do this.
+
+!!! note "Note"
+    In most cases, customizing behavior based on the config should be handled by a single graph where each node can read a config and change its behavior based on it
+
+## Prerequisites
+
+Make sure to check out [this how-to guide](./setup.md) on setting up your app for deployment first.
+
+## Define graphs
+
+Let's say you have an app with a simple graph that calls an LLM and returns the response to the user. The app file directory looks like the following:
+
+```
+my-app/
+|-- requirements.txt
+|-- .env
+|-- openai_agent.py     # code for your graph
+```
+
+where the graph is defined in `openai_agent.py`. 
+
+### No rebuild
+
+In the standard LangGraph API configuration, the server uses the compiled graph instance that's defined at the top level of `openai_agent.py`, which looks like the following:
+
+```python
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, MessageGraph
+
+model = ChatOpenAI(temperature=0)
+
+graph_workflow = MessageGraph()
+
+graph_workflow.add_node("agent", model)
+graph_workflow.add_edge("agent", END)
+graph_workflow.set_entry_point("agent")
+
+agent = graph_workflow.compile()
+```
+
+To make the server aware of your graph, you need to specify a path to the variable that contains the `CompiledStateGraph` instance in your LangGraph API configuration (`langgraph.json`), e.g.:
+
+```
+{
+    "dependencies": ["."],
+    "graphs": {
+        "openai_agent": "./openai_agent.py:agent",
+    },
+    "env": "./.env"
+}
+```
+
+### Rebuild
+
+To make your graph rebuild on each new run with custom configuration, you need to rewrite `openai_agent.py` to instead provide a _function_ that takes a config and returns a graph (or compiled graph) instance. Let's say we want to return our existing graph for user ID '1', and a tool-calling agent for other users. We can modify `openai_agent.py` as follows:
+
+```python
+from typing import Annotated, TypedDict
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, MessageGraph
+from langgraph.graph.state import StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode
+from langchain_core.tools import tool
+from langchain_core.messages import BaseMessage
+from langchain_core.runnables import RunnableConfig
+
+
+class State(TypedDict):
+    messages: Annotated[list[BaseMessage], add_messages]
+
+
+model = ChatOpenAI(temperature=0)
+
+def make_default_graph():
+    """Make a simple LLM agent"""
+    graph_workflow = StateGraph(State)
+    def call_model(state):
+        return {"messages": [model.invoke(state["messages"])]}
+
+    graph_workflow.add_node("agent", call_model)
+    graph_workflow.add_edge("agent", END)
+    graph_workflow.set_entry_point("agent")
+
+    agent = graph_workflow.compile()
+    return agent
+
+
+def make_alternative_graph():
+    """Make a tool-calling agent"""
+
+    @tool
+    def add(a: float, b: float):
+        """Adds two numbers."""
+        return a + b
+
+    tool_node = ToolNode([add])
+    model_with_tools = model.bind_tools([add])
+    def call_model(state):
+        return {"messages": [model_with_tools.invoke(state["messages"])]}
+
+    def should_continue(state: State):
+        if state["messages"][-1].tool_calls:
+            return "tools"
+        else:
+            return END
+
+    graph_workflow = StateGraph(State)
+
+    graph_workflow.add_node("agent", call_model)
+    graph_workflow.add_node("tools", tool_node)
+    graph_workflow.add_edge("tools", "agent")
+    graph_workflow.set_entry_point("agent")
+    graph_workflow.add_conditional_edges("agent", should_continue)
+
+    agent = graph_workflow.compile()
+    return agent
+
+
+# this is the graph making function that will decide which graph to
+# build based on the provided config
+def make_graph(config: RunnableConfig):
+    user_id = config.get("configurable", {}).get("user_id")
+    # route to different graph state / structure based on the user ID
+    if user_id == "1":
+        return make_default_graph()
+    else:
+        return make_alternative_graph()
+```
+
+Finally, you need to specify the path to your graph-making function (`make_graph`) in `langgraph.json`:
+
+```
+{
+    "dependencies": ["."],
+    "graphs": {
+        "openai_agent": "./openai_agent.py:make_graph",
+    },
+    "env": "./.env"
+}
+```
+
+See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file)

docs/docs/cloud/deployment/setup.md

@@ -88,7 +88,7 @@ agent = graph_workflow.compile()
 ```
 
 !!! warning "Assign `CompiledGraph` to Variable"
-    The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module.
+    The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
 
 Example file directory:
 ```

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

@@ -8,6 +8,8 @@ The LangGraph Studio lets you test different configurations and inputs to your g
     1. Select `Submit` to invoke the selected assistant.
 1. View output of the invocation in the right-hand pane.
 
-The following GIF shows these exact steps being carried out:
+The following video shows these exact steps being carried out:
 
-![Using LangGraph Studio](./img/studio_input.gif)
+<video controls allowfullscreen="true" poster="../img/studio_input_poster.png">
+    <source src="../img/studio_input.mp4" type="video/mp4">
+</video>

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

@@ -9,6 +9,8 @@ Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmi
 1. In the top-right corner, select `Open LangGraph Studio`.
 1. [Invoke an assistant](./invoke_studio.md) or [view an existing thread](./threads_studio.md).
 
-The following GIF shows these exact steps being carried out:
+The following video shows these exact steps being carried out:
 
-![Using LangGraph Studio](./img/studio_usage.gif)
+<video controls allowfullscreen="true" poster="../img/studio_usage_poster.png">
+    <source src="../img/studio_usage.mp4" type="video/mp4">
+</video>

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

@@ -6,14 +6,18 @@
 1. View the state of the thread (i.e. the output) in the right-hand pane.
 1. To create a new thread, select `+ New Thread`.
 
-The following GIF shows these exact steps being carried out:
+The following video shows these exact steps being carried out:
 
-![Using LangGraph Studio](./img/studio_threads.gif)
+<video controls="true" allowfullscreen="true" poster="../img/studio_threads_poster.png">
+    <source src="../img/studio_threads.mp4" type="video/mp4">
+</video>
 
 ## Edit Thread State
 
 The LangGraph Studio UI contains features for editing thread state. Explore these features in the right-hand pane. Select the `Edit` icon, modify the desired state, and then select `Fork` to invoke the assistant with the updated state.
 
-The following GIF shows how to edit a thread in the studio:
+The following video shows how to edit a thread in the studio:
 
-![Using LangGraph Studio](./img/studio_forks.gif)
+<video controls allowfullscreen="true" poster="../img/studio_forks_poster.png">
+    <source src="../img/studio_forks.mp4" type="video/mp4">
+</video>

docs/docs/cloud/index.md

@@ -12,7 +12,11 @@
 !!! warning "Under Construction"
     LangGraph Cloud documentation is under construction. Contents may change until general availability.
 
-![GIF](./how-tos/img/studio_input.gif)
+
+<video controls preload="auto" allowfullscreen="true" poster="how-tos/img/studio_forks_poster.png">
+    <source src="how-tos/img/studio_forks.mp4" type="video/mp4">
+</video>
+
 
 ## Overview
 

docs/docs/cloud/reference/cli.md

@@ -13,7 +13,7 @@ The LangGraph CLI requires a JSON configuration file with the following keys:
 | Key | Description |
 | --- | ----------- |
 | `dependencies` | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name. |
-| `graphs` | **Required**. Mapping from graph ID to path where the compiled graph is defined. Example: `./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.graph.CompiledGraph`. |
+| `graphs` | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul> |
 | `env` | Path to `.env` file or a mapping from environment variable to its value. |
 | `python_version` | `3.11` or `3.12`. Defaults to `3.11`. |
 | `pip_config_file`| Path to `pip` config file. |
@@ -49,7 +49,7 @@ Example:
         "."
     ],
     "graphs": {
-        "my_graph_id": "./your_package/your_file.py:variable"
+        "my_graph_id": "./your_package/your_file.py:make_graph"
     },
     "env": {
         "OPENAI_API_KEY": "secret-key"

docs/docs/how-tos/index.md

@@ -61,6 +61,13 @@ These guides show how to use different streaming modes.
 - [How to pass graph state to tools](pass-run-time-values-to-tools.ipynb)
 - [How to pass config to tools](pass-config-to-tools.ipynb)
 
+## State Management
+
+- [Use Pydantic model as state](state-model.ipynb)
+- [Use a context object in state](state-context-key.ipynb)
+- [Have a separate input and output schema](input_output_schema.ipynb)
+- [Pass private state between nodes inside the graph](pass_private_state.ipynb)
+
 ## Other
 
 - [How to run graph asynchronously](async.ipynb)

docs/mkdocs.yml

@@ -192,6 +192,7 @@ nav:
           - Deployment:
               - Setup App: "cloud/deployment/setup.md"
               - Setup App (pyproject.toml): "cloud/deployment/setup_pyproject.md"
+              - Rebuild Graph at Runtime: "cloud/deployment/graph_rebuild.md"
               - Test App Locally: "cloud/deployment/test_locally.md"
               - Deploy to Cloud: "cloud/deployment/cloud.md"
               - Self-Host: "cloud/deployment/self_hosted.md"

examples/cloud_examples/langgraph_to_langgraph_cloud.ipynb

@@ -50,7 +50,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "OPENAI_API_KEY:  ········\n"
@@ -991,7 +991,7 @@
    "id": "08996d90-a3ff-4655-9763-1dd4971344d4",
    "metadata": {},
    "source": [
-    "### With LangGraph Clound"
+    "### With LangGraph Cloud"
    ]
   },
   {

examples/introduction.ipynb

@@ -84,8 +84,9 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "ef7bcad1-1274-4b7c-a2e9-365180ef3a31",
+   "id": "9c374e41-f9b7-439e-a520-6d8c853c5220",
    "metadata": {},
    "source": [
     "## Part 1: Build a Basic Chatbot\n",
@@ -120,13 +121,24 @@
     "graph_builder = StateGraph(State)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "31c755cd-8994-4867-bdff-96a55d7beae7",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Note</p>\n",
+    "    <p>\n",
+    "    The first thing you do when you define a graph is define the <code>State</code> of the graph. The <code>State</code> consists of the schema of the graph as well as reducer functions which specify how to apply updates to the state. In our example <code>State</code> is a <code>TypedDict</code> with a single key: <code>messages</code>. The <code>messages</code> key is annotated with the <a href=\"https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages\"><code>add_messages</code></a> reducer function, which tells LangGraph to append new messages to the existing list, rather than overwriting it. State keys without an annotation will be overwritten by each update, storing the most recent value. Check out <a href=\"https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages\">this conceptual guide</a> to learn more about state, reducers and other low-level concepts.\n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4137feed-746e-4c72-a34a-f7a699ad5dcf",
    "metadata": {},
    "source": [
-    "**Notice** that we've defined our `State` as a TypedDict with a single key: `messages`. The `messages` key is annotated with the [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages) function, which tells LangGraph to append new messages to the existing list, rather than overwriting it.\n",
-    "\n",
     "So now our graph knows two things:\n",
     "\n",
     "1. Every `node` we define will receive the current `State` as input and return a value that updates that state.\n",
@@ -3056,9 +3068,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "langgraph",
    "language": "python",
-   "name": "python3"
+   "name": "langgraph"
   },
   "language_info": {
    "codemirror_mode": {
@@ -3070,7 +3082,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

libs/langgraph/README.md

@@ -3,7 +3,6 @@
 ![Version](https://img.shields.io/pypi/v/langgraph)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
-[![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.com/channels/1038097195422978059/1170024642245832774)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
 
 ⚡ Building language agents as graphs ⚡

libs/langgraph/langgraph/graph/state.py

@@ -452,7 +452,11 @@ class CompiledStateGraph(CompiledGraph):
     def get_input_schema(
         self, config: Optional[RunnableConfig] = None
     ) -> type[BaseModel]:
-        if isclass(self.builder.input) and issubclass(self.builder.input, BaseModel):
+        from pydantic import BaseModel as BaseModelP
+
+        if isclass(self.builder.input) and issubclass(
+            self.builder.input, (BaseModel, BaseModelP)
+        ):
             return self.builder.input
         else:
             keys = list(self.builder.schemas[self.builder.input].keys())
@@ -475,7 +479,11 @@ class CompiledStateGraph(CompiledGraph):
     def get_output_schema(
         self, config: Optional[RunnableConfig] = None
     ) -> type[BaseModel]:
-        if isclass(self.builder.input) and issubclass(self.builder.output, BaseModel):
+        from pydantic import BaseModel as BaseModelP
+
+        if isclass(self.builder.input) and issubclass(
+            self.builder.output, (BaseModel, BaseModelP)
+        ):
             return self.builder.output
 
         return super().get_output_schema(config)
@@ -497,7 +505,7 @@ class CompiledStateGraph(CompiledGraph):
                 return SKIP_WRITE
             elif isinstance(input, dict):
                 return input.get(key, SKIP_WRITE)
-            elif get_type_hints(type(input)).get(key):
+            elif get_type_hints(type(input)):
                 value = getattr(input, key, SKIP_WRITE)
                 return value if value is not None else SKIP_WRITE
             else:

libs/langgraph/langgraph/prebuilt/tool_node.py

@@ -1,4 +1,5 @@
 import asyncio
+import json
 from copy import copy
 from typing import (
     Any,
@@ -28,6 +29,16 @@ INVALID_TOOL_NAME_ERROR_TEMPLATE = (
 TOOL_CALL_ERROR_TEMPLATE = "Error: {error}\n Please fix your mistakes."
 
 
+def str_output(output: Any) -> str:
+    if isinstance(output, str):
+        return output
+    else:
+        try:
+            return json.dumps(output)
+        except Exception:
+            return str(output)
+
+
 class ToolNode(RunnableCallable):
     """A node that runs the tools called in the last AIMessage.
 
@@ -94,7 +105,12 @@ class ToolNode(RunnableCallable):
 
         try:
             input = {**call, **{"type": "tool_call"}}
-            return self.tools_by_name[call["name"]].invoke(input, config)
+            tool_message: ToolMessage = self.tools_by_name[call["name"]].invoke(
+                input, config
+            )
+            # TODO: handle this properly in core
+            tool_message.content = str_output(tool_message.content)
+            return tool_message
         except Exception as e:
             if not self.handle_tool_errors:
                 raise e
@@ -106,7 +122,12 @@ class ToolNode(RunnableCallable):
             return invalid_tool_message
         try:
             input = {**call, **{"type": "tool_call"}}
-            return await self.tools_by_name[call["name"]].ainvoke(input, config)
+            tool_message: ToolMessage = await self.tools_by_name[call["name"]].ainvoke(
+                input, config
+            )
+            # TODO: handle this properly in core
+            tool_message.content = str_output(tool_message.content)
+            return tool_message
         except Exception as e:
             if not self.handle_tool_errors:
                 raise e

libs/langgraph/langgraph/pregel/__init__.py

@@ -722,8 +722,10 @@ class Pregel(
         if config and config.get("configurable", {}).get(CONFIG_KEY_READ) is not None:
             # if being called as a node in another graph, always use values mode
             stream_mode = ["values"]
-        if config is not None and config.get("configurable", {}).get(
-            CONFIG_KEY_CHECKPOINTER
+        if (
+            config is not None
+            and config.get("configurable", {}).get(CONFIG_KEY_CHECKPOINTER)
+            and (interrupt_after or interrupt_before)
         ):
             checkpointer: Optional[BaseCheckpointSaver] = config["configurable"][
                 CONFIG_KEY_CHECKPOINTER

libs/langgraph/langgraph/pregel/algo.py

@@ -257,6 +257,9 @@ def prepare_next_tasks(
         if not isinstance(packet, Send):
             logger.warn(f"Ignoring invalid packet type {type(packet)} in pending sends")
             continue
+        if packet.node not in processes:
+            logger.warn(f"Ignoring unknown node name {packet.node} in pending sends")
+            continue
         if for_execution:
             proc = processes[packet.node]
             if node := proc.get_node():

libs/langgraph/langgraph/pregel/loop.py

@@ -102,6 +102,7 @@ class PregelLoop:
     checkpoint_pending_writes: List[PendingWrite]
 
     step: int
+    stop: int
     status: Literal[
         "pending", "done", "interrupt_before", "interrupt_after", "out_of_steps"
     ]
@@ -203,7 +204,7 @@ class PregelLoop:
             return False
 
         # check if iteration limit is reached
-        if self.step > self.config["recursion_limit"]:
+        if self.step > self.stop:
             self.status = "out_of_steps"
             return False
 
@@ -419,6 +420,7 @@ class SyncPregelLoop(PregelLoop, ContextManager):
         )
         self.status = "pending"
         self.step = self.checkpoint_metadata["step"] + 1
+        self.stop = self.step + self.config["recursion_limit"] + 1
 
         return self
 
@@ -497,6 +499,7 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
         )
         self.status = "pending"
         self.step = self.checkpoint_metadata["step"] + 1
+        self.stop = self.step + self.config["recursion_limit"] + 1
 
         return self
 

libs/langgraph/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.1.11"
+version = "0.1.15"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"

libs/langgraph/tests/__snapshots__/test_pregel.ambr

@@ -615,6 +615,232 @@
   
   '''
 # ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1
+  '''
+  graph TD;
+  	__start__ --> rewrite_query;
+  	analyzer_one --> retriever_one;
+  	qa --> __end__;
+  	retriever_one --> qa;
+  	retriever_two --> qa;
+  	rewrite_query --> analyzer_one;
+  	rewrite_query -.-> retriever_two;
+  
+  '''
+# ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1.1
+  dict({
+    'definitions': dict({
+      'InnerObject': dict({
+        'properties': dict({
+          'yo': dict({
+            'title': 'Yo',
+            'type': 'integer',
+          }),
+        }),
+        'required': list([
+          'yo',
+        ]),
+        'title': 'InnerObject',
+        'type': 'object',
+      }),
+    }),
+    'properties': dict({
+      'answer': dict({
+        'title': 'Answer',
+        'type': 'string',
+      }),
+      'docs': dict({
+        'items': dict({
+          'type': 'string',
+        }),
+        'title': 'Docs',
+        'type': 'array',
+      }),
+      'inner': dict({
+        '$ref': '#/definitions/InnerObject',
+      }),
+      'query': dict({
+        'title': 'Query',
+        'type': 'string',
+      }),
+    }),
+    'required': list([
+      'query',
+      'inner',
+      'docs',
+    ]),
+    'title': 'State',
+    'type': 'object',
+  })
+# ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1.2
+  dict({
+    'definitions': dict({
+      'InnerObject': dict({
+        'properties': dict({
+          'yo': dict({
+            'title': 'Yo',
+            'type': 'integer',
+          }),
+        }),
+        'required': list([
+          'yo',
+        ]),
+        'title': 'InnerObject',
+        'type': 'object',
+      }),
+    }),
+    'properties': dict({
+      'answer': dict({
+        'title': 'Answer',
+        'type': 'string',
+      }),
+      'docs': dict({
+        'items': dict({
+          'type': 'string',
+        }),
+        'title': 'Docs',
+        'type': 'array',
+      }),
+      'inner': dict({
+        '$ref': '#/definitions/InnerObject',
+      }),
+      'query': dict({
+        'title': 'Query',
+        'type': 'string',
+      }),
+    }),
+    'required': list([
+      'query',
+      'inner',
+      'docs',
+    ]),
+    'title': 'State',
+    'type': 'object',
+  })
+# ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2
+  '''
+  graph TD;
+  	__start__ --> rewrite_query;
+  	analyzer_one --> retriever_one;
+  	qa --> __end__;
+  	retriever_one --> qa;
+  	retriever_two --> qa;
+  	rewrite_query --> analyzer_one;
+  	rewrite_query -.-> retriever_two;
+  
+  '''
+# ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2.1
+  dict({
+    '$defs': dict({
+      'InnerObject': dict({
+        'properties': dict({
+          'yo': dict({
+            'title': 'Yo',
+            'type': 'integer',
+          }),
+        }),
+        'required': list([
+          'yo',
+        ]),
+        'title': 'InnerObject',
+        'type': 'object',
+      }),
+    }),
+    'properties': dict({
+      'answer': dict({
+        'anyOf': list([
+          dict({
+            'type': 'string',
+          }),
+          dict({
+            'type': 'null',
+          }),
+        ]),
+        'default': None,
+        'title': 'Answer',
+      }),
+      'docs': dict({
+        'items': dict({
+          'type': 'string',
+        }),
+        'title': 'Docs',
+        'type': 'array',
+      }),
+      'inner': dict({
+        '$ref': '#/$defs/InnerObject',
+      }),
+      'query': dict({
+        'title': 'Query',
+        'type': 'string',
+      }),
+    }),
+    'required': list([
+      'query',
+      'inner',
+      'docs',
+    ]),
+    'title': 'State',
+    'type': 'object',
+  })
+# ---
+# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2.2
+  dict({
+    '$defs': dict({
+      'InnerObject': dict({
+        'properties': dict({
+          'yo': dict({
+            'title': 'Yo',
+            'type': 'integer',
+          }),
+        }),
+        'required': list([
+          'yo',
+        ]),
+        'title': 'InnerObject',
+        'type': 'object',
+      }),
+    }),
+    'properties': dict({
+      'answer': dict({
+        'anyOf': list([
+          dict({
+            'type': 'string',
+          }),
+          dict({
+            'type': 'null',
+          }),
+        ]),
+        'default': None,
+        'title': 'Answer',
+      }),
+      'docs': dict({
+        'items': dict({
+          'type': 'string',
+        }),
+        'title': 'Docs',
+        'type': 'array',
+      }),
+      'inner': dict({
+        '$ref': '#/$defs/InnerObject',
+      }),
+      'query': dict({
+        'title': 'Query',
+        'type': 'string',
+      }),
+    }),
+    'required': list([
+      'query',
+      'inner',
+      'docs',
+    ]),
+    'title': 'State',
+    'type': 'object',
+  })
+# ---
 # name: test_in_one_fan_out_state_graph_waiting_edge_via_branch
   '''
   graph TD;

libs/langgraph/tests/memory_assert.py

@@ -7,6 +7,7 @@ from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
     Checkpoint,
     CheckpointMetadata,
+    CheckpointTuple,
     SerializerProtocol,
     copy_checkpoint,
 )
@@ -119,3 +120,11 @@ class MemorySaverAssertCheckpointMetadata(MemorySaver):
         return await asyncio.get_running_loop().run_in_executor(
             None, self.put, config, checkpoint, metadata
         )
+
+
+class MemorySaverNoPending(MemorySaver):
+    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+        result = super().get_tuple(config)
+        if result:
+            return CheckpointTuple(result.config, result.checkpoint, result.metadata)
+        return result

libs/langgraph/tests/test_prebuilt.py

@@ -267,6 +267,13 @@ async def test_tool_node():
             raise ValueError("Test error")
         return f"tool2: {some_val} - {some_other_val}"
 
+    async def tool3(some_val: int, some_other_val: str) -> str:
+        """Tool 3 docstring."""
+        return [
+            {"key_1": some_val, "key_2": "foo"},
+            {"key_1": some_other_val, "key_2": "baz"},
+        ]
+
     result = ToolNode([tool1]).invoke(
         {
             "messages": [
@@ -377,6 +384,31 @@ async def test_tool_node():
     )
     assert tool_message.tool_call_id == "some 0"
 
+    # list of dicts tool content
+    result3 = await ToolNode([tool3]).ainvoke(
+        {
+            "messages": [
+                AIMessage(
+                    "hi?",
+                    tool_calls=[
+                        {
+                            "name": "tool3",
+                            "args": {"some_val": 2, "some_other_val": "bar"},
+                            "id": "some 0",
+                        }
+                    ],
+                )
+            ]
+        }
+    )
+    tool_message: ToolMessage = result3["messages"][-1]
+    assert tool_message.type == "tool"
+    assert (
+        tool_message.content
+        == '[{"key_1": 2, "key_2": "foo"}, {"key_1": "bar", "key_2": "baz"}]'
+    )
+    assert tool_message.tool_call_id == "some 0"
+
 
 def my_function(some_val: int, some_other_val: str) -> str:
     return f"{some_val} - {some_other_val}"

libs/langgraph/tests/test_pregel.py

@@ -66,6 +66,7 @@ from tests.any_str import AnyStr
 from tests.memory_assert import (
     MemorySaverAssertCheckpointMetadata,
     MemorySaverAssertImmutable,
+    MemorySaverNoPending,
     NoopSerializer,
 )
 
@@ -1149,10 +1150,32 @@ def test_cond_edge_after_send() -> None:
     builder.add_conditional_edges("1", send_for_fun)
     builder.add_conditional_edges("2", route_to_three)
     graph = builder.compile()
-
     assert graph.invoke(["0"]) == ["0", "1", "2", "3"]
 
 
+async def test_checkpointer_null_pending_writes() -> None:
+    class Node:
+        def __init__(self, name: str):
+            self.name = name
+            setattr(self, "__name__", name)
+
+        def __call__(self, state):
+            return [self.name]
+
+    builder = StateGraph(Annotated[list, operator.add])
+    builder.add_node(Node("1"))
+    builder.add_edge(START, "1")
+    graph = builder.compile(checkpointer=MemorySaverNoPending())
+    assert graph.invoke([], {"configurable": {"thread_id": "foo"}}) == ["1"]
+    assert graph.invoke([], {"configurable": {"thread_id": "foo"}}) == ["1"] * 2
+    assert (await graph.ainvoke([], {"configurable": {"thread_id": "foo"}})) == [
+        "1"
+    ] * 3
+    assert (await graph.ainvoke([], {"configurable": {"thread_id": "foo"}})) == [
+        "1"
+    ] * 4
+
+
 def test_invoke_checkpoint_sqlite(mocker: MockerFixture) -> None:
     adder = mocker.Mock(side_effect=lambda x: x["total"] + x["input"])
 
@@ -7025,7 +7048,7 @@ def test_in_one_fan_out_state_graph_waiting_edge_via_branch(
     ]
 
 
-def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class(
+def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1(
     snapshot: SnapshotAssertion,
 ) -> None:
     from langchain_core.pydantic_v1 import BaseModel, ValidationError
@@ -7039,8 +7062,12 @@ def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class(
             y = [t[1] for t in y]
         return sorted(operator.add(x, y))
 
+    class InnerObject(BaseModel):
+        yo: int
+
     class State(BaseModel):
         query: str
+        inner: InnerObject
         answer: Optional[str] = None
         docs: Annotated[list[str], sorted_add]
 
@@ -7089,17 +7116,20 @@ def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class(
     app = workflow.compile()
 
     assert app.get_graph().draw_mermaid(with_styles=False) == snapshot
+    assert app.get_input_schema().schema() == snapshot
+    assert app.get_output_schema().schema() == snapshot
 
     with pytest.raises(ValidationError):
         app.invoke({"query": {}})
 
-    assert app.invoke({"query": "what is weather in sf"}) == {
+    assert app.invoke({"query": "what is weather in sf", "inner": {"yo": 1}}) == {
         "query": "analyzed: query: what is weather in sf",
         "docs": ["doc1", "doc2", "doc3", "doc4"],
         "answer": "doc1,doc2,doc3,doc4",
+        "inner": {"yo": 1},
     }
 
-    assert [*app.stream({"query": "what is weather in sf"})] == [
+    assert [*app.stream({"query": "what is weather in sf", "inner": {"yo": 1}})] == [
         {"rewrite_query": {"query": "query: what is weather in sf"}},
         {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
         {"retriever_two": {"docs": ["doc3", "doc4"]}},
@@ -7114,7 +7144,122 @@ def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class(
     config = {"configurable": {"thread_id": "1"}}
 
     assert [
-        c for c in app_w_interrupt.stream({"query": "what is weather in sf"}, config)
+        c
+        for c in app_w_interrupt.stream(
+            {"query": "what is weather in sf", "inner": {"yo": 1}}, config
+        )
+    ] == [
+        {"rewrite_query": {"query": "query: what is weather in sf"}},
+        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
+        {"retriever_two": {"docs": ["doc3", "doc4"]}},
+        {"retriever_one": {"docs": ["doc1", "doc2"]}},
+    ]
+
+    assert [c for c in app_w_interrupt.stream(None, config)] == [
+        {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
+    ]
+
+
+def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2(
+    snapshot: SnapshotAssertion,
+) -> None:
+    from pydantic import BaseModel, ValidationError
+
+    def sorted_add(
+        x: list[str], y: Union[list[str], list[tuple[str, str]]]
+    ) -> list[str]:
+        if isinstance(y[0], tuple):
+            for rem, _ in y:
+                x.remove(rem)
+            y = [t[1] for t in y]
+        return sorted(operator.add(x, y))
+
+    class InnerObject(BaseModel):
+        yo: int
+
+    class State(BaseModel):
+        query: str
+        inner: InnerObject
+        answer: Optional[str] = None
+        docs: Annotated[list[str], sorted_add]
+
+    class StateUpdate(BaseModel):
+        query: Optional[str] = None
+        answer: Optional[str] = None
+        docs: Optional[list[str]] = None
+
+    def rewrite_query(data: State) -> State:
+        return {"query": f"query: {data.query}"}
+
+    def analyzer_one(data: State) -> State:
+        return StateUpdate(query=f"analyzed: {data.query}")
+
+    def retriever_one(data: State) -> State:
+        return {"docs": ["doc1", "doc2"]}
+
+    def retriever_two(data: State) -> State:
+        time.sleep(0.1)
+        return {"docs": ["doc3", "doc4"]}
+
+    def qa(data: State) -> State:
+        return {"answer": ",".join(data.docs)}
+
+    def decider(data: State) -> str:
+        assert isinstance(data, State)
+        return "retriever_two"
+
+    workflow = StateGraph(State)
+
+    workflow.add_node("rewrite_query", rewrite_query)
+    workflow.add_node("analyzer_one", analyzer_one)
+    workflow.add_node("retriever_one", retriever_one)
+    workflow.add_node("retriever_two", retriever_two)
+    workflow.add_node("qa", qa)
+
+    workflow.set_entry_point("rewrite_query")
+    workflow.add_edge("rewrite_query", "analyzer_one")
+    workflow.add_edge("analyzer_one", "retriever_one")
+    workflow.add_conditional_edges(
+        "rewrite_query", decider, {"retriever_two": "retriever_two"}
+    )
+    workflow.add_edge(["retriever_one", "retriever_two"], "qa")
+    workflow.set_finish_point("qa")
+
+    app = workflow.compile()
+
+    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot
+    assert app.get_input_schema().schema() == snapshot
+    assert app.get_output_schema().schema() == snapshot
+
+    with pytest.raises(ValidationError):
+        app.invoke({"query": {}})
+
+    assert app.invoke({"query": "what is weather in sf", "inner": {"yo": 1}}) == {
+        "query": "analyzed: query: what is weather in sf",
+        "docs": ["doc1", "doc2", "doc3", "doc4"],
+        "answer": "doc1,doc2,doc3,doc4",
+        "inner": {"yo": 1},
+    }
+
+    assert [*app.stream({"query": "what is weather in sf", "inner": {"yo": 1}})] == [
+        {"rewrite_query": {"query": "query: what is weather in sf"}},
+        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
+        {"retriever_two": {"docs": ["doc3", "doc4"]}},
+        {"retriever_one": {"docs": ["doc1", "doc2"]}},
+        {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
+    ]
+
+    app_w_interrupt = workflow.compile(
+        checkpointer=MemorySaverAssertImmutable(),
+        interrupt_after=["retriever_one"],
+    )
+    config = {"configurable": {"thread_id": "1"}}
+
+    assert [
+        c
+        for c in app_w_interrupt.stream(
+            {"query": "what is weather in sf", "inner": {"yo": 1}}, config
+        )
     ] == [
         {"rewrite_query": {"query": "query: what is weather in sf"}},
         {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
@@ -8570,6 +8715,7 @@ def test_nested_graph_interrupts_parallel(checkpointer: BaseCheckpointSaver) ->
             checkpointer.__exit__(None, None, None)
 
 
+@pytest.mark.skip
 @pytest.mark.parametrize(
     "checkpointer",
     [

libs/langgraph/tests/test_pregel_async.py

@@ -7099,6 +7099,7 @@ async def test_nested_graph_interrupts_parallel(
             await checkpointer.__aexit__(None, None, None)
 
 
+@pytest.mark.skip
 @pytest.mark.parametrize(
     "checkpointer",
     [

libs/sdk-js/scripts/create-entrypoints.js

@@ -38,9 +38,19 @@ const updateConfig = () => {
     ...json,
     typedocOptions: {
       ...json.typedocOptions,
-      entryPoints: [...Object.keys(entrypoints)].map(
-        (key) => `src/${entrypoints[key]}.ts`,
-      ),
+      entryPoints: [...Object.keys(entrypoints)].map((key) => {
+        const basePath = `src/${entrypoints[key]}`;
+        if (fs.existsSync(`${basePath}.mts`)) {
+          return `${basePath}.mts`;
+        } else if (fs.existsSync(`${basePath}.ts`)) {
+          return `${basePath}.ts`;
+        } else {
+          console.warn(
+            `Warning: Neither ${basePath}.mts nor ${basePath}.ts found for entrypoint ${key}`,
+          );
+          return `${basePath}.ts`; // Default to .ts if neither exists
+        }
+      }),
     },
   }));
 

libs/sdk-js/src/client.mts

@@ -107,7 +107,7 @@ class BaseClient {
   }
 }
 
-class CronsClient extends BaseClient {
+export class CronsClient extends BaseClient {
   /**
    *
    * @param threadId The ID of the thread.
@@ -195,7 +195,7 @@ class CronsClient extends BaseClient {
   }
 }
 
-class AssistantsClient extends BaseClient {
+export class AssistantsClient extends BaseClient {
   /**
    * Get an assistant by ID.
    *
@@ -302,7 +302,7 @@ class AssistantsClient extends BaseClient {
   }
 }
 
-class ThreadsClient extends BaseClient {
+export class ThreadsClient extends BaseClient {
   /**
    * Get a thread by ID.
    *
@@ -496,7 +496,7 @@ class ThreadsClient extends BaseClient {
   }
 }
 
-class RunsClient extends BaseClient {
+export class RunsClient extends BaseClient {
   stream(
     threadId: null,
     assistantId: string,

libs/sdk-js/tsconfig.json

@@ -2,7 +2,11 @@
   "extends": "@tsconfig/recommended",
   "compilerOptions": {
     "target": "ES2021",
-    "lib": ["ES2021", "ES2022.Object", "DOM"],
+    "lib": [
+      "ES2021",
+      "ES2022.Object",
+      "DOM"
+    ],
     "module": "NodeNext",
     "moduleResolution": "nodenext",
     "esModuleInterop": true,
@@ -17,19 +21,26 @@
     "strict": true,
     "outDir": "dist"
   },
-  "include": ["src/**/*"],
-  "exclude": ["node_modules", "dist", "coverage"],
+  "include": [
+    "src/**/*"
+  ],
+  "exclude": [
+    "node_modules",
+    "dist",
+    "coverage"
+  ],
   "includeVersion": true,
   "typedocOptions": {
     "entryPoints": [
-      "src/client.mts",
-      "src/schema.ts",
-      "src/types.mts"
+      "src/client.mts"
     ],
     "readme": "none",
     "out": "docs",
     "plugin": [
       "typedoc-plugin-markdown"
-    ]
+    ],
+    "excludePrivate": true,
+    "excludeProtected": true,
+    "excludeExternals": false
   }
 }