langgraph: release 0.2.66 (#3128)

- async tests are placed in test_pregel_async, not in test_pregel
- to avoid tests placed in wrong file being accidentally skipped i've
added the auto-async mark to sync test file

.github/workflows/_test_langgraph.yml

@@ -17,21 +17,11 @@ jobs:
           - "3.11"
           - "3.12"
           - "3.13"
-        core-version:
-          - "latest"
-        ff-send-v2:
-          - "false"
-        include:
-          - python-version: "3.11"
-            core-version: ">=0.2.42,<0.3.0"
-          - python-version: "3.11"
-            core-version: "latest"
-            ff-send-v2: "true"
 
     defaults:
       run:
         working-directory: libs/langgraph
-    name: "test #${{ matrix.python-version }} (langchain-core: ${{ matrix.core-version }}, ff-send-v2: ${{ matrix.ff-send-v2 }})"
+    name: "test #${{ matrix.python-version }}"
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
@@ -51,14 +41,9 @@ jobs:
         shell: bash
         run: |
           poetry install --with dev
-          if [ "${{ matrix.core-version }}" != "latest" ]; then
-            poetry run pip install "langchain-core${{ matrix.core-version }}"
-          fi
 
       - name: Run tests
         shell: bash
-        env:
-          LANGGRAPH_FF_SEND_V2: ${{ matrix.ff-send-v2 }}
         run: |
           make test_parallel
 

.github/workflows/ci.yml

@@ -31,7 +31,6 @@ jobs:
             "libs/cli",
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
-            "libs/checkpoint-duckdb",
             "libs/checkpoint-postgres",
             "libs/scheduler-kafka",
           ]
@@ -44,12 +43,12 @@ jobs:
     name: cd ${{ matrix.working-directory }}
     strategy:
       matrix:
-        working-directory: [
+        working-directory:
+          [
             "libs/cli",
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
-            "libs/checkpoint-duckdb",
-            "libs/checkpoint-postgres"
+            "libs/checkpoint-postgres",
           ]
     uses: ./.github/workflows/_test.yml
     with:
@@ -76,7 +75,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: '3.11'
+          python-version: "3.11"
       - name: Run check_sdk_methods script
         run: python .github/scripts/check_sdk_methods.py
 
@@ -133,7 +132,16 @@ jobs:
 
   ci_success:
     name: "CI Success"
-    needs: [lint, lint-js, test, test-langgraph, test-scheduler-kafka, integration-test, test-js]
+    needs:
+      [
+        lint,
+        lint-js,
+        test,
+        test-langgraph,
+        test-scheduler-kafka,
+        integration-test,
+        test-js,
+      ]
     if: |
       always()
     runs-on: ubuntu-latest

.github/workflows/deploy_docs.yml

@@ -86,6 +86,7 @@ jobs:
             echo "Running link check on all HTML files matching notebooks in docs directory..."
             poetry run pytest -v \
               --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
+              --check-links-ignore "https://academy\.langchain\.com/.*" \
               --check-links-ignore "https://x.com/.*" \
               --check-links-ignore "https://github\.com/.*" \
               --check-links-ignore "http://localhost:8123/.*" \
@@ -107,6 +108,7 @@ jobs:
               echo "Running link check on HTML files matching changed notebook files..."
               poetry run pytest -v \
                 --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
+                --check-links-ignore "https://academy\.langchain\.com/.*" \
                 --check-links-ignore "http://localhost:8123/.*" \
                 --check-links-ignore "http://localhost:2024.*" \
                 --check-links-ignore "http://127.0.0.1:.*" \

README.md

@@ -12,25 +12,48 @@
 
 ## Overview
 
-[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
+[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building
+stateful, multi-actor applications with LLMs, used to create agent and multi-agent
+workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraph/tutorials/introduction/).
+
 
 LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
 
-[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger),
+### Why use LangGraph?
 
-To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
+LangGraph provides fine-grained control over both the flow and state of your
+agent applications. It implements a central
+[persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/),
+enabling features that are common to most agent architectures:
 
-### Key Features
+- **Memory**: LangGraph persists arbitrary aspects of your application's state,
+supporting memory of conversations and other updates within and across user
+interactions;
+- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted
+and resumed, allowing for decisions, validation, and corrections at key stages via
+human input.
 
-- **Cycles and Branching**: Implement loops and conditionals in your apps.
-- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
-- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
-- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
-- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+Standardizing these components allows individuals and teams to focus on the behavior
+of their agent, instead of its supporting infrastructure.
+
+Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for
+the development, deployment, debugging, and monitoring of your applications.
+
+LangGraph integrates seamlessly with
+[LangChain](https://python.langchain.com/docs/introduction/) and
+[LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+
+To learn more about LangGraph, check out our first LangChain Academy
+course, *Introduction to LangGraph*, available for free
+[here](https://academy.langchain.com/courses/intro-to-langgraph).
 
 ### LangGraph Platform
 
-LangGraph Platform is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework.
+[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger).
+
+See deployment options [here](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
+(includes a free tier).
+
 Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
 
 - **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
@@ -47,9 +70,7 @@ pip install -U langgraph
 
 ## Example
 
-One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
-
-Let's take a look at a simple example of an agent that can use a search tool.
+Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
 
 ```shell
 pip install langchain-anthropic
@@ -66,10 +87,72 @@ export LANGSMITH_TRACING=true
 export LANGSMITH_API_KEY=lsv2_sk_...
 ```
 
-```python
-from typing import Annotated, Literal, TypedDict
+The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
+
+<details open>
+  <summary>High-level implementation</summary>
+
+```python
+from langgraph.prebuilt import create_react_agent
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+
+# Define the tools for the agent to use
+@tool
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."
+
+
+tools = [search]
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
+
+# Initialize memory to persist state between graph runs
+checkpointer = MemorySaver()
+
+app = create_react_agent(model, tools, checkpointer=checkpointer)
+
+# Use the agent
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+```
+"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
+```
+
+Now when we pass the same <code>"thread_id"</code>, the conversation context is retained via the saved state (i.e. stored list of messages)
+
+```python
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what about ny"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+
+```
+"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
+```
+</details>
+
+> [!TIP]
+> LangGraph is a **low-level** framework that allows you to implement any custom agent
+architectures. Click on the low-level implementation below to see how to implement a
+tool-calling agent from scratch.
+
+<details>
+<summary>Low-level implementation</summary>
+
+```python
+from typing import Literal
 
-from langchain_core.messages import HumanMessage
 from langchain_anthropic import ChatAnthropic
 from langchain_core.tools import tool
 from langgraph.checkpoint.memory import MemorySaver
@@ -91,7 +174,7 @@ tools = [search]
 
 tool_node = ToolNode(tools)
 
-model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0).bind_tools(tools)
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
 
 # Define the function that determines whether to continue or not
 def should_continue(state: MessagesState) -> Literal["tools", END]:
@@ -145,92 +228,102 @@ checkpointer = MemorySaver()
 # Note that we're (optionally) passing the memory when compiling the graph
 app = workflow.compile(checkpointer=checkpointer)
 
-# Use the Runnable
+# Use the agent
 final_state = app.invoke(
-    {"messages": [HumanMessage(content="what is the weather in sf")]},
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
     config={"configurable": {"thread_id": 42}}
 )
 final_state["messages"][-1].content
 ```
 
-```
-"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
-```
+<b>Step-by-step Breakdown</b>:
 
-Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
+<details>
+<summary>Initialize the model and tools.</summary>
+<ul>
+  <li>
+    We use <code>ChatAnthropic</code> as our LLM. <strong>NOTE:</strong> we need to make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the <code>.bind_tools()</code> method.
+  </li>
+  <li>
+    We define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that <a href="https://python.langchain.com/docs/modules/agents/tools/custom_tools">here</a>.
+  </li>
+</ul>
+</details>
 
-```python
-final_state = app.invoke(
-    {"messages": [HumanMessage(content="what about ny")]},
-    config={"configurable": {"thread_id": 42}}
-)
-final_state["messages"][-1].content
-```
+<details>
+<summary>Initialize graph with state.</summary>
 
-```
-"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
-```
+<ul>
+    <li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
+    <li><code>MessagesState</code> is a prebuilt state schema that has one attribute -- a list of LangChain <code>Message</code> objects, as well as logic for merging the updates from each node into the state.</li>
+</ul>
+</details>
 
-### Step-by-step Breakdown
+<details>
+<summary>Define graph nodes.</summary>
 
-1. <details>
-    <summary>Initialize the model and tools.</summary>
+There are two main nodes we need:
 
-    - we use `ChatAnthropic` as our LLM. **NOTE:** we need make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the `.bind_tools()` method.
-    - we define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
-   </details>
+<ul>
+    <li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
+    <li>The <code>tools</code> node that invokes tools: if the agent decides to take an action, this node will then execute that action.</li>
+</ul>
+</details>
 
-2. <details>
-    <summary>Initialize graph with state.</summary>
+<details>
+<summary>Define entry point and graph edges.</summary>
 
-    - we initialize graph (`StateGraph`) by passing state schema (in our case `MessagesState`)
-    - `MessagesState` is a prebuilt state schema that has one attribute -- a list of LangChain `Message` objects, as well as logic for merging the updates from each node into the state
-   </details>
+First, we need to set the entry point for graph execution - <code>agent</code> node.
 
-3. <details>
-    <summary>Define graph nodes.</summary>
+Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (<code>MessagesState</code>). In our case, the destination is not known until the agent (LLM) decides.
 
-    There are two main nodes we need:
+<ul>
+  <li>Conditional edge: after the agent is called, we should either:
+    <ul>
+      <li>a. Run tools if the agent said to take an action, OR</li>
+      <li>b. Finish (respond to the user) if the agent did not ask to run tools</li>
+    </ul>
+  </li>
+  <li>Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next</li>
+</ul>
+</details>
 
-      - The `agent` node: responsible for deciding what (if any) actions to take.
-      - The `tools` node that invokes tools: if the agent decides to take an action, this node will then execute that action.
-   </details>
+<details>
+<summary>Compile the graph.</summary>
 
-4. <details>
-    <summary>Define entry point and graph edges.</summary>
+<ul>
+  <li>
+    When we compile the graph, we turn it into a LangChain 
+    <a href="https://python.langchain.com/v0.2/docs/concepts/#runnable-interface">Runnable</a>, 
+    which automatically enables calling <code>.invoke()</code>, <code>.stream()</code> and <code>.batch()</code> 
+    with your inputs
+  </li>
+  <li>
+    We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, 
+    human-in-the-loop workflows, time travel and more. In our case we use <code>MemorySaver</code> - 
+    a simple in-memory checkpointer
+  </li>
+</ul>
+</details>
 
-      First, we need to set the entry point for graph execution - `agent` node.
+<details>
+<summary>Execute the graph.</summary>
 
-      Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (`MessageState`). In our case, the destination is not known until the agent (LLM) decides.
-
-      - Conditional edge: after the agent is called, we should either:
-        - a. Run tools if the agent said to take an action, OR
-        - b. Finish (respond to the user) if the agent did not ask to run tools
-      - Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
-   </details>
-
-5. <details>
-    <summary>Compile the graph.</summary>
-
-    - When we compile the graph, we turn it into a LangChain [Runnable](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface), which automatically enables calling `.invoke()`, `.stream()` and `.batch()` with your inputs
-    - We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, human-in-the-loop workflows, time travel and more. In our case we use `MemorySaver` - a simple in-memory checkpointer
-    </details>
-
-6. <details>
-   <summary>Execute the graph.</summary>
-
-    1. LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, `"agent"`.
-    2. The `"agent"` node executes, invoking the chat model.
-    3. The chat model returns an `AIMessage`. LangGraph adds this to the state.
-    4. Graph cycles the following steps until there are no more `tool_calls` on `AIMessage`:
-
-        - If `AIMessage` has `tool_calls`, `"tools"` node executes
-        - The `"agent"` node executes again and returns `AIMessage`
-
-    5. Execution progresses to the special `END` value and outputs the final state.
-    And as a result, we get a list of all our chat messages as output.
-   </details>
+<ol>
+  <li>LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, <code>"agent"</code>.</li>
+  <li>The <code>"agent"</code> node executes, invoking the chat model.</li>
+  <li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
+  <li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
+    <ul>
+      <li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
+      <li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
+    </ul>
+  </li>
+  <li>Execution progresses to the special <code>END</code> value and outputs the final state. And as a result, we get a list of all our chat messages as output.</li>
+</ol>
+</details>
 
+</details>
 
 ## Documentation
 

docs/cassettes/create-react-agent-structured-output_9ffff6c3-a4f5-47c9-b51d-97caaee85cd6.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/create-react-agent-structured-output_d1386f99-ffd1-4b36-86ec-cabb3357d929.msgpack.zlib

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docs/cassettes/introduction_6a385b06-8d34-4a2d-aded-1cf4bb0ca590.msgpack.zlib

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docs/cassettes/introduction_df6d81bb-e674-44ef-a46e-17b1a8d2381a.msgpack.zlib

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docs/cassettes/introduction_effb95d9-b7d5-40c5-9253-253d193b23b2.msgpack.zlib

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docs/cassettes/introduction_faa345c6-38a2-42e8-9035-9cf56f7bb5b1.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/docs/cloud/reference/api/openapi_control_plane.json

@@ -561,6 +561,16 @@
           },
           "resource": {
             "$ref": "#/components/schemas/ResourceService"
+          },
+          "status": {
+            "type": "string",
+            "enum": [
+              "AWAITING_DATABASE",
+              "READY",
+              "AWAITING_DELETE",
+              "UNKNOWN"
+            ],
+            "description": "Deployment status of the project.\n\nNon-terminal statuses: `AWAITING_DATABASE`, `AWAITING_DELETE`. All other statuses are terminal."
           }
         }
       },

docs/docs/cloud/reference/cli.md

@@ -4,20 +4,26 @@ The LangGraph command line interface includes commands to build and run a LangGr
 
 ## Installation
 
-1. Ensure that Docker is installed (e.g. `docker --version`).
-2. Install the `langgraph-cli` package:
- 
-    === "pip"
-        ```bash    
-        pip install langgraph-cli
-        ```
+1.  Ensure that Docker is installed (e.g. `docker --version`).
+2.  Install the CLI package:
 
-    === "Homebrew (MacOS only)"
+    === "Python"
         ```bash
+        pip install langgraph-cli
+
+        # Install via Homebrew
         brew install langgraph-cli
         ```
- 
-3. Run the command `langgraph --help` to confirm that the CLI is installed.
+
+    === "JS"
+        ```bash
+        npx @langchain/langgraph-cli
+
+        # Install globally, will be available as `langgraphjs`
+        npm install -g @langchain/langgraph-cli
+        ```
+
+3.  Run the command `langgraph --help` or `npx @langchain/langgraph-cli --help` to confirm that the CLI is working correctly.
 
 [](){#langgraph.json}
 
@@ -25,17 +31,6 @@ The LangGraph command line interface includes commands to build and run a LangGr
 
 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 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> |
-| `auth`             | _(Added in v0.0.11)_ Auth configuration containing the path to your authentication handler. Example: `./your_package/auth.py:auth`, where `auth` is an instance of `langgraph_sdk.Auth`. See [authentication guide](../../concepts/auth.md) for details.                                                                                                                                                                                                                                                                                                                      |
-| `env`              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
-| `store`            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, meaningto index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul>                                                                  |
-| `python_version`   | `3.11` or `3.12`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
-| `pip_config_file`  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-
 <div class="admonition tip">
     <p class="admonition-title">Note</p>
     <p>
@@ -43,253 +38,418 @@ The LangGraph CLI requires a JSON configuration file with the following keys:
     </p>
 </div>
 
+=== "Python"
+
+    | Key                                                          | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+    | ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+    | <span style="white-space: nowrap;">`dependencies`</span>     | **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.                                                                                                                                                                                                                                                                                     |
+    | <span style="white-space: nowrap;">`graphs`</span>           | **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>                                    |
+    | <span style="white-space: nowrap;">`auth`</span>             | _(Added in v0.0.11)_ Auth configuration containing the path to your authentication handler. Example: `./your_package/auth.py:auth`, where `auth` is an instance of `langgraph_sdk.Auth`. See [authentication guide](../../concepts/auth.md) for details.                                                                                                                                                                                                                                                                                                                        |
+    | <span style="white-space: nowrap;">`env`</span>              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+    | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, which means to index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul> |
+    | <span style="white-space: nowrap;">`python_version`</span>   | `3.11` or `3.12`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+    | <span style="white-space: nowrap;">`node_version`</span>     | Specify `node_version: 20` to use LangGraph.js.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+    | <span style="white-space: nowrap;">`pip_config_file`</span>  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+    | <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+
+=== "JS"
+
+    | Key                                                          | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+    | ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+    | <span style="white-space: nowrap;">`graphs`</span>           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./src/graph.ts:variable`, where `variable` is an instance of `CompiledStateGraph`</li><li>`./src/graph.ts:makeGraph`, where `makeGraph` is a function that takes a config dictionary (`LangGraphRunnableConfig`) and creates an instance of `StateGraph` / `CompiledStateGraph`.</li></ul>                                    |
+    | <span style="white-space: nowrap;">`env`</span>              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+    | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, which means to index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul> |
+    | <span style="white-space: nowrap;">`node_version`</span>     | Specify `node_version: 20` to use LangGraph.js.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+    | <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+
 ### Examples
 
-#### Basic Configuration
+=== "Python"
+    
+    #### Basic Configuration
 
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "chat": "./chat/graph.py:graph"
-  }
-}
-```
-
-#### Adding semantic search to the store
-
-All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.
-
-The `fields` configuration determines which parts of your documents to embed:
-
-- If omitted or set to `["$"]`, the entire document will be embedded
-- To embed specific fields, use JSON path notation: `["metadata.title", "content.text"]`
-- Documents missing specified fields will still be stored but won't have embeddings for those fields
-- You can still override which fields to embed on a specific item at `put` time using the `index` parameter
-
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "memory_agent": "./agent/graph.py:graph"
-  },
-  "store": {
-    "index": {
-      "embed": "openai:text-embedding-3-small",
-      "dims": 1536,
-      "fields": ["$"]
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "chat": "./chat/graph.py:graph"
+      }
     }
-  }
-}
-```
+    ```
 
-!!! note "Common model dimensions"
-        - openai:text-embedding-3-large: 3072
-        - openai:text-embedding-3-small: 1536
-        - openai:text-embedding-ada-002: 1536
-        - cohere:embed-english-v3.0: 1024
-        - cohere:embed-english-light-v3.0: 384
-        - cohere:embed-multilingual-v3.0: 1024
-        - cohere:embed-multilingual-light-v3.0: 384
+    #### Adding semantic search to the store
 
-#### Semantic search with a custom embedding function
+    All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.
 
-If you want to use semantic search with a custom embedding function, you can pass a path to a custom embedding function:
+    The `fields` configuration determines which parts of your documents to embed:
 
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "memory_agent": "./agent/graph.py:graph"
-  },
-  "store": {
-    "index": {
-      "embed": "./embeddings.py:embed_texts",
-      "dims": 768,
-      "fields": ["text", "summary"]
-    }
-  }
-}
-```
+    - If omitted or set to `["$"]`, the entire document will be embedded
+    - To embed specific fields, use JSON path notation: `["metadata.title", "content.text"]`
+    - Documents missing specified fields will still be stored but won't have embeddings for those fields
+    - You can still override which fields to embed on a specific item at `put` time using the `index` parameter
 
-The `embed` field in store configuration can reference a custom function that takes a list of strings and returns a list of embeddings. Example implementation:
-
-```python
-# embeddings.py
-def embed_texts(texts: list[str]) -> list[list[float]]:
-    """Custom embedding function for semantic search."""
-    # Implementation using your preferred embedding model
-    return [[0.1, 0.2, ...] for _ in texts]  # dims-dimensional vectors
-```
-
-#### Adding custom authentication
-
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "chat": "./chat/graph.py:graph"
-  },
-  "auth": {
-    "path": "./auth.py:auth",
-    "openapi": {
-      "securitySchemes": {
-        "apiKeyAuth": {
-          "type": "apiKey",
-          "in": "header",
-          "name": "X-API-Key"
-        }
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "memory_agent": "./agent/graph.py:graph"
       },
-      "security": [
-        {"apiKeyAuth": []}
-      ]
-    },
-    "disable_studio_auth": false
-  }
-}
-```
+      "store": {
+        "index": {
+          "embed": "openai:text-embedding-3-small",
+          "dims": 1536,
+          "fields": ["$"]
+        }
+      }
+    }
+    ```
+
+    !!! note "Common model dimensions" 
+        - `openai:text-embedding-3-large`: 3072 
+        - `openai:text-embedding-3-small`: 1536 
+        - `openai:text-embedding-ada-002`: 1536 
+        - `cohere:embed-english-v3.0`: 1024 
+        - `cohere:embed-english-light-v3.0`: 384 
+        - `cohere:embed-multilingual-v3.0`: 1024 
+        - `cohere:embed-multilingual-light-v3.0`: 384
+
+    #### Semantic search with a custom embedding function
+
+    If you want to use semantic search with a custom embedding function, you can pass a path to a custom embedding function:
+
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "memory_agent": "./agent/graph.py:graph"
+      },
+      "store": {
+        "index": {
+          "embed": "./embeddings.py:embed_texts",
+          "dims": 768,
+          "fields": ["text", "summary"]
+        }
+      }
+    }
+    ```
+
+    The `embed` field in store configuration can reference a custom function that takes a list of strings and returns a list of embeddings. Example implementation:
+
+    ```python
+    # embeddings.py
+    def embed_texts(texts: list[str]) -> list[list[float]]:
+        """Custom embedding function for semantic search."""
+        # Implementation using your preferred embedding model
+        return [[0.1, 0.2, ...] for _ in texts]  # dims-dimensional vectors
+    ```
+
+    #### Adding custom authentication
+
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "chat": "./chat/graph.py:graph"
+      },
+      "auth": {
+        "path": "./auth.py:auth",
+        "openapi": {
+          "securitySchemes": {
+            "apiKeyAuth": {
+              "type": "apiKey",
+              "in": "header",
+              "name": "X-API-Key"
+            }
+          },
+          "security": [{ "apiKeyAuth": [] }]
+        },
+        "disable_studio_auth": false
+      }
+    }
+    ```
+
+    See the [authentication conceptual guide](../../concepts/auth.md) for details, and the [setting up custom authentication](../../tutorials/auth/getting_started.md) guide for a practical walk through of the process.
+
+
+=== "JS"
+    
+    #### Basic Configuration
+
+    ```json
+    {
+      "graphs": {
+        "chat": "./src/graph.ts:graph"
+      }
+    }
+    ```
 
-See the [authentication conceptual guide](../../concepts/auth.md) for details, and the [setting up custom authentication](../../tutorials/auth/getting_started.md) guide for a practical walk through of the process.
 
 ## Commands
 
-The base command for the LangGraph CLI is `langgraph`.
-
 **Usage**
 
-```
-langgraph [OPTIONS] COMMAND [ARGS]
-```
+=== "Python"
+
+    The base command for the LangGraph CLI is `langgraph`.
+
+    ```
+    langgraph [OPTIONS] COMMAND [ARGS]
+    ```
+=== "JS"
+
+    The base command for the LangGraph.js CLI is `langgraphjs`. 
+
+    ```
+    npx @langchain/langgraph-cli [OPTIONS] COMMAND [ARGS]
+    ```
+
+    We recommend using `npx` to always use the latest version of the CLI.
 
 ### `dev`
 
-Run LangGraph API server in development mode with hot reloading and debugging capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
+=== "Python"
 
-!!! note "Python only"
+    Run LangGraph API server in development mode with hot reloading and debugging capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
 
-    Currently, the CLI only supports Python >= 3.11.
-    JS support is coming soon.
+    !!! note
 
-**Installation**
+        Currently, the CLI only supports Python >= 3.11.
 
-This command requires the "inmem" extra to be installed:
+    **Installation**
 
-```bash
-pip install -U "langgraph-cli[inmem]"
-```
+    This command requires the "inmem" extra to be installed:
 
-**Usage**
+    ```bash
+    pip install -U "langgraph-cli[inmem]"
+    ```
 
-```
-langgraph dev [OPTIONS]
-```
+    **Usage**
 
-**Options**
+    ```
+    langgraph dev [OPTIONS]
+    ```
 
-| Option                        | Default          | Description                                                                         |
-| ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
-| `-c, --config FILE`           | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
-| `--host TEXT`                 | `127.0.0.1`      | Host to bind the server to                                                          |
-| `--port INTEGER`              | `2024`           | Port to bind the server to                                                          |
-| `--no-reload`                 |                  | Disable auto-reload                                                                 |
-| `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
-| `--no-browser`                |                  | Disable automatic browser opening                                                   |
-| `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
-| `--help`                      |                  | Display command documentation                                                       |
+    **Options**
+
+    | Option                        | Default          | Description                                                                         |
+    | ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
+    | `-c, --config FILE`           | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
+    | `--host TEXT`                 | `127.0.0.1`      | Host to bind the server to                                                          |
+    | `--port INTEGER`              | `2024`           | Port to bind the server to                                                          |
+    | `--no-reload`                 |                  | Disable auto-reload                                                                 |
+    | `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
+    | `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
+    | `--help`                      |                  | Display command documentation                                                       |
+
+
+=== "JS"
+
+    Run LangGraph API server in development mode with hot reloading capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
+
+    **Usage**
+
+    ```
+    npx @langchain/langgraph-cli dev [OPTIONS]
+    ```
+
+    **Options**
+
+    | Option                        | Default          | Description                                                                         |
+    | ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
+    | `-c, --config FILE`           | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
+    | `--host TEXT`                 | `127.0.0.1`      | Host to bind the server to                                                          |
+    | `--port INTEGER`              | `2024`           | Port to bind the server to                                                          |
+    | `--no-reload`                 |                  | Disable auto-reload                                                                 |
+    | `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
+    | `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
+    | `--help`                      |                  | Display command documentation                                                       |
 
 ### `build`
 
-Build LangGraph Cloud API server Docker image.
+=== "Python"
 
-**Usage**
+    Build LangGraph Cloud API server Docker image.
 
-```
-langgraph build [OPTIONS]
-```
+    **Usage**
 
-**Options**
+    ```
+    langgraph build [OPTIONS]
+    ```
+
+    **Options**
+
+    | Option               | Default          | Description                                                                                                                  |
+    | -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
+    | `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
+    | `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
+    | `--pull / --no-pull` | `--pull`         | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images. |
+    | `-c, --config FILE`  | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables.                                         |
+    | `--help`             |                  | Display command documentation.                                                                                               |
+
+=== "JS"
+
+    Build LangGraph Cloud API server Docker image.
+
+    **Usage**
+
+    ```
+    npx @langchain/langgraph-cli build [OPTIONS]
+    ```
+
+    **Options**
+
+    | Option               | Default          | Description                                                                                                                  |
+    | -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
+    | `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
+    | `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
+    | `--no-pull`          |                  | Use locally built images. Defaults to `false` to build with latest remote Docker image.                                      |
+    | `-c, --config FILE`  | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables.                                         |
+    | `--help`             |                  | Display command documentation.                                                                                               |
 
-| Option               | Default          | Description                                                                                                                  |
-| -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
-| `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
-| `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
-| `--pull / --no-pull` | `--pull`         | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images. |
-| `-c, --config FILE`  | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables.                                         |
-| `--help`             |                  | Display command documentation.                                                                                               |
 
 ### `up`
 
-Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
+=== "Python"
 
-**Usage**
+    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
 
-```
-langgraph up [OPTIONS]
-```
+    **Usage**
 
-**Options**
+    ```
+    langgraph up [OPTIONS]
+    ```
 
-| Option                       | Default                   | Description                                                                                                             |
-| ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
-| `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
-| `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
-| `--watch`                    |                           | Restart on file changes                                                                                                 |
-| `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
-| `--debugger-port INTEGER`    |                           | Pull the debugger image locally and serve the UI on specified port                                                      |
-| `--verbose`                  |                           | Show more output from the server logs.                                                                                  |
-| `-c, --config FILE`          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
-| `-d, --docker-compose FILE`  |                           | Path to docker-compose.yml file with additional services to launch.                                                     |
-| `-p, --port INTEGER`         | `8123`                    | Port to expose. Example: `langgraph up --port 8000`                                                                     |
-| `--pull / --no-pull`         | `pull`                    | Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull` |
-| `--recreate / --no-recreate` | `no-recreate`             | Recreate containers even if their configuration and image haven't changed                                               |
-| `--help`                     |                           | Display command documentation.                                                                                          |
+    **Options**
+
+    | Option                       | Default                   | Description                                                                                                             |
+    | ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
+    | `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
+    | `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
+    | `--watch`                    |                           | Restart on file changes                                                                                                 |
+    | `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
+    | `--debugger-port INTEGER`    |                           | Pull the debugger image locally and serve the UI on specified port                                                      |
+    | `--verbose`                  |                           | Show more output from the server logs.                                                                                  |
+    | `-c, --config FILE`          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
+    | `-d, --docker-compose FILE`  |                           | Path to docker-compose.yml file with additional services to launch.                                                     |
+    | `-p, --port INTEGER`         | `8123`                    | Port to expose. Example: `langgraph up --port 8000`                                                                     |
+    | `--pull / --no-pull`         | `pull`                    | Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull` |
+    | `--recreate / --no-recreate` | `no-recreate`             | Recreate containers even if their configuration and image haven't changed                                               |
+    | `--help`                     |                           | Display command documentation.                                                                                          |
+
+=== "JS"
+
+    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
+
+    **Usage**
+
+    ```
+    npx @langchain/langgraph-cli up [OPTIONS]
+    ```
+
+    **Options**
+
+    | Option                                                                 | Default                   | Description                                                                                                             |
+    | ---------------------------------------------------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
+    | <span style="white-space: nowrap;">`--wait`</span>                     |                           | Wait for services to start before returning. Implies --detach                                                           |
+    | <span style="white-space: nowrap;">`--postgres-uri TEXT`</span>        | Local database            | Postgres URI to use for the database.                                                                                   |
+    | <span style="white-space: nowrap;">`--watch`</span>                    |                           | Restart on file changes                                                                                                 |
+    | <span style="white-space: nowrap;">`-c, --config FILE`</span>          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
+    | <span style="white-space: nowrap;">`-d, --docker-compose FILE`</span>  |                           | Path to docker-compose.yml file with additional services to launch.                                                     |
+    | <span style="white-space: nowrap;">`-p, --port INTEGER`</span>         | `8123`                    | Port to expose. Example: `langgraph up --port 8000`                                                                     |
+    | <span style="white-space: nowrap;">`--no-pull`</span>                  |                           | Use locally built images. Defaults to `false` to build with latest remote Docker image.                                 |
+    | <span style="white-space: nowrap;">`--recreate`</span>                 |                           | Recreate containers even if their configuration and image haven't changed                                               |
+    | <span style="white-space: nowrap;">`--help`</span>                     |                           | Display command documentation.                                                                                          |
 
 ### `dockerfile`
 
-Generate a Dockerfile for building a LangGraph Cloud API server Docker image.
+=== "Python"
 
-**Usage**
+    Generate a Dockerfile for building a LangGraph Cloud API server Docker image.
 
-```
-langgraph dockerfile [OPTIONS] SAVE_PATH
-```
+    **Usage**
 
-**Options**
+    ```
+    langgraph dockerfile [OPTIONS] SAVE_PATH
+    ```
 
-| Option              | Default          | Description                                                                                                     |
-| ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
-| `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
-| `--help`            |                  | Show this message and exit.                                                                                     |
+    **Options**
 
-Example:
+    | Option              | Default          | Description                                                                                                     |
+    | ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
+    | `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
+    | `--help`            |                  | Show this message and exit.                                                                                     |
 
-```bash
-langgraph dockerfile -c langgraph.json Dockerfile
-```
+    Example:
 
-This generates a Dockerfile that looks similar to:
+    ```bash
+    langgraph dockerfile -c langgraph.json Dockerfile
+    ```
 
-```dockerfile
-FROM langchain/langgraph-api:3.11
+    This generates a Dockerfile that looks similar to:
 
-ADD ./pipconf.txt /pipconfig.txt
+    ```dockerfile
+    FROM langchain/langgraph-api:3.11
 
-RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn
+    ADD ./pipconf.txt /pipconfig.txt
 
-ADD ./graphs /deps/__outer_graphs/src
-RUN set -ex && \
-    for line in '[project]' \
-                'name = "graphs"' \
-                'version = "0.1"' \
-                '[tool.setuptools.package-data]' \
-                '"*" = ["**/*"]'; do \
-        echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
-    done
+    RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn
 
-RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+    ADD ./graphs /deps/__outer_graphs/src
+    RUN set -ex && \
+        for line in '[project]' \
+                    'name = "graphs"' \
+                    'version = "0.1"' \
+                    '[tool.setuptools.package-data]' \
+                    '"*" = ["**/*"]'; do \
+            echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
+        done
 
-ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
-```
+    RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
 
-???+ note "Updating your langgraph.json file"
-    The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
+    ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
+    ```
+
+    ???+ note "Updating your langgraph.json file"
+         The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
+
+=== "JS"
+
+    Generate a Dockerfile for building a LangGraph Cloud API server Docker image.
+
+    **Usage**
+
+    ```
+    npx @langchain/langgraph-cli dockerfile [OPTIONS] SAVE_PATH
+    ```
+
+    **Options**
+
+    | Option              | Default          | Description                                                                                                     |
+    | ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
+    | `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
+    | `--help`            |                  | Show this message and exit.                                                                                     |
+
+    Example:
+
+    ```bash
+    npx @langchain/langgraph-cli dockerfile -c langgraph.json Dockerfile
+    ```
+
+    This generates a Dockerfile that looks similar to:
+
+    ```dockerfile
+    FROM langchain/langgraphjs-api:20
+    
+    ADD . /deps/agent
+    
+    RUN cd /deps/agent && yarn install
+    
+    ENV LANGSERVE_GRAPHS='{"agent":"./src/react_agent/graph.ts:graph"}'
+    
+    WORKDIR /deps/agent
+    
+    RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not found, skipping") || tsx /api/langgraph_api/js/build.mts
+    ```
+
+    ???+ note "Updating your langgraph.json file"
+         The `npx @langchain/langgraph-cli dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.

docs/docs/concepts/deployment_options.md

@@ -57,6 +57,7 @@ With a Self-Hosted Lite deployment, you are responsible for managing the infrast
 
 You’ll build a Docker image using the [LangGraph CLI](./langgraph_cli.md), which can then be deployed on your own infrastructure.
 
+[Cron jobs](../cloud/how-tos/cron_jobs.md) are not available for Self-Hosted Lite deployments.
 
 For more information, please see:
 

docs/docs/concepts/index.md

@@ -1,6 +1,4 @@
 ---
-hide:
-  - navigation
 title: Concepts
 description: Conceptual Guide for LangGraph
 ---
@@ -15,11 +13,11 @@ The conceptual guide does not cover step-by-step instructions or specific implem
 
 ## LangGraph
 
-**High Level**
+### High Level
 
 - [Why LangGraph?](high_level.md): A high-level overview of LangGraph and its goals.
 
-**Concepts**
+### Concepts
 
 - [LangGraph Glossary](low_level.md): LangGraph workflows are designed as graphs, with nodes representing different components and edges representing the flow of information between them. This guide provides an overview of the key concepts associated with LangGraph graph primitives.
 - [Common Agentic Patterns](agentic_concepts.md): An agent uses an LLM to pick its own control flow to solve more complex problems! Agents are a key building block in many LLM applications. This guide explains the different types of agent architectures and how they can be used to control the flow of an application.

docs/docs/concepts/langgraph_cloud.md

@@ -6,21 +6,29 @@
 
 ## Overview
 
-LangGraph's Cloud SaaS is a managed service for deploying LangGraph APIs, regardless of its definition or dependencies. The service offers managed implementations of checkpointers and stores, allowing you to focus on building the right cognitive architecture for your use case. By handling scalable & secure infrastructure, LangGraph Cloud offers the fastest path to getting your LangGraph API deployed to production.
+LangGraph's Cloud SaaS is a managed service for deploying LangGraph Servers, regardless of its definition or dependencies. The service offers managed implementations of checkpointers and stores, allowing you to focus on building the right cognitive architecture for your use case. By handling scalable & secure infrastructure, LangGraph Cloud SaaS offers the fastest path to getting your LangGraph Server deployed to production.
 
 ## Deployment
 
-A **deployment** is an instance of a LangGraph API. A single deployment can have many [revisions](#revision). When a deployment is created, all the necessary infrastructure (e.g. database, containers, secrets store) are automatically provisioned. See the [architecture diagram](#architecture) below for more details.
+A **deployment** is an instance of a LangGraph Server. A single deployment can have many [revisions](#revision). When a deployment is created, all the necessary infrastructure (e.g. database, containers, secrets store) are automatically provisioned. See the [architecture diagram](#architecture) below for more details.
 
-See the [how-to guide](../cloud/deployment/cloud.md#create-new-deployment) for creating a new deployment.
-
-## Resource Allocation
+Resource Allocation:
 
 | **Deployment Type** | **CPU** | **Memory** | **Scaling**         |
 |---------------------|---------|------------|---------------------|
 | Development         | 1 CPU   | 1 GB       | Up to 1 container   |
 | Production          | 2 CPU   | 2 GB       | Up to 10 containers |
 
+See the [how-to guide](../cloud/deployment/cloud.md#create-new-deployment) for creating a new deployment.
+
+## Persistence
+
+A dedicated database is automatically created for each deployment. The database serves as the [persistence layer](../concepts/persistence.md) for the deployment.
+
+When defining a graph to be deployed to LangGraph Cloud SaaS, a [checkpointer](../concepts/persistence.md#checkpointer-libraries) should not be configured by the user. Instead, a checkpointer is automatically configured for the graph.
+
+There is no direct access to the database. All access to the database occurs through the LangGraph Server APIs.
+
 ## Autoscaling
 `Production` type deployments automatically scale up to 10 containers. Scaling is based on the current request load for a single container. Specifically, the autoscaling implementation scales the deployment so that each container is processing about 10 concurrent requests. For example... 
 

docs/docs/concepts/langgraph_studio.md

@@ -60,7 +60,18 @@ LangGraph Studio (desktop) requires Docker Desktop version 4.24 or higher. Pleas
 
 #### Configuration or environment issues
 
-Another reason your project might fail to start is because your configuration file is defined incorrectly, or you are missing required environment variables. 
+Another reason your project might fail to start is because your configuration file is defined incorrectly, or you are missing required environment variables.
+
+!!! Important "Note (desktop only)"
+
+    LangGraph Studio Desktop automatically populates `LANGCHAIN_*` environment variables for license verification and tracing, regardless of the contents of the `.env` file. All other environment variables defined in `.env` will be read as normal.
+
+#### Incorrect data region (desktop only)
+
+If you receive a license verification error when attempting to start the LangGraph Server, you may be logged into the incorrect LangSmith data region. Ensure that you're logged into the correct LangSmith data region and ensure that the LangSmith account has access to LangGraph platform.
+
+1. In the top right-hand corner, click the user icon and select `Logout`.
+1. At the login screen, click the `Data Region` dropdown menu and select the appropriate data region. Then click `Login to LangSmith`.
 
 ### How does interrupt work?
 

docs/docs/concepts/low_level.md

@@ -359,6 +359,25 @@ Use `Command` when you need to **both** update the graph state **and** route to
 
 Use [conditional edges](#conditional-edges) to route between nodes conditionally without updating the state.
 
+### Navigating to a node in a parent graph
+
+If you are using [subgraphs](#subgraphs), you might want to navigate from a node a subgraph to a different subgraph (i.e. a different node in the parent graph). To do so, you can specify `graph=Command.PARENT` in `Command`:
+
+```python
+def my_node(state: State) -> Command[Literal["my_other_node"]]:
+    return Command(
+        update={"foo": "bar"},
+        goto="other_subgraph",  # where `other_subgraph` is a node in the parent graph
+        graph=Command.PARENT
+    )
+```
+
+!!! note
+
+    Setting `graph` to `Command.PARENT` will navigate to the closest parent graph.
+
+This is particularly useful when implementing [multi-agent handoffs](./multi_agent.md#handoffs).
+
 ### Using inside tools
 
 A common use case is updating graph state from inside a tool. For example, in a customer support application you might want to look up customer information based on their account number or ID in the beginning of the conversation. To update the graph state from the tool, you can return `Command(update={"my_custom_key": "foo", "messages": [...]})` from the tool:

docs/docs/how-tos/create-react-agent-hitl.ipynb

@@ -23,12 +23,12 @@
     "                </a>                   \n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/chat_models/\">\n",
     "                    Chat Models\n",
     "                </a>\n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/tools/\">\n",
     "                    Tools\n",
     "                </a>\n",
     "            </li>            \n",
@@ -368,7 +368,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/create-react-agent-memory.ipynb

@@ -5,7 +5,7 @@
    "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
    "metadata": {},
    "source": [
-    "# How to add memory to the prebuilt ReAct agent\n",
+    "# How to add thread-level memory to a ReAct Agent\n",
     "\n",
     "<div class=\"admonition tip\">\n",
     "    <p class=\"admonition-title\">Prerequisites</p>\n",
@@ -28,12 +28,12 @@
     "                </a>                   \n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/chat_models/\">\n",
     "                    Chat Models\n",
     "                </a>\n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/tools/\">\n",
     "                    Tools\n",
     "                </a>\n",
     "            </li>\n",
@@ -285,7 +285,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

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

@@ -0,0 +1,287 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
+   "metadata": {},
+   "source": [
+    "# How to return structured output from the prebuilt ReAct agent\n",
+    "\n",
+    "!!! info \"Prerequisites\"\n",
+    "    This guide assumes familiarity with the following:\n",
+    "    \n",
+    "    - [Agent Architectures](../../concepts/agentic_concepts/)\n",
+    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
+    "    - [Tools](https://python.langchain.com/docs/concepts/tools/)\n",
+    "    - [Structured Output](https://python.langchain.com/docs/concepts/structured_outputs/)\n",
+    "\n",
+    "To return structured output from the prebuilt ReAct agent you can provide a `response_format` parameter with the desired output schema to [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent]:\n",
+    "\n",
+    "```python\n",
+    "class ResponseFormat(BaseModel):\n",
+    "    \"\"\"Respond to the user in this format.\"\"\"\n",
+    "    my_special_output: str\n",
+    "\n",
+    "\n",
+    "graph = create_react_agent(\n",
+    "    model,\n",
+    "    tools=tools,\n",
+    "    # specify the schema for the structured output using `response_format` parameter\n",
+    "    response_format=ResponseFormat\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "Prebuilt ReAct makes an additional LLM call at the end of the ReAct loop to produce a structured output response. Please see [this guide](../react-agent-structured-output) to learn about other  strategies for returning structured outputs from a tool-calling agent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's install the required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a213e11a-5c62-4ddb-a707-490d91add383",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87a00ce9",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
+   "metadata": {},
+   "source": [
+    "## Code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First we initialize the model we want to use.\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
+    "\n",
+    "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
+    "\n",
+    "from typing import Literal\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
+    "    \"\"\"Use this to get weather information.\"\"\"\n",
+    "    if city == \"nyc\":\n",
+    "        return \"It might be cloudy in nyc\"\n",
+    "    elif city == \"sf\":\n",
+    "        return \"It's always sunny in sf\"\n",
+    "    else:\n",
+    "        raise AssertionError(\"Unknown city\")\n",
+    "\n",
+    "\n",
+    "tools = [get_weather]\n",
+    "\n",
+    "# Define the structured output schema\n",
+    "\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "class WeatherResponse(BaseModel):\n",
+    "    \"\"\"Respond to the user in this format.\"\"\"\n",
+    "\n",
+    "    conditions: str = Field(description=\"Weather conditions\")\n",
+    "\n",
+    "\n",
+    "# Define the graph\n",
+    "\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "graph = create_react_agent(\n",
+    "    model,\n",
+    "    tools=tools,\n",
+    "    # specify the schema for the structured output using `response_format` parameter\n",
+    "    response_format=WeatherResponse,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00407425-506d-4ffd-9c86-987921d8c844",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "\n",
+    "Let's now test our agent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
+    "response = graph.invoke(inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50e273a0-fbdb-4eee-89ca-580fbfb52daf",
+   "metadata": {},
+   "source": [
+    "You can see that the agent output contains a `structured_response` key with the structured output conforming to the specified `WeatherResponse` schema, in addition to the message history under `messages` key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "300748d4-0ed2-470d-8dbc-7c14231e73b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WeatherResponse(conditions='cloudy')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response[\"structured_response\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd9e3487-2cec-44cf-9472-0a51eebeddff",
+   "metadata": {},
+   "source": [
+    "### Customizing prompt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a608548d-77fc-4d7a-845c-32ae9ec0489a",
+   "metadata": {},
+   "source": [
+    "You might need to further customize the second LLM call for the structured output generation and provide a system prompt. To do so, you can pass a tuple (prompt, schema):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d1386f99-ffd1-4b36-86ec-cabb3357d929",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph = create_react_agent(\n",
+    "    model,\n",
+    "    tools=tools,\n",
+    "    # specify both the system prompt and the schema for the structured output\n",
+    "    response_format=(\"Always return capitalized weather conditions\", WeatherResponse),\n",
+    ")\n",
+    "\n",
+    "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
+    "response = graph.invoke(inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91f34991-b406-4fd2-a776-4dd03e3dc3dd",
+   "metadata": {},
+   "source": [
+    "You can verify that the structured response now contains a capitalized value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ba43a67f-127c-45e7-982c-a8210d97a3ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WeatherResponse(conditions='Cloudy')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response[\"structured_response\"]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how-tos/create-react-agent-system-prompt.ipynb

@@ -14,7 +14,7 @@
     "        This guide assumes familiarity with the following:\n",
     "        <ul>\n",
     "            <li>            \n",
-    "                <a href=\"https://python.langchain.com/v0.1/docs/modules/model_io/concepts/#systemmessage\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/messages/#systemmessage\">\n",
     "                    SystemMessage\n",
     "                </a>\n",
     "            </li>\n",
@@ -24,12 +24,12 @@
     "                </a>                   \n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/chat_models/\">\n",
     "                    Chat Models\n",
     "                </a>\n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/tools/\">\n",
     "                    Tools\n",
     "                </a>\n",
     "            </li>\n",
@@ -223,7 +223,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/create-react-agent.ipynb

@@ -5,7 +5,7 @@
    "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
    "metadata": {},
    "source": [
-    "# How to use the prebuilt ReAct agent"
+    "# How to use the pre-built ReAct agent"
    ]
   },
   {
@@ -24,12 +24,12 @@
     "                </a>                   \n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/chat_models/\">\n",
     "                    Chat Models\n",
     "                </a>\n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/tools/\">\n",
     "                    Tools\n",
     "                </a>\n",
     "            </li>\n",
@@ -292,7 +292,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

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

@@ -591,7 +591,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/index.md

@@ -1,6 +1,4 @@
 ---
-hide:
-  - navigation
 title: How-to Guides
 description: How to accomplish common tasks in LangGraph
 ---
@@ -24,10 +22,10 @@ These how-to guides show how to achieve that controllability.
 
 ### Persistence
 
-[LangGraph Persistence](../concepts/persistence.md) makes it easy to persist state across graph runs (thread-level persistence) and across threads (cross-thread persistence). These how-to guides show how to add persistence to your graph.
+[LangGraph Persistence](../concepts/persistence.md) makes it easy to persist state across graph runs (per-thread persistence) and across threads (cross-thread persistence). These how-to guides show how to add persistence to your graph.
 
 - [How to add thread-level persistence to your graph](persistence.ipynb)
-- [How to add thread-level persistence to subgraphs](subgraph-persistence.ipynb)
+- [How to add thread-level persistence to a subgraph](subgraph-persistence.ipynb)
 - [How to add cross-thread persistence to your graph](cross-thread-persistence.ipynb)
 - [How to use Postgres checkpointer for persistence](persistence_postgres.ipynb)
 - [How to use MongoDB checkpointer for persistence](persistence_mongodb.ipynb)
@@ -85,7 +83,10 @@ Other methods:
 
 ### Tool calling
 
-[Tool calling](https://python.langchain.com/docs/concepts/tool_calling/) is a type of chat model API that accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message.
+[Tool calling](https://python.langchain.com/docs/concepts/tool_calling/) is a type of
+[chat model](https://python.langchain.com/docs/concepts/chat_models/) API that accepts
+tool schemas, along with messages, as input and returns invocations of those tools as
+part of the output message.
 
 These how-to guides show common patterns for tool calling with LangGraph:
 
@@ -100,7 +101,7 @@ These how-to guides show common patterns for tool calling with LangGraph:
 
 [Subgraphs](../concepts/low_level.md#subgraphs) allow you to reuse an existing graph from another graph. These how-to guides show how to use subgraphs:
 
-- [How to add and use subgraphs](subgraph.ipynb)
+- [How to use subgraphs](subgraph.ipynb)
 - [How to view and update state in subgraphs](subgraphs-manage-state.ipynb)
 - [How to transform inputs and outputs of a subgraph](subgraph-transform-state.ipynb)
 
@@ -116,7 +117,7 @@ See the [multi-agent tutorials](../tutorials/index.md#multi-agent-systems) for i
 
 ### State Management
 
-- [How to use Pydantic model as state](state-model.ipynb)
+- [How to use Pydantic model as graph state](state-model.ipynb)
 - [How to define input/output schema for your graph](input_output_schema.ipynb)
 - [How to pass private state between nodes inside the graph](pass_private_state.ipynb)
 
@@ -126,7 +127,7 @@ See the [multi-agent tutorials](../tutorials/index.md#multi-agent-systems) for i
 - [How to visualize your graph](visualization.ipynb)
 - [How to add runtime configuration to your graph](configuration.ipynb)
 - [How to add node retries](node-retries.ipynb)
-- [How to force function calling agent to structure output](react-agent-structured-output.ipynb)
+- [How to force tool-calling agent to structure output](react-agent-structured-output.ipynb)
 - [How to pass custom LangSmith run ID for graph runs](run-id-langsmith.ipynb)
 - [How to return state before hitting recursion limit](return-when-recursion-limit-hits.ipynb)
 - [How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](autogen-integration.ipynb)
@@ -139,13 +140,18 @@ One of the big benefits of LangGraph is that you can easily create your own agen
 
 These guides show how to use the prebuilt ReAct agent:
 
-- [How to create a ReAct agent](create-react-agent.ipynb)
-- [How to add memory to a ReAct agent](create-react-agent-memory.ipynb)
+- [How to use the pre-built ReAct agent](create-react-agent.ipynb)
+- [How to add thread-level memory to a ReAct Agent](create-react-agent-memory.ipynb)
 - [How to add a custom system prompt to a ReAct agent](create-react-agent-system-prompt.ipynb)
 - [How to add human-in-the-loop processes to a ReAct agent](create-react-agent-hitl.ipynb)
-- [How to create prebuilt ReAct agent from scratch](react-agent-from-scratch.ipynb)
+- [How to return structured output from a ReAct agent](create-react-agent-structured-output.ipynb)
 - [How to add semantic search for long-term memory to a ReAct agent](memory/semantic-search.ipynb#using-in-create-react-agent)
 
+Interested in further customizing the ReAct agent? This guide provides an
+overview of its underlying implementation to help you customize for your own needs:
+
+- [How to create prebuilt ReAct agent from scratch](react-agent-from-scratch.ipynb)
+
 ## LangGraph Platform
 
 This section includes how-to guides for LangGraph Platform.
@@ -189,11 +195,17 @@ LangGraph applications can be deployed using LangGraph Cloud, which provides a r
 
 [Assistants](../concepts/assistants.md) is a configured instance of a template.
 
+See [SDK Reference](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.client.AssistantsClient)
+for supported endpoints and other details.
+
 - [How to configure agents](../cloud/how-tos/configuration_cloud.md)
 - [How to version assistants](../cloud/how-tos/assistant_versioning.md)
 
 ### Threads
 
+See [SDK Reference](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.client.ThreadsClient)
+for supported endpoints and other details.
+
 - [How to copy threads](../cloud/how-tos/copy_threads.md)
 - [How to check status of your threads](../cloud/how-tos/check_thread_status.md)
 

docs/docs/how-tos/persistence_postgres.ipynb

@@ -44,7 +44,8 @@
     "...\n",
     "```\n",
     "\n",
-    "!!! info \"Setup\n",
+    "!!! info \"Setup\"",
+    "\n",
     "    You need to run `.setup()` once on your checkpointer to initialize the database before you can use it."
    ]
   },

docs/docs/how-tos/react-agent-from-scratch.ipynb

@@ -17,12 +17,12 @@
     "                </a>\n",
     "            </li>                \n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/chat_models\">\n",
     "                    Chat Models\n",
     "                </a>\n",
     "            </li>\n",
     "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/#messages\">\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/messages\">\n",
     "                    Messages\n",
     "                </a>\n",
     "            </li>\n",
@@ -375,7 +375,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

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

@@ -15,7 +15,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# How to return structured output with a ReAct style agent\n",
+    "# How to force tool-calling agent to structure output\n",
     "\n",
     "<div class=\"admonition tip\">\n",
     "    <p class=\"admonition-title\">Prerequisites</p>\n",

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

@@ -5,7 +5,7 @@
    "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53",
    "metadata": {},
    "source": [
-    "# How to use Pydantic model as state\n",
+    "# How to use Pydantic model as graph state\n",
     "\n",
     "<div class=\"admonition tip\">\n",
     "    <p class=\"admonition-title\">Prerequisites</p>\n",

docs/docs/how-tos/subgraph-persistence.ipynb

@@ -5,7 +5,7 @@
    "id": "176e8dbb-1a0a-49ce-a10e-2417e8ea17a0",
    "metadata": {},
    "source": [
-    "# How to add thread-level persistence to subgraphs"
+    "# How to add thread-level persistence to a subgraph"
    ]
   },
   {

docs/docs/how-tos/subgraph.ipynb

@@ -9,7 +9,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# How to add and use subgraphs\n",
+    "# How to use subgraphs\n",
     "\n",
     "<div class=\"admonition tip\">\n",
     "    <p class=\"admonition-title\">Prerequisites</p>\n",

docs/docs/index.md

@@ -1,7 +1,5 @@
 ---
 hide_comments: true
-hide:
-  - navigation
 title: Home
 ---
 

docs/docs/reference/func.md

@@ -0,0 +1,5 @@
+::: langgraph.func
+    options:
+      members:
+        - task
+        - entrypoint

docs/docs/troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE.md

@@ -6,7 +6,7 @@ support it.
 One way this can occur is if you are using a [fanout](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/)
 or other parallel execution in your graph and you have defined a graph like this:
 
-```python
+```python hl_lines="2"
 class State(TypedDict):
     some_key: str
 
@@ -31,7 +31,7 @@ there is uncertainty around how to update the internal state.
 
 To get around this, you can define a reducer that combines multiple values:
 
-```python
+```python hl_lines="5-6"
 import operator
 from typing import Annotated
 

docs/docs/tutorials/deployment.md

@@ -0,0 +1,18 @@
+# Deployment
+
+Get started deploying your LangGraph applications locally or on the cloud with
+[LangGraph Platform](../concepts/langgraph_platform.md).
+
+## Get Started 🚀 {#quick-start}
+
+- [LangGraph Server Quickstart](../tutorials/langgraph-platform/local-server.md): Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI.
+- [LangGraph Template Quickstart](../concepts/template_applications.md): Start building with LangGraph Platform using a template application.
+- [Deploy with LangGraph Cloud Quickstart](../cloud/quick_start.md): Deploy a LangGraph app using LangGraph Cloud.
+
+
+## Deployment Options
+
+- [Self-Hosted Lite](../concepts/self_hosted.md): A free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner
+- [Cloud SaaS](../concepts/langgraph_cloud.md): Hosted as part of LangSmith.
+- [Bring Your Own Cloud](../concepts/bring_your_own_cloud.md): We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud.
+- [Self-Hosted Enterprise](../concepts/self_hosted.md): Completely managed by you.

docs/docs/tutorials/index.md

@@ -1,6 +1,4 @@
 ---
-hide:
-  - navigation
 title: Tutorials
 ---
 

docs/docs/tutorials/langgraph-platform/local-server.md

@@ -1,4 +1,4 @@
-# QuickStart: Launch Local LangGraph Server
+# Quickstart: Launch Local LangGraph Server
 
 This is a quick start guide to help you get a LangGraph app up and running locally.
 
@@ -250,4 +250,4 @@ Access detailed documentation for development and API usage:
 
 - **[LangGraph Server API Reference](../../cloud/reference/api/api_ref.html)**: Explore the LangGraph Server API documentation.  
 - **[Python SDK Reference](../../cloud/reference/sdk/python_sdk_ref.md)**: Explore the Python SDK API Reference.
-- **[JS/TS SDK Reference](../../cloud/reference/sdk/js_ts_sdk_ref.md)**: Explore the Python SDK API Reference.
+- **[JS/TS SDK Reference](../../cloud/reference/sdk/js_ts_sdk_ref.md)**: Explore the JS/TS SDK API Reference.

docs/docs/tutorials/multi_agent/agent_supervisor.ipynb

@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "f04c6778-403b-4b49-9b93-678e910d5cec",
    "metadata": {},
    "outputs": [],
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "df2bd80b-c477-4d74-8faa-1c0548622239",
    "metadata": {},
    "outputs": [],
@@ -162,7 +162,11 @@
     "llm = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n",
     "\n",
     "\n",
-    "def supervisor_node(state: MessagesState) -> Command[Literal[*members, \"__end__\"]]:\n",
+    "class State(MessagesState):\n",
+    "    next: str\n",
+    "\n",
+    "\n",
+    "def supervisor_node(state: State) -> Command[Literal[*members, \"__end__\"]]:\n",
     "    messages = [\n",
     "        {\"role\": \"system\", \"content\": system_prompt},\n",
     "    ] + state[\"messages\"]\n",
@@ -171,7 +175,7 @@
     "    if goto == \"FINISH\":\n",
     "        goto = END\n",
     "\n",
-    "    return Command(goto=goto)"
+    "    return Command(goto=goto, update={\"next\": goto})"
    ]
   },
   {
@@ -201,7 +205,7 @@
     ")\n",
     "\n",
     "\n",
-    "def research_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def research_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = research_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -217,7 +221,7 @@
     "code_agent = create_react_agent(llm, tools=[python_repl_tool])\n",
     "\n",
     "\n",
-    "def code_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def code_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = code_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -229,7 +233,7 @@
     "    )\n",
     "\n",
     "\n",
-    "builder = StateGraph(MessagesState)\n",
+    "builder = StateGraph(State)\n",
     "builder.add_edge(START, \"supervisor\")\n",
     "builder.add_node(\"supervisor\", supervisor_node)\n",
     "builder.add_node(\"researcher\", research_node)\n",

docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb

@@ -293,6 +293,10 @@
     "from langchain_core.messages import HumanMessage, trim_messages\n",
     "\n",
     "\n",
+    "class State(MessagesState):\n",
+    "    next: str\n",
+    "\n",
+    "\n",
     "def make_supervisor_node(llm: BaseChatModel, members: list[str]) -> str:\n",
     "    options = [\"FINISH\"] + members\n",
     "    system_prompt = (\n",
@@ -308,7 +312,7 @@
     "\n",
     "        next: Literal[*options]\n",
     "\n",
-    "    def supervisor_node(state: MessagesState) -> Command[Literal[*members, \"__end__\"]]:\n",
+    "    def supervisor_node(state: State) -> Command[Literal[*members, \"__end__\"]]:\n",
     "        \"\"\"An LLM-based router.\"\"\"\n",
     "        messages = [\n",
     "            {\"role\": \"system\", \"content\": system_prompt},\n",
@@ -318,7 +322,7 @@
     "        if goto == \"FINISH\":\n",
     "            goto = END\n",
     "\n",
-    "        return Command(goto=goto)\n",
+    "        return Command(goto=goto, update={\"next\": goto})\n",
     "\n",
     "    return supervisor_node"
    ]
@@ -358,7 +362,7 @@
     "search_agent = create_react_agent(llm, tools=[tavily_tool])\n",
     "\n",
     "\n",
-    "def search_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def search_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = search_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -374,7 +378,7 @@
     "web_scraper_agent = create_react_agent(llm, tools=[scrape_webpages])\n",
     "\n",
     "\n",
-    "def web_scraper_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def web_scraper_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = web_scraper_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -410,7 +414,7 @@
    },
    "outputs": [],
    "source": [
-    "research_builder = StateGraph(MessagesState)\n",
+    "research_builder = StateGraph(State)\n",
     "research_builder.add_node(\"supervisor\", research_supervisor_node)\n",
     "research_builder.add_node(\"search\", search_node)\n",
     "research_builder.add_node(\"web_scraper\", web_scraper_node)\n",
@@ -528,7 +532,7 @@
     ")\n",
     "\n",
     "\n",
-    "def doc_writing_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def doc_writing_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = doc_writer_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -551,7 +555,7 @@
     ")\n",
     "\n",
     "\n",
-    "def note_taking_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def note_taking_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = note_taking_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -569,7 +573,7 @@
     ")\n",
     "\n",
     "\n",
-    "def chart_generating_node(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def chart_generating_node(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    result = chart_generating_agent.invoke(state)\n",
     "    return Command(\n",
     "        update={\n",
@@ -610,7 +614,7 @@
    "outputs": [],
    "source": [
     "# Create the graph here\n",
-    "paper_writing_builder = StateGraph(MessagesState)\n",
+    "paper_writing_builder = StateGraph(State)\n",
     "paper_writing_builder.add_node(\"supervisor\", doc_writing_supervisor_node)\n",
     "paper_writing_builder.add_node(\"doc_writer\", doc_writing_node)\n",
     "paper_writing_builder.add_node(\"note_taker\", note_taking_node)\n",
@@ -730,7 +734,7 @@
    },
    "outputs": [],
    "source": [
-    "def call_research_team(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def call_research_team(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    response = research_graph.invoke({\"messages\": state[\"messages\"][-1]})\n",
     "    return Command(\n",
     "        update={\n",
@@ -744,7 +748,7 @@
     "    )\n",
     "\n",
     "\n",
-    "def call_paper_writing_team(state: MessagesState) -> Command[Literal[\"supervisor\"]]:\n",
+    "def call_paper_writing_team(state: State) -> Command[Literal[\"supervisor\"]]:\n",
     "    response = paper_writing_graph.invoke({\"messages\": state[\"messages\"][-1]})\n",
     "    return Command(\n",
     "        update={\n",
@@ -759,7 +763,7 @@
     "\n",
     "\n",
     "# Define the graph.\n",
-    "super_builder = StateGraph(MessagesState)\n",
+    "super_builder = StateGraph(State)\n",
     "super_builder.add_node(\"supervisor\", teams_supervisor_node)\n",
     "super_builder.add_node(\"research_team\", call_research_team)\n",
     "super_builder.add_node(\"writing_team\", call_paper_writing_team)\n",

docs/mkdocs.yml

@@ -20,9 +20,9 @@ theme:
     - content.action.edit
     - content.tooltips
     - header.autohide
+    - navigation.indexes
     - navigation.expand
     - navigation.footer
-    - navigation.indexes
     - navigation.instant
     - navigation.sections
     - navigation.instant.prefetch
@@ -30,7 +30,6 @@ theme:
     - navigation.path
     - navigation.prune
     - navigation.tabs
-    - navigation.tabs.sticky
     - navigation.top
     - navigation.tracking
     - search.highlight
@@ -89,281 +88,296 @@ plugins:
             options:
               filters:
                 - "!^_"
-nav:
-  - Home: index.md
-  - Tutorials:
-      - tutorials/index.md
-      - Quick Start:
-        - Quick Start: tutorials#quick-start
-        - tutorials/introduction.ipynb
-        - tutorials/langgraph-platform/local-server.md
-        - cloud/quick_start.md
-      - Chatbots:
-          - Chatbots: tutorials#chatbots
-          - tutorials/customer-support/customer-support.ipynb
-          - tutorials/chatbots/information-gather-prompting.ipynb
-          - tutorials/code_assistant/langgraph_code_assistant.ipynb
-      - RAG:
-          - RAG: tutorials#rag
-          - tutorials/rag/langgraph_adaptive_rag.ipynb
-          - tutorials/rag/langgraph_adaptive_rag_local.ipynb
-          - tutorials/rag/langgraph_agentic_rag.ipynb
-          - tutorials/rag/langgraph_crag.ipynb
-          - tutorials/rag/langgraph_crag_local.ipynb
-          - tutorials/rag/langgraph_self_rag.ipynb
-          - tutorials/rag/langgraph_self_rag_local.ipynb
-          - tutorials/sql-agent.ipynb
-      - Agent Architectures:
-          - Agent Architectures: tutorials#agent-architectures
-          - Multi-Agent Systems:
-              - Multi-Agent Systems: tutorials#multi-agent-systems
-              - tutorials/multi_agent/multi-agent-collaboration.ipynb
-              - tutorials/multi_agent/agent_supervisor.ipynb
-              - tutorials/multi_agent/hierarchical_agent_teams.ipynb
-          - Planning Agents:
-              - Planning Agents: tutorials#planning-agents
-              - tutorials/plan-and-execute/plan-and-execute.ipynb
-              - tutorials/rewoo/rewoo.ipynb
-              - tutorials/llm-compiler/LLMCompiler.ipynb
-          - Reflection & Critique:
-              - Reflection & Critique: tutorials#reflection-critique
-              - tutorials/reflection/reflection.ipynb
-              - tutorials/reflexion/reflexion.ipynb
-              - tutorials/tot/tot.ipynb
-              - tutorials/lats/lats.ipynb
-              - tutorials/self-discover/self-discover.ipynb
-      - Evaluation & Analysis:
-          - Evaluation & Analysis: tutorials#evaluation
-          - tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb
-          - tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb
-      - Experimental:
-          - Experimental: tutorials#experimental
-          - tutorials/storm/storm.ipynb
-          - tutorials/tnt-llm/tnt-llm.ipynb
-          - tutorials/web-navigation/web_voyager.ipynb
-          - tutorials/usaco/usaco.ipynb
-          - tutorials/extraction/retries.ipynb
-      - LangGraph Platform:
-          - LangGraph Platform: concepts#langgraph-platform
-          - tutorials/auth/getting_started.md
-          - tutorials/auth/resource_auth.md
-          - tutorials/auth/add_auth_server.md
 
-  - How-to Guides:
-      - how-tos/index.md
-      - LangGraph:
-        - LangGraph: how-tos#langgraph
-        - Controllability:
-            - Controllability: how-tos#controllability
-            - how-tos/branching.ipynb
-            - how-tos/map-reduce.ipynb
-            - how-tos/recursion-limit.ipynb
-            - how-tos/command.ipynb
-        - Persistence:
-            - Persistence: how-tos#persistence
-            - how-tos/persistence.ipynb
-            - how-tos/subgraph-persistence.ipynb
-            - how-tos/cross-thread-persistence.ipynb
-            - how-tos/persistence_postgres.ipynb
-            - how-tos/persistence_mongodb.ipynb
-            - how-tos/persistence_redis.ipynb
-        - Memory:
-            - Memory: how-tos#memory
-            - how-tos/memory/manage-conversation-history.ipynb
-            - how-tos/memory/delete-messages.ipynb
-            - how-tos/memory/add-summary-conversation-history.ipynb
-            - how-tos/memory/semantic-search.ipynb
-        - Human-in-the-loop:
-            - Human-in-the-loop: how-tos#human-in-the-loop
-            - how-tos/human_in_the_loop/breakpoints.ipynb
-            - how-tos/human_in_the_loop/dynamic_breakpoints.ipynb
-            - how-tos/human_in_the_loop/edit-graph-state.ipynb
-            - how-tos/human_in_the_loop/wait-user-input.ipynb
-            - how-tos/human_in_the_loop/time-travel.ipynb
-            - how-tos/human_in_the_loop/review-tool-calls.ipynb
-        - Streaming:
-            - Streaming: how-tos#streaming
-            - how-tos/stream-values.ipynb
-            - how-tos/stream-updates.ipynb
-            - how-tos/streaming-tokens.ipynb
-            - how-tos/streaming-tokens-without-langchain.ipynb
-            - how-tos/streaming-content.ipynb
-            - how-tos/stream-multiple.ipynb
-            - how-tos/streaming-events-from-within-tools.ipynb
-            - how-tos/streaming-events-from-within-tools-without-langchain.ipynb
-            - how-tos/streaming-from-final-node.ipynb
-            - how-tos/streaming-subgraphs.ipynb
-            - how-tos/disable-streaming.ipynb
-        - Tool calling:
-            - Tool calling: how-tos#tool-calling
-            - how-tos/tool-calling.ipynb
-            - how-tos/tool-calling-errors.ipynb
-            - how-tos/pass-run-time-values-to-tools.ipynb
-            - how-tos/update-state-from-tools.ipynb
-            - how-tos/pass-config-to-tools.ipynb
-            - how-tos/many-tools.ipynb
-        - Subgraphs:
-            - Subgraphs: how-tos#subgraphs
-            - how-tos/subgraph.ipynb
-            - how-tos/subgraphs-manage-state.ipynb
-            - how-tos/subgraph-transform-state.ipynb
-        - Multi-agent:
-            - Multi-agent: how-tos#multi-agent
-            - how-tos/agent-handoffs.ipynb
-            - how-tos/multi-agent-network.ipynb
-            - how-tos/multi-agent-multi-turn-convo.ipynb
-        - State Management:
-            - State Management: how-tos#state-management
-            - how-tos/state-model.ipynb
-            - how-tos/input_output_schema.ipynb
-            - how-tos/pass_private_state.ipynb
-        - Other:
-            - Other: how-tos#other
-            - how-tos/async.ipynb
-            - how-tos/visualization.ipynb
-            - how-tos/configuration.ipynb
-            - how-tos/node-retries.ipynb
-            - how-tos/react-agent-structured-output.ipynb
-            - how-tos/run-id-langsmith.ipynb
-            - how-tos/return-when-recursion-limit-hits.ipynb
-        - Prebuilt ReAct Agent:
-            - Prebuilt ReAct Agent: how-tos#prebuilt-react-agent
-            - how-tos/create-react-agent.ipynb
-            - how-tos/create-react-agent-memory.ipynb
-            - how-tos/create-react-agent-system-prompt.ipynb
-            - how-tos/create-react-agent-hitl.ipynb
-            - how-tos/react-agent-from-scratch.ipynb
-      - LangGraph Platform:
-          - LangGraph Platform: how-tos#langgraph-platform
-          - Application Structure:
-              - Application Structure: how-tos#application-structure
-              - cloud/deployment/setup.md
-              - cloud/deployment/setup_pyproject.md
-              - cloud/deployment/setup_javascript.md
-              - cloud/deployment/semantic_search.md
-              - cloud/deployment/custom_docker.md
-              - cloud/deployment/test_locally.md
-              - cloud/deployment/graph_rebuild.md
-          - Deployment:
-              - Deployment: how-tos#deployment
-              - cloud/deployment/cloud.md
-              - how-tos/deploy-self-hosted.md
-              - how-tos/use-remote-graph.md
-          - Authentication & Access Control:
-              - Authentication & Access Control: how-tos#authentication-access-control
-              - cloud/how-tos/auth/custom_auth_new.md
-              - cloud/how-tos/auth/openapi_security_new.md
-          - Assistants:
-              - Assistants: how-tos#assistants
-              - cloud/how-tos/configuration_cloud.md
-              - cloud/how-tos/assistant_versioning.md
-          - Threads:
-              - Threads: how-tos#threads
-              - cloud/how-tos/copy_threads.md
-              - cloud/how-tos/check_thread_status.md
-          - Runs:
-              - Runs: how-tos#runs
-              - cloud/how-tos/background_run.md
-              - cloud/how-tos/same-thread.md
-              - cloud/how-tos/cron_jobs.md
-              - cloud/how-tos/stateless_runs.md
-          - Streaming:
-              - Streaming: how-tos#streaming_1
-              - cloud/how-tos/stream_values.md
-              - cloud/how-tos/stream_updates.md
-              - cloud/how-tos/stream_messages.md
-              - cloud/how-tos/stream_events.md
-              - cloud/how-tos/stream_debug.md
-              - cloud/how-tos/stream_multiple.md
+nav:
+  - Home:
+    - Introduction: index.md
+    - Get started:
+      - Learn the basics: tutorials/introduction.ipynb
+      - Deployment:
+        - tutorials/deployment.md
+        - Local Deploy: tutorials/langgraph-platform/local-server.md
+        - Template Applications: concepts/template_applications.md  # TODO: make tutorial
+        - Cloud Deploy: cloud/quick_start.md
+    - Guides:
+      - How-to Guides:
+        - how-tos/index.md
+        - LangGraph:
+          - LangGraph: how-tos#langgraph
+          - Controllability:
+              - Controllability: how-tos#controllability
+              - how-tos/branching.ipynb
+              - how-tos/map-reduce.ipynb
+              - how-tos/recursion-limit.ipynb
+              - how-tos/command.ipynb
+          - Persistence:
+              - Persistence: how-tos#persistence
+              - how-tos/persistence.ipynb
+              - how-tos/subgraph-persistence.ipynb
+              - how-tos/cross-thread-persistence.ipynb
+              - how-tos/persistence_postgres.ipynb
+              - how-tos/persistence_mongodb.ipynb
+              - how-tos/persistence_redis.ipynb
+          - Memory:
+              - Memory: how-tos#memory
+              - how-tos/memory/manage-conversation-history.ipynb
+              - how-tos/memory/delete-messages.ipynb
+              - how-tos/memory/add-summary-conversation-history.ipynb
+              - how-tos/memory/semantic-search.ipynb
           - Human-in-the-loop:
-              - Human-in-the-loop: how-tos#human-in-the-loop_1
-              - cloud/how-tos/human_in_the_loop_breakpoint.md
-              - cloud/how-tos/human_in_the_loop_user_input.md
-              - cloud/how-tos/human_in_the_loop_edit_state.md
-              - cloud/how-tos/human_in_the_loop_time_travel.md
-              - cloud/how-tos/human_in_the_loop_review_tool_calls.md
-          - Double-texting:
-              - Double-texting: how-tos#double-texting
-              - cloud/how-tos/interrupt_concurrent.md
-              - cloud/how-tos/rollback_concurrent.md
-              - cloud/how-tos/reject_concurrent.md
-              - cloud/how-tos/enqueue_concurrent.md
-          - Webhooks:
-              - cloud/how-tos/webhooks.md
-          - Cron Jobs:
-              - cloud/how-tos/cron_jobs.md
-          - LangGraph Studio:
-              - LangGraph Studio: how-tos#langgraph-studio
-              - cloud/how-tos/test_deployment.md
-              - cloud/how-tos/test_local_deployment.md
-              - cloud/how-tos/invoke_studio.md
-              - cloud/how-tos/threads_studio.md
-              - cloud/how-tos/datasets_studio.md
+              - Human-in-the-loop: how-tos#human-in-the-loop
+              - how-tos/human_in_the_loop/breakpoints.ipynb
+              - how-tos/human_in_the_loop/dynamic_breakpoints.ipynb
+              - how-tos/human_in_the_loop/edit-graph-state.ipynb
+              - how-tos/human_in_the_loop/wait-user-input.ipynb
+              - how-tos/human_in_the_loop/time-travel.ipynb
+              - how-tos/human_in_the_loop/review-tool-calls.ipynb
+          - Streaming:
+              - Streaming: how-tos#streaming
+              - how-tos/stream-values.ipynb
+              - how-tos/stream-updates.ipynb
+              - how-tos/streaming-tokens.ipynb
+              - how-tos/streaming-tokens-without-langchain.ipynb
+              - how-tos/streaming-content.ipynb
+              - how-tos/stream-multiple.ipynb
+              - how-tos/streaming-events-from-within-tools.ipynb
+              - how-tos/streaming-events-from-within-tools-without-langchain.ipynb
+              - how-tos/streaming-from-final-node.ipynb
+              - how-tos/streaming-subgraphs.ipynb
+              - how-tos/disable-streaming.ipynb
+          - Tool calling:
+              - Tool calling: how-tos#tool-calling
+              - how-tos/tool-calling.ipynb
+              - how-tos/tool-calling-errors.ipynb
+              - how-tos/pass-run-time-values-to-tools.ipynb
+              - how-tos/update-state-from-tools.ipynb
+              - how-tos/pass-config-to-tools.ipynb
+              - how-tos/many-tools.ipynb
+          - Subgraphs:
+              - Subgraphs: how-tos#subgraphs
+              - how-tos/subgraph.ipynb
+              - how-tos/subgraphs-manage-state.ipynb
+              - how-tos/subgraph-transform-state.ipynb
+          - Multi-agent:
+              - Multi-agent: how-tos#multi-agent
+              - how-tos/agent-handoffs.ipynb
+              - how-tos/multi-agent-network.ipynb
+              - how-tos/multi-agent-multi-turn-convo.ipynb
+          - State Management:
+              - State Management: how-tos#state-management
+              - how-tos/state-model.ipynb
+              - how-tos/input_output_schema.ipynb
+              - how-tos/pass_private_state.ipynb
+          - Other:
+              - Other: how-tos#other
+              - how-tos/async.ipynb
+              - how-tos/visualization.ipynb
+              - how-tos/configuration.ipynb
+              - how-tos/node-retries.ipynb
+              - how-tos/react-agent-structured-output.ipynb
+              - how-tos/run-id-langsmith.ipynb
+              - how-tos/return-when-recursion-limit-hits.ipynb
+          - Prebuilt ReAct Agent:
+              - Prebuilt ReAct Agent: how-tos#prebuilt-react-agent
+              - how-tos/create-react-agent.ipynb
+              - how-tos/create-react-agent-memory.ipynb
+              - how-tos/create-react-agent-system-prompt.ipynb
+              - how-tos/create-react-agent-hitl.ipynb
+              - how-tos/create-react-agent-structured-output.ipynb
+              - how-tos/react-agent-from-scratch.ipynb
+        - LangGraph Platform:
+            - LangGraph Platform: how-tos#langgraph-platform
+            - Application Structure:
+                - Application Structure: how-tos#application-structure
+                - cloud/deployment/setup.md
+                - cloud/deployment/setup_pyproject.md
+                - cloud/deployment/setup_javascript.md
+                - cloud/deployment/semantic_search.md
+                - cloud/deployment/custom_docker.md
+                - cloud/deployment/test_locally.md
+                - cloud/deployment/graph_rebuild.md
+            - Deployment:
+                - Deployment: how-tos#deployment
+                - cloud/deployment/cloud.md
+                - how-tos/deploy-self-hosted.md
+                - how-tos/use-remote-graph.md
+            - Authentication & Access Control:
+                - Authentication & Access Control: how-tos#authentication-access-control
+                - cloud/how-tos/auth/custom_auth_new.md
+                - cloud/how-tos/auth/openapi_security_new.md
+            - Assistants:
+                - Assistants: how-tos#assistants
+                - cloud/how-tos/configuration_cloud.md
+                - cloud/how-tos/assistant_versioning.md
+            - Threads:
+                - Threads: how-tos#threads
+                - cloud/how-tos/copy_threads.md
+                - cloud/how-tos/check_thread_status.md
+            - Runs:
+                - Runs: how-tos#runs
+                - cloud/how-tos/background_run.md
+                - cloud/how-tos/same-thread.md
+                - cloud/how-tos/cron_jobs.md
+                - cloud/how-tos/stateless_runs.md
+            - Streaming:
+                - Streaming: how-tos#streaming_1
+                - cloud/how-tos/stream_values.md
+                - cloud/how-tos/stream_updates.md
+                - cloud/how-tos/stream_messages.md
+                - cloud/how-tos/stream_events.md
+                - cloud/how-tos/stream_debug.md
+                - cloud/how-tos/stream_multiple.md
+            - Human-in-the-loop:
+                - Human-in-the-loop: how-tos#human-in-the-loop_1
+                - cloud/how-tos/human_in_the_loop_breakpoint.md
+                - cloud/how-tos/human_in_the_loop_user_input.md
+                - cloud/how-tos/human_in_the_loop_edit_state.md
+                - cloud/how-tos/human_in_the_loop_time_travel.md
+                - cloud/how-tos/human_in_the_loop_review_tool_calls.md
+            - Double-texting:
+                - Double-texting: how-tos#double-texting
+                - cloud/how-tos/interrupt_concurrent.md
+                - cloud/how-tos/rollback_concurrent.md
+                - cloud/how-tos/reject_concurrent.md
+                - cloud/how-tos/enqueue_concurrent.md
+            - Webhooks:
+                - cloud/how-tos/webhooks.md
+            - Cron Jobs:
+                - cloud/how-tos/cron_jobs.md
+            - LangGraph Studio:
+                - LangGraph Studio: how-tos#langgraph-studio
+                - cloud/how-tos/test_deployment.md
+                - cloud/how-tos/test_local_deployment.md
+                - cloud/how-tos/invoke_studio.md
+                - cloud/how-tos/threads_studio.md
+                - cloud/how-tos/datasets_studio.md
+      - Concepts:
+        - concepts/index.md
+        - LangGraph:
+          - LangGraph: concepts#langgraph
+          - concepts/high_level.md
+          - concepts/low_level.md
+          - concepts/agentic_concepts.md
+          - concepts/multi_agent.md
+          - concepts/breakpoints
+          - concepts/human_in_the_loop.md
+          - concepts/time-travel.md
+          - concepts/persistence.md
+          - concepts/memory.md
+          - concepts/streaming.md
+        - LangGraph Platform:
+          - LangGraph Platform: concepts#langgraph-platform
+          - High Level:
+            - High Level: concepts#high-level
+            - concepts/langgraph_platform.md
+            - concepts/deployment_options.md
+            - concepts/plans.md
+            - concepts/template_applications.md
+          - Components:
+            - Components: concepts#components
+            - concepts/langgraph_server.md
+            - concepts/langgraph_studio.md
+            - concepts/langgraph_cli.md
+            - concepts/sdk.md
+            - how-tos/use-remote-graph.md
+          - LangGraph Server:
+            - LangGraph Server: concepts#langgraph-server
+            - concepts/application_structure.md
+            - concepts/assistants.md
+            - concepts/double_texting.md
+            - concepts/auth.md
+          - Deployment Options:
+            - Deployment Options: concepts#deployment-options
+            - concepts/self_hosted.md
+            - concepts/langgraph_cloud.md
+            - concepts/bring_your_own_cloud.md
+      - Tutorials:
+        - tutorials/index.md
+        - Quick Start:
+          - Quick Start: tutorials#quick-start
+          - tutorials/introduction.ipynb
+          - tutorials/langgraph-platform/local-server.md
+          - cloud/quick_start.md
+        - Chatbots:
+            - Chatbots: tutorials#chatbots
+            - tutorials/customer-support/customer-support.ipynb
+            - tutorials/chatbots/information-gather-prompting.ipynb
+            - tutorials/code_assistant/langgraph_code_assistant.ipynb
+        - RAG:
+            - RAG: tutorials#rag
+            - tutorials/rag/langgraph_adaptive_rag.ipynb
+            - tutorials/rag/langgraph_adaptive_rag_local.ipynb
+            - tutorials/rag/langgraph_agentic_rag.ipynb
+            - tutorials/rag/langgraph_crag.ipynb
+            - tutorials/rag/langgraph_crag_local.ipynb
+            - tutorials/rag/langgraph_self_rag.ipynb
+            - tutorials/rag/langgraph_self_rag_local.ipynb
+            - tutorials/sql-agent.ipynb
+        - Agent Architectures:
+            - Agent Architectures: tutorials#agent-architectures
+            - Multi-Agent Systems:
+                - Multi-Agent Systems: tutorials#multi-agent-systems
+                - tutorials/multi_agent/multi-agent-collaboration.ipynb
+                - tutorials/multi_agent/agent_supervisor.ipynb
+                - tutorials/multi_agent/hierarchical_agent_teams.ipynb
+            - Planning Agents:
+                - Planning Agents: tutorials#planning-agents
+                - tutorials/plan-and-execute/plan-and-execute.ipynb
+                - tutorials/rewoo/rewoo.ipynb
+                - tutorials/llm-compiler/LLMCompiler.ipynb
+            - Reflection & Critique:
+                - Reflection & Critique: tutorials#reflection-critique
+                - tutorials/reflection/reflection.ipynb
+                - tutorials/reflexion/reflexion.ipynb
+                - tutorials/tot/tot.ipynb
+                - tutorials/lats/lats.ipynb
+                - tutorials/self-discover/self-discover.ipynb
+        - Evaluation & Analysis:
+            - Evaluation & Analysis: tutorials#evaluation
+            - tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb
+            - tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb
+        - Experimental:
+            - Experimental: tutorials#experimental
+            - tutorials/storm/storm.ipynb
+            - tutorials/tnt-llm/tnt-llm.ipynb
+            - tutorials/web-navigation/web_voyager.ipynb
+            - tutorials/usaco/usaco.ipynb
+            - tutorials/extraction/retries.ipynb
+        - LangGraph Platform:
+            - LangGraph Platform: concepts#langgraph-platform
+            - tutorials/auth/getting_started.md
+            - tutorials/auth/resource_auth.md
+            - tutorials/auth/add_auth_server.md
+    - Resources:
+      - FAQ: concepts/faq.md
       - Troubleshooting:
-          - Troubleshooting: how-tos#troubleshooting
+          - Troubleshooting: troubleshooting/errors/index.md
           - troubleshooting/errors/index.md
           - troubleshooting/errors/GRAPH_RECURSION_LIMIT.md
           - troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE.md
           - troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE.md
           - troubleshooting/errors/MULTIPLE_SUBGRAPHS.md
-
-  - Conceptual Guides:
-      - concepts/index.md
-      - LangGraph:
-        - LangGraph: concepts#langgraph
-        - concepts/high_level.md
-        - concepts/low_level.md
-        - concepts/agentic_concepts.md
-        - concepts/multi_agent.md
-        - concepts/human_in_the_loop.md
-        - concepts/persistence.md
-        - concepts/memory.md
-        - concepts/streaming.md
-        - concepts/faq.md
-      - LangGraph Platform:
-        - LangGraph Platform: concepts#langgraph-platform
-        - High Level:
-          - High Level: concepts#high-level
-          - concepts/langgraph_platform.md
-          - concepts/deployment_options.md
-          - concepts/plans.md
-          - concepts/template_applications.md
-        - Components:
-          - Components: concepts#components
-          - concepts/langgraph_server.md
-          - concepts/langgraph_studio.md
-          - concepts/langgraph_cli.md
-          - concepts/sdk.md
-          - how-tos/use-remote-graph.md
-        - LangGraph Server:
-          - LangGraph Server: concepts#langgraph-server
-          - concepts/application_structure.md
-          - concepts/assistants.md
-          - concepts/double_texting.md
-        - Deployment Options:
-          - Deployment Options: concepts#deployment-options
-          - concepts/self_hosted.md
-          - concepts/langgraph_cloud.md
-          - concepts/bring_your_own_cloud.md
-
-  - Reference:
-      - "reference/index.md"
-      - Library:
-        - Graphs: reference/graphs.md
-        - Checkpointing: reference/checkpoints.md
-        - Storage: reference/store.md
-        - Prebuilt Components: reference/prebuilt.md
-        - Channels: reference/channels.md
-        - Errors: reference/errors.md
-        - Types: reference/types.md
-        - Constants: reference/constants.md
-      - LangGraph Platform:
-        - Server API: "cloud/reference/api/api_ref.md"
-        - CLI: "cloud/reference/cli.md"
-        - SDK (Python): "cloud/reference/sdk/python_sdk_ref.md"
-        - SDK (JS/TS): "cloud/reference/sdk/js_ts_sdk_ref.md"
-        - RemoteGraph: reference/remote_graph.md
-        - Environment Variables: "cloud/reference/env_var.md"
+          - troubleshooting/errors/INVALID_CHAT_HISTORY.md
+      - LangGraph Academy Course: https://academy.langchain.com/courses/intro-to-langgraph
+  
+  - API reference:
+    - Library:
+      - Graphs: reference/graphs.md
+      - Checkpointing: reference/checkpoints.md
+      - Storage: reference/store.md
+      - Prebuilt components: reference/prebuilt.md
+      - Channels: reference/channels.md
+      - Errors: reference/errors.md
+      - Types: reference/types.md
+      - Constants: reference/constants.md
+      - Functional API: reference/func.md
+    - LangGraph Platform:
+      - Server API: "cloud/reference/api/api_ref.md"
+      - CLI: "cloud/reference/cli.md"
+      - SDK (Python): "cloud/reference/sdk/python_sdk_ref.md"
+      - SDK (JS/TS): "cloud/reference/sdk/js_ts_sdk_ref.md"
+      - RemoteGraph: reference/remote_graph.md
+      - Environment variables: "cloud/reference/env_var.md"
 
 markdown_extensions:
   - abbr

docs/overrides/main.html

@@ -34,17 +34,6 @@
       color: #1E88E5;
     }
 
-    .md-sidebar {
-      display: none;
-    }
-
-    /* Show sidebar on mobile */
-    @media screen and (max-width: 1220px) {
-      .md-sidebar--primary {
-        display: block;
-      }
-    }
-
     .md-typeset a:hover {
       color: #1565C0;
     }
@@ -169,6 +158,16 @@
       background-color: #CFC9FA;
       color: #000000;
     }
+
+    /* control the navbar depth */
+    [data-md-level="2"] .md-nav {
+      display: none;
+    }
+
+    /* disable the collapse/expand icon in the navar */
+    .md-nav__icon {
+      display: none;
+    }
   </style>
 {% endblock %}
 

libs/checkpoint-duckdb/Makefile

@@ -1,35 +0,0 @@
-.PHONY: test test_watch lint format
-
-######################
-# TESTING AND COVERAGE
-######################
-
-test:
-	poetry run pytest tests
-
-test_watch:
-	poetry run ptw .
-
-######################
-# LINTING AND FORMATTING
-######################
-
-# Define a variable for Python and notebook files.
-PYTHON_FILES=.
-MYPY_CACHE=.mypy_cache
-lint format: PYTHON_FILES=.
-lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --relative --diff-filter=d main . | grep -E '\.py$$|\.ipynb$$')
-lint_package: PYTHON_FILES=langgraph
-lint_tests: PYTHON_FILES=tests
-lint_tests: MYPY_CACHE=.mypy_cache_test
-
-lint lint_diff lint_package lint_tests:
-	poetry run ruff check .
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
-
-format format_diff:
-	poetry run ruff format $(PYTHON_FILES)
-	poetry run ruff check --select I --fix $(PYTHON_FILES)

libs/checkpoint-duckdb/README.md

@@ -1,95 +0,0 @@
-# LangGraph Checkpoint DuckDB
-
-Implementation of LangGraph CheckpointSaver that uses DuckDB.
-
-## Usage
-
-> [!IMPORTANT]
-> When using DuckDB checkpointers for the first time, make sure to call `.setup()` method on them to create required tables. See example below.
-
-```python
-from langgraph.checkpoint.duckdb import DuckDBSaver
-
-write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
-read_config = {"configurable": {"thread_id": "1"}}
-
-with DuckDBSaver.from_conn_string(":memory:") as checkpointer:
-    # call .setup() the first time you're using the checkpointer
-    checkpointer.setup()
-    checkpoint = {
-        "v": 1,
-        "ts": "2024-07-31T20:14:19.804150+00:00",
-        "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
-        "channel_values": {
-            "my_key": "meow",
-            "node": "node"
-        },
-        "channel_versions": {
-            "__start__": 2,
-            "my_key": 3,
-            "start:node": 3,
-            "node": 3
-        },
-        "versions_seen": {
-            "__input__": {},
-            "__start__": {
-            "__start__": 1
-            },
-            "node": {
-            "start:node": 2
-            }
-        },
-        "pending_sends": [],
-    }
-
-    # store checkpoint
-    checkpointer.put(write_config, checkpoint, {}, {})
-
-    # load checkpoint
-    checkpointer.get(read_config)
-
-    # list checkpoints
-    list(checkpointer.list(read_config))
-```
-
-### Async
-
-```python
-from langgraph.checkpoint.duckdb.aio import AsyncDuckDBSaver
-
-async with AsyncDuckDBSaver.from_conn_string(":memory:") as checkpointer:
-    checkpoint = {
-        "v": 1,
-        "ts": "2024-07-31T20:14:19.804150+00:00",
-        "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
-        "channel_values": {
-            "my_key": "meow",
-            "node": "node"
-        },
-        "channel_versions": {
-            "__start__": 2,
-            "my_key": 3,
-            "start:node": 3,
-            "node": 3
-        },
-        "versions_seen": {
-            "__input__": {},
-            "__start__": {
-            "__start__": 1
-            },
-            "node": {
-            "start:node": 2
-            }
-        },
-        "pending_sends": [],
-    }
-
-    # store checkpoint
-    await checkpointer.aput(write_config, checkpoint, {}, {})
-
-    # load checkpoint
-    await checkpointer.aget(read_config)
-
-    # list checkpoints
-    [c async for c in checkpointer.alist(read_config)]
-```

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

@@ -1,356 +0,0 @@
-import threading
-from contextlib import contextmanager
-from typing import Any, Iterator, Optional, Sequence
-
-from langchain_core.runnables import RunnableConfig
-
-import duckdb
-from langgraph.checkpoint.base import (
-    WRITES_IDX_MAP,
-    ChannelVersions,
-    Checkpoint,
-    CheckpointMetadata,
-    CheckpointTuple,
-    get_checkpoint_id,
-)
-from langgraph.checkpoint.duckdb.base import BaseDuckDBSaver
-from langgraph.checkpoint.serde.base import SerializerProtocol
-
-
-class DuckDBSaver(BaseDuckDBSaver):
-    lock: threading.Lock
-
-    def __init__(
-        self,
-        conn: duckdb.DuckDBPyConnection,
-        serde: Optional[SerializerProtocol] = None,
-    ) -> None:
-        super().__init__(serde=serde)
-
-        self.conn = conn
-        self.lock = threading.Lock()
-
-    @classmethod
-    @contextmanager
-    def from_conn_string(cls, conn_string: str) -> Iterator["DuckDBSaver"]:
-        """Create a new DuckDBSaver instance from a connection string.
-
-        Args:
-            conn_string (str): The DuckDB connection info string.
-
-        Returns:
-            DuckDBSaver: A new DuckDBSaver instance.
-        """
-        with duckdb.connect(conn_string) as conn:
-            yield cls(conn)
-
-    def setup(self) -> None:
-        """Set up the checkpoint database asynchronously.
-
-        This method creates the necessary tables in the DuckDB database if they don't
-        already exist and runs database migrations. It MUST be called directly by the user
-        the first time checkpointer is used.
-        """
-        with self.lock, self.conn.cursor() as cur:
-            try:
-                row = cur.execute(
-                    "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
-                ).fetchone()
-                if row is None:
-                    version = -1
-                else:
-                    version = row[0]
-            except duckdb.CatalogException:
-                version = -1
-            for v, migration in zip(
-                range(version + 1, len(self.MIGRATIONS)),
-                self.MIGRATIONS[version + 1 :],
-            ):
-                cur.execute(migration)
-                cur.execute("INSERT INTO checkpoint_migrations (v) VALUES (?)", [v])
-
-    def list(
-        self,
-        config: Optional[RunnableConfig],
-        *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
-    ) -> Iterator[CheckpointTuple]:
-        """List checkpoints from the database.
-
-        This method retrieves a list of checkpoint tuples from the DuckDB database based
-        on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).
-
-        Args:
-            config (RunnableConfig): The config to use for listing the checkpoints.
-            filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata. Defaults to None.
-            before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.
-            limit (Optional[int]): The maximum number of checkpoints to return. Defaults to None.
-
-        Yields:
-            Iterator[CheckpointTuple]: An iterator of checkpoint tuples.
-
-        Examples:
-            >>> from langgraph.checkpoint.duckdb import DuckDBSaver
-            >>> with DuckDBSaver.from_conn_string(":memory:") as memory:
-            ... # Run a graph, then list the checkpoints
-            >>>     config = {"configurable": {"thread_id": "1"}}
-            >>>     checkpoints = list(memory.list(config, limit=2))
-            >>> print(checkpoints)
-            [CheckpointTuple(...), CheckpointTuple(...)]
-
-            >>> config = {"configurable": {"thread_id": "1"}}
-            >>> before = {"configurable": {"checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875"}}
-            >>> with DuckDBSaver.from_conn_string(":memory:") as memory:
-            ... # Run a graph, then list the checkpoints
-            >>>     checkpoints = list(memory.list(config, before=before))
-            >>> print(checkpoints)
-            [CheckpointTuple(...), ...]
-        """
-        where, args = self._search_where(config, filter, before)
-        query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
-        if limit:
-            query += f" LIMIT {limit}"
-        # if we change this to use .stream() we need to make sure to close the cursor
-        with self._cursor() as cur:
-            cur.execute(query, args)
-            for value in cur.fetchall():
-                (
-                    thread_id,
-                    checkpoint,
-                    checkpoint_ns,
-                    checkpoint_id,
-                    parent_checkpoint_id,
-                    metadata,
-                    channel_values,
-                    pending_writes,
-                    pending_sends,
-                ) = value
-                yield CheckpointTuple(
-                    {
-                        "configurable": {
-                            "thread_id": thread_id,
-                            "checkpoint_ns": checkpoint_ns,
-                            "checkpoint_id": checkpoint_id,
-                        }
-                    },
-                    self._load_checkpoint(
-                        checkpoint,
-                        channel_values,
-                        pending_sends,
-                    ),
-                    self._load_metadata(metadata),
-                    (
-                        {
-                            "configurable": {
-                                "thread_id": thread_id,
-                                "checkpoint_ns": checkpoint_ns,
-                                "checkpoint_id": parent_checkpoint_id,
-                            }
-                        }
-                        if parent_checkpoint_id
-                        else None
-                    ),
-                    self._load_writes(pending_writes),
-                )
-
-    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
-        """Get a checkpoint tuple from the database.
-
-        This method retrieves a checkpoint tuple from the DuckDB database based on the
-        provided config. If the config contains a "checkpoint_id" key, the checkpoint with
-        the matching thread ID and timestamp is retrieved. Otherwise, the latest checkpoint
-        for the given thread ID is retrieved.
-
-        Args:
-            config (RunnableConfig): The config to use for retrieving the checkpoint.
-
-        Returns:
-            Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
-
-        Examples:
-
-            Basic:
-            >>> config = {"configurable": {"thread_id": "1"}}
-            >>> checkpoint_tuple = memory.get_tuple(config)
-            >>> print(checkpoint_tuple)
-            CheckpointTuple(...)
-
-            With timestamp:
-
-            >>> config = {
-            ...    "configurable": {
-            ...        "thread_id": "1",
-            ...        "checkpoint_ns": "",
-            ...        "checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875",
-            ...    }
-            ... }
-            >>> checkpoint_tuple = memory.get_tuple(config)
-            >>> print(checkpoint_tuple)
-            CheckpointTuple(...)
-        """  # noqa
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_id = get_checkpoint_id(config)
-        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-        if checkpoint_id:
-            args: tuple[Any, ...] = (thread_id, checkpoint_ns, checkpoint_id)
-            where = "WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ?"
-        else:
-            args = (thread_id, checkpoint_ns)
-            where = "WHERE thread_id = ? AND checkpoint_ns = ? ORDER BY checkpoint_id DESC LIMIT 1"
-
-        with self._cursor() as cur:
-            cur.execute(
-                self.SELECT_SQL + where,
-                args,
-            )
-
-            value = cur.fetchone()
-            if value:
-                (
-                    thread_id,
-                    checkpoint,
-                    checkpoint_ns,
-                    checkpoint_id,
-                    parent_checkpoint_id,
-                    metadata,
-                    channel_values,
-                    pending_writes,
-                    pending_sends,
-                ) = value
-                return CheckpointTuple(
-                    {
-                        "configurable": {
-                            "thread_id": thread_id,
-                            "checkpoint_ns": checkpoint_ns,
-                            "checkpoint_id": checkpoint_id,
-                        }
-                    },
-                    self._load_checkpoint(
-                        checkpoint,
-                        channel_values,
-                        pending_sends,
-                    ),
-                    self._load_metadata(metadata),
-                    (
-                        {
-                            "configurable": {
-                                "thread_id": thread_id,
-                                "checkpoint_ns": checkpoint_ns,
-                                "checkpoint_id": parent_checkpoint_id,
-                            }
-                        }
-                        if parent_checkpoint_id
-                        else None
-                    ),
-                    self._load_writes(pending_writes),
-                )
-
-    def put(
-        self,
-        config: RunnableConfig,
-        checkpoint: Checkpoint,
-        metadata: CheckpointMetadata,
-        new_versions: ChannelVersions,
-    ) -> RunnableConfig:
-        """Save a checkpoint to the database.
-
-        This method saves a checkpoint to the DuckDB database. The checkpoint is associated
-        with the provided config and its parent config (if any).
-
-        Args:
-            config (RunnableConfig): The config to associate with the checkpoint.
-            checkpoint (Checkpoint): The checkpoint to save.
-            metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
-            new_versions (ChannelVersions): New channel versions as of this write.
-
-        Returns:
-            RunnableConfig: Updated configuration after storing the checkpoint.
-
-        Examples:
-
-            >>> from langgraph.checkpoint.duckdb import DuckDBSaver
-            >>> with DuckDBSaver.from_conn_string(":memory:") as memory:
-            >>>     config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
-            >>>     checkpoint = {"ts": "2024-05-04T06:32:42.235444+00:00", "id": "1ef4f797-8335-6428-8001-8a1503f9b875", "channel_values": {"key": "value"}}
-            >>>     saved_config = memory.put(config, checkpoint, {"source": "input", "step": 1, "writes": {"key": "value"}}, {})
-            >>> print(saved_config)
-            {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef4f797-8335-6428-8001-8a1503f9b875'}}
-        """
-        configurable = config["configurable"].copy()
-        thread_id = configurable.pop("thread_id")
-        checkpoint_ns = configurable.pop("checkpoint_ns")
-        checkpoint_id = configurable.pop(
-            "checkpoint_id", configurable.pop("thread_ts", None)
-        )
-
-        copy = checkpoint.copy()
-        next_config = {
-            "configurable": {
-                "thread_id": thread_id,
-                "checkpoint_ns": checkpoint_ns,
-                "checkpoint_id": checkpoint["id"],
-            }
-        }
-        checkpoint_blobs = self._dump_blobs(
-            thread_id,
-            checkpoint_ns,
-            copy.pop("channel_values"),  # type: ignore[misc]
-            new_versions,
-        )
-        with self._cursor() as cur:
-            if checkpoint_blobs:
-                cur.executemany(self.UPSERT_CHECKPOINT_BLOBS_SQL, checkpoint_blobs)
-            cur.execute(
-                self.UPSERT_CHECKPOINTS_SQL,
-                (
-                    thread_id,
-                    checkpoint_ns,
-                    checkpoint["id"],
-                    checkpoint_id,
-                    self._dump_checkpoint(copy),
-                    self._dump_metadata(metadata),
-                ),
-            )
-        return next_config
-
-    def put_writes(
-        self,
-        config: RunnableConfig,
-        writes: Sequence[tuple[str, Any]],
-        task_id: str,
-    ) -> None:
-        """Store intermediate writes linked to a checkpoint.
-
-        This method saves intermediate writes associated with a checkpoint to the DuckDB database.
-
-        Args:
-            config (RunnableConfig): Configuration of the related checkpoint.
-            writes (List[Tuple[str, Any]]): List of writes to store.
-            task_id (str): Identifier for the task creating the writes.
-        """
-        query = (
-            self.UPSERT_CHECKPOINT_WRITES_SQL
-            if all(w[0] in WRITES_IDX_MAP for w in writes)
-            else self.INSERT_CHECKPOINT_WRITES_SQL
-        )
-        with self._cursor() as cur:
-            cur.executemany(
-                query,
-                self._dump_writes(
-                    config["configurable"]["thread_id"],
-                    config["configurable"]["checkpoint_ns"],
-                    config["configurable"]["checkpoint_id"],
-                    task_id,
-                    writes,
-                ),
-            )
-
-    @contextmanager
-    def _cursor(self) -> Iterator[duckdb.DuckDBPyConnection]:
-        with self.lock, self.conn.cursor() as cur:
-            yield cur
-
-
-__all__ = ["DuckDBSaver", "Conn"]

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

@@ -1,443 +0,0 @@
-import asyncio
-from contextlib import asynccontextmanager
-from typing import Any, AsyncIterator, Iterator, Optional, Sequence
-
-from langchain_core.runnables import RunnableConfig
-
-import duckdb
-from langgraph.checkpoint.base import (
-    WRITES_IDX_MAP,
-    ChannelVersions,
-    Checkpoint,
-    CheckpointMetadata,
-    CheckpointTuple,
-    get_checkpoint_id,
-)
-from langgraph.checkpoint.duckdb.base import BaseDuckDBSaver
-from langgraph.checkpoint.serde.base import SerializerProtocol
-
-
-class AsyncDuckDBSaver(BaseDuckDBSaver):
-    lock: asyncio.Lock
-
-    def __init__(
-        self,
-        conn: duckdb.DuckDBPyConnection,
-        serde: Optional[SerializerProtocol] = None,
-    ) -> None:
-        super().__init__(serde=serde)
-        self.conn = conn
-        self.lock = asyncio.Lock()
-        self.loop = asyncio.get_running_loop()
-
-    @classmethod
-    @asynccontextmanager
-    async def from_conn_string(
-        cls,
-        conn_string: str,
-    ) -> AsyncIterator["AsyncDuckDBSaver"]:
-        """Create a new AsyncDuckDBSaver instance from a connection string.
-
-        Args:
-            conn_string (str): The DuckDB connection info string.
-
-        Returns:
-            AsyncDuckDBSaver: A new AsyncDuckDBSaver instance.
-        """
-        with duckdb.connect(conn_string) as conn:
-            yield cls(conn)
-
-    async def setup(self) -> None:
-        """Set up the checkpoint database asynchronously.
-
-        This method creates the necessary tables in the DuckDB database if they don't
-        already exist and runs database migrations. It MUST be called directly by the user
-        the first time checkpointer is used.
-        """
-        async with self.lock:
-            with self.conn.cursor() as cur:
-                try:
-                    await asyncio.to_thread(
-                        cur.execute,
-                        "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1",
-                    )
-                    row = await asyncio.to_thread(cur.fetchone)
-                    if row is None:
-                        version = -1
-                    else:
-                        version = row[0]
-                except duckdb.CatalogException:
-                    version = -1
-                for v, migration in zip(
-                    range(version + 1, len(self.MIGRATIONS)),
-                    self.MIGRATIONS[version + 1 :],
-                ):
-                    await asyncio.to_thread(cur.execute, migration)
-                    await asyncio.to_thread(
-                        cur.execute,
-                        "INSERT INTO checkpoint_migrations (v) VALUES (?)",
-                        [v],
-                    )
-
-    async def alist(
-        self,
-        config: Optional[RunnableConfig],
-        *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
-    ) -> AsyncIterator[CheckpointTuple]:
-        """List checkpoints from the database asynchronously.
-
-        This method retrieves a list of checkpoint tuples from the DuckDB database based
-        on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).
-
-        Args:
-            config (Optional[RunnableConfig]): Base configuration for filtering checkpoints.
-            filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata.
-            before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.
-            limit (Optional[int]): Maximum number of checkpoints to return.
-
-        Yields:
-            AsyncIterator[CheckpointTuple]: An asynchronous iterator of matching checkpoint tuples.
-        """
-        where, args = self._search_where(config, filter, before)
-        query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
-        if limit:
-            query += f" LIMIT {limit}"
-        # if we change this to use .stream() we need to make sure to close the cursor
-        async with self._cursor() as cur:
-            await asyncio.to_thread(cur.execute, query, args)
-            results = await asyncio.to_thread(cur.fetchall)
-            for value in results:
-                (
-                    thread_id,
-                    checkpoint,
-                    checkpoint_ns,
-                    checkpoint_id,
-                    parent_checkpoint_id,
-                    metadata,
-                    channel_values,
-                    pending_writes,
-                    pending_sends,
-                ) = value
-                yield CheckpointTuple(
-                    {
-                        "configurable": {
-                            "thread_id": thread_id,
-                            "checkpoint_ns": checkpoint_ns,
-                            "checkpoint_id": checkpoint_id,
-                        }
-                    },
-                    await asyncio.to_thread(
-                        self._load_checkpoint,
-                        checkpoint,
-                        channel_values,
-                        pending_sends,
-                    ),
-                    self._load_metadata(metadata),
-                    (
-                        {
-                            "configurable": {
-                                "thread_id": thread_id,
-                                "checkpoint_ns": checkpoint_ns,
-                                "checkpoint_id": parent_checkpoint_id,
-                            }
-                        }
-                        if parent_checkpoint_id
-                        else None
-                    ),
-                    await asyncio.to_thread(self._load_writes, pending_writes),
-                )
-
-    async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
-        """Get a checkpoint tuple from the database asynchronously.
-
-        This method retrieves a checkpoint tuple from the DuckDBdatabase based on the
-        provided config. If the config contains a "checkpoint_id" key, the checkpoint with
-        the matching thread ID and "checkpoint_id" is retrieved. Otherwise, the latest checkpoint
-        for the given thread ID is retrieved.
-
-        Args:
-            config (RunnableConfig): The config to use for retrieving the checkpoint.
-
-        Returns:
-            Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
-        """
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_id = get_checkpoint_id(config)
-        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-        if checkpoint_id:
-            args: tuple[Any, ...] = (thread_id, checkpoint_ns, checkpoint_id)
-            where = "WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ?"
-        else:
-            args = (thread_id, checkpoint_ns)
-            where = "WHERE thread_id = ? AND checkpoint_ns = ? ORDER BY checkpoint_id DESC LIMIT 1"
-
-        async with self._cursor() as cur:
-            await asyncio.to_thread(
-                cur.execute,
-                self.SELECT_SQL + where,
-                args,
-            )
-
-            value = await asyncio.to_thread(cur.fetchone)
-            if value:
-                (
-                    thread_id,
-                    checkpoint,
-                    checkpoint_ns,
-                    checkpoint_id,
-                    parent_checkpoint_id,
-                    metadata,
-                    channel_values,
-                    pending_writes,
-                    pending_sends,
-                ) = value
-                return CheckpointTuple(
-                    {
-                        "configurable": {
-                            "thread_id": thread_id,
-                            "checkpoint_ns": checkpoint_ns,
-                            "checkpoint_id": checkpoint_id,
-                        }
-                    },
-                    await asyncio.to_thread(
-                        self._load_checkpoint,
-                        checkpoint,
-                        channel_values,
-                        pending_sends,
-                    ),
-                    self._load_metadata(metadata),
-                    (
-                        {
-                            "configurable": {
-                                "thread_id": thread_id,
-                                "checkpoint_ns": checkpoint_ns,
-                                "checkpoint_id": parent_checkpoint_id,
-                            }
-                        }
-                        if parent_checkpoint_id
-                        else None
-                    ),
-                    await asyncio.to_thread(self._load_writes, pending_writes),
-                )
-
-    async def aput(
-        self,
-        config: RunnableConfig,
-        checkpoint: Checkpoint,
-        metadata: CheckpointMetadata,
-        new_versions: ChannelVersions,
-    ) -> RunnableConfig:
-        """Save a checkpoint to the database asynchronously.
-
-        This method saves a checkpoint to the DuckDB database. The checkpoint is associated
-        with the provided config and its parent config (if any).
-
-        Args:
-            config (RunnableConfig): The config to associate with the checkpoint.
-            checkpoint (Checkpoint): The checkpoint to save.
-            metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
-            new_versions (ChannelVersions): New channel versions as of this write.
-
-        Returns:
-            RunnableConfig: Updated configuration after storing the checkpoint.
-        """
-        configurable = config["configurable"].copy()
-        thread_id = configurable.pop("thread_id")
-        checkpoint_ns = configurable.pop("checkpoint_ns")
-        checkpoint_id = configurable.pop(
-            "checkpoint_id", configurable.pop("thread_ts", None)
-        )
-
-        copy = checkpoint.copy()
-        next_config = {
-            "configurable": {
-                "thread_id": thread_id,
-                "checkpoint_ns": checkpoint_ns,
-                "checkpoint_id": checkpoint["id"],
-            }
-        }
-
-        checkpoint_blobs = await asyncio.to_thread(
-            self._dump_blobs,
-            thread_id,
-            checkpoint_ns,
-            copy.pop("channel_values"),  # type: ignore[misc]
-            new_versions,
-        )
-        async with self._cursor() as cur:
-            if checkpoint_blobs:
-                await asyncio.to_thread(
-                    cur.executemany, self.UPSERT_CHECKPOINT_BLOBS_SQL, checkpoint_blobs
-                )
-            await asyncio.to_thread(
-                cur.execute,
-                self.UPSERT_CHECKPOINTS_SQL,
-                (
-                    thread_id,
-                    checkpoint_ns,
-                    checkpoint["id"],
-                    checkpoint_id,
-                    self._dump_checkpoint(copy),
-                    self._dump_metadata(metadata),
-                ),
-            )
-
-        return next_config
-
-    async def aput_writes(
-        self,
-        config: RunnableConfig,
-        writes: Sequence[tuple[str, Any]],
-        task_id: str,
-    ) -> None:
-        """Store intermediate writes linked to a checkpoint asynchronously.
-
-        This method saves intermediate writes associated with a checkpoint to the database.
-
-        Args:
-            config (RunnableConfig): Configuration of the related checkpoint.
-            writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.
-            task_id (str): Identifier for the task creating the writes.
-        """
-        query = (
-            self.UPSERT_CHECKPOINT_WRITES_SQL
-            if all(w[0] in WRITES_IDX_MAP for w in writes)
-            else self.INSERT_CHECKPOINT_WRITES_SQL
-        )
-        params = await asyncio.to_thread(
-            self._dump_writes,
-            config["configurable"]["thread_id"],
-            config["configurable"]["checkpoint_ns"],
-            config["configurable"]["checkpoint_id"],
-            task_id,
-            writes,
-        )
-        async with self._cursor() as cur:
-            await asyncio.to_thread(cur.executemany, query, params)
-
-    @asynccontextmanager
-    async def _cursor(self) -> AsyncIterator[duckdb.DuckDBPyConnection]:
-        async with self.lock:
-            with self.conn.cursor() as cur:
-                yield cur
-
-    def list(
-        self,
-        config: Optional[RunnableConfig],
-        *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
-    ) -> Iterator[CheckpointTuple]:
-        """List checkpoints from the database.
-
-        This method retrieves a list of checkpoint tuples from the DuckDB database based
-        on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).
-
-        Args:
-            config (Optional[RunnableConfig]): Base configuration for filtering checkpoints.
-            filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata.
-            before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.
-            limit (Optional[int]): Maximum number of checkpoints to return.
-
-        Yields:
-            Iterator[CheckpointTuple]: An iterator of matching checkpoint tuples.
-        """
-        try:
-            # check if we are in the main thread, only bg threads can block
-            # we don't check in other methods to avoid the overhead
-            if asyncio.get_running_loop() is self.loop:
-                raise asyncio.InvalidStateError(
-                    "Synchronous calls to AsyncDuckDBSaver are only allowed from a "
-                    "different thread. From the main thread, use the async interface. "
-                    "For example, use `checkpointer.alist(...)` or `await "
-                    "graph.ainvoke(...)`."
-                )
-        except RuntimeError:
-            pass
-        aiter_ = self.alist(config, filter=filter, before=before, limit=limit)
-        while True:
-            try:
-                yield asyncio.run_coroutine_threadsafe(
-                    anext(aiter_),
-                    self.loop,
-                ).result()
-            except StopAsyncIteration:
-                break
-
-    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
-        """Get a checkpoint tuple from the database.
-
-        This method retrieves a checkpoint tuple from the DuckDB database based on the
-        provided config. If the config contains a "checkpoint_id" key, the checkpoint with
-        the matching thread ID and "checkpoint_id" is retrieved. Otherwise, the latest checkpoint
-        for the given thread ID is retrieved.
-
-        Args:
-            config (RunnableConfig): The config to use for retrieving the checkpoint.
-
-        Returns:
-            Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
-        """
-        try:
-            # check if we are in the main thread, only bg threads can block
-            # we don't check in other methods to avoid the overhead
-            if asyncio.get_running_loop() is self.loop:
-                raise asyncio.InvalidStateError(
-                    "Synchronous calls to AsyncDuckDBSaver are only allowed from a "
-                    "different thread. From the main thread, use the async interface."
-                    "For example, use `await checkpointer.aget_tuple(...)` or `await "
-                    "graph.ainvoke(...)`."
-                )
-        except RuntimeError:
-            pass
-        return asyncio.run_coroutine_threadsafe(
-            self.aget_tuple(config), self.loop
-        ).result()
-
-    def put(
-        self,
-        config: RunnableConfig,
-        checkpoint: Checkpoint,
-        metadata: CheckpointMetadata,
-        new_versions: ChannelVersions,
-    ) -> RunnableConfig:
-        """Save a checkpoint to the database.
-
-        This method saves a checkpoint to the DuckDB database. The checkpoint is associated
-        with the provided config and its parent config (if any).
-
-        Args:
-            config (RunnableConfig): The config to associate with the checkpoint.
-            checkpoint (Checkpoint): The checkpoint to save.
-            metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
-            new_versions (ChannelVersions): New channel versions as of this write.
-
-        Returns:
-            RunnableConfig: Updated configuration after storing the checkpoint.
-        """
-        return asyncio.run_coroutine_threadsafe(
-            self.aput(config, checkpoint, metadata, new_versions), self.loop
-        ).result()
-
-    def put_writes(
-        self,
-        config: RunnableConfig,
-        writes: Sequence[tuple[str, Any]],
-        task_id: str,
-    ) -> None:
-        """Store intermediate writes linked to a checkpoint.
-
-        This method saves intermediate writes associated with a checkpoint to the database.
-
-        Args:
-            config (RunnableConfig): Configuration of the related checkpoint.
-            writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.
-            task_id (str): Identifier for the task creating the writes.
-        """
-        return asyncio.run_coroutine_threadsafe(
-            self.aput_writes(config, writes, task_id), self.loop
-        ).result()

libs/checkpoint-duckdb/langgraph/checkpoint/duckdb/base.py

@@ -1,290 +0,0 @@
-import json
-import random
-from typing import Any, List, Optional, Sequence, Tuple, cast
-
-from langchain_core.runnables import RunnableConfig
-
-from langgraph.checkpoint.base import (
-    WRITES_IDX_MAP,
-    BaseCheckpointSaver,
-    ChannelVersions,
-    Checkpoint,
-    CheckpointMetadata,
-    get_checkpoint_id,
-)
-from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
-from langgraph.checkpoint.serde.types import TASKS, ChannelProtocol
-
-MetadataInput = Optional[dict[str, Any]]
-
-"""
-To add a new migration, add a new string to the MIGRATIONS list.
-The position of the migration in the list is the version number.
-"""
-MIGRATIONS = [
-    """CREATE TABLE IF NOT EXISTS checkpoint_migrations (
-    v INTEGER PRIMARY KEY
-);""",
-    """CREATE TABLE IF NOT EXISTS checkpoints (
-    thread_id TEXT NOT NULL,
-    checkpoint_ns TEXT NOT NULL DEFAULT '',
-    checkpoint_id TEXT NOT NULL,
-    parent_checkpoint_id TEXT,
-    type TEXT,
-    checkpoint JSON NOT NULL,
-    metadata JSON NOT NULL DEFAULT '{}',
-    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id)
-);""",
-    """CREATE TABLE IF NOT EXISTS checkpoint_blobs (
-    thread_id TEXT NOT NULL,
-    checkpoint_ns TEXT NOT NULL DEFAULT '',
-    channel TEXT NOT NULL,
-    version TEXT NOT NULL,
-    type TEXT NOT NULL,
-    blob BLOB,
-    PRIMARY KEY (thread_id, checkpoint_ns, channel, version)
-);""",
-    """CREATE TABLE IF NOT EXISTS checkpoint_writes (
-    thread_id TEXT NOT NULL,
-    checkpoint_ns TEXT NOT NULL DEFAULT '',
-    checkpoint_id TEXT NOT NULL,
-    task_id TEXT NOT NULL,
-    idx INTEGER NOT NULL,
-    channel TEXT NOT NULL,
-    type TEXT,
-    blob BLOB NOT NULL,
-    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx)
-);""",
-]
-
-SELECT_SQL = f"""
-select
-    thread_id,
-    checkpoint,
-    checkpoint_ns,
-    checkpoint_id,
-    parent_checkpoint_id,
-    metadata,
-    (
-        select array_agg(array[bl.channel::bytea, bl.type::bytea, bl.blob])
-        from (
-            SELECT unnest(json_keys(json_extract(checkpoint, '$.channel_versions'))) as key
-        ) cv
-        inner join checkpoint_blobs bl
-            on bl.thread_id = checkpoints.thread_id
-            and bl.checkpoint_ns = checkpoints.checkpoint_ns
-            and bl.channel = cv.key
-            and bl.version = json_extract_string(checkpoint, '$.channel_versions.' || cv.key)
-    ) as channel_values,
-    (
-        select
-        array_agg(array[cw.task_id::blob, cw.channel::blob, cw.type::blob, cw.blob])
-        from checkpoint_writes cw
-        where cw.thread_id = checkpoints.thread_id
-            and cw.checkpoint_ns = checkpoints.checkpoint_ns
-            and cw.checkpoint_id = checkpoints.checkpoint_id
-    ) as pending_writes,
-    (
-        select array_agg(array[cw.type::blob, cw.blob])
-        from checkpoint_writes cw
-        where cw.thread_id = checkpoints.thread_id
-            and cw.checkpoint_ns = checkpoints.checkpoint_ns
-            and cw.checkpoint_id = checkpoints.parent_checkpoint_id
-            and cw.channel = '{TASKS}'
-    ) as pending_sends
-from checkpoints """
-
-UPSERT_CHECKPOINT_BLOBS_SQL = """
-    INSERT INTO checkpoint_blobs (thread_id, checkpoint_ns, channel, version, type, blob)
-    VALUES (?, ?, ?, ?, ?, ?)
-    ON CONFLICT (thread_id, checkpoint_ns, channel, version) DO NOTHING
-"""
-
-UPSERT_CHECKPOINTS_SQL = """
-    INSERT INTO checkpoints (thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, checkpoint, metadata)
-    VALUES (?, ?, ?, ?, ?, ?)
-    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id)
-    DO UPDATE SET
-        checkpoint = EXCLUDED.checkpoint,
-        metadata = EXCLUDED.metadata;
-"""
-
-UPSERT_CHECKPOINT_WRITES_SQL = """
-    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, blob)
-    VALUES (?, ?, ?, ?, ?, ?, ?, ?)
-    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO UPDATE SET
-        channel = EXCLUDED.channel,
-        type = EXCLUDED.type,
-        blob = EXCLUDED.blob;
-"""
-
-INSERT_CHECKPOINT_WRITES_SQL = """
-    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, blob)
-    VALUES (?, ?, ?, ?, ?, ?, ?, ?)
-    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING
-"""
-
-
-class BaseDuckDBSaver(BaseCheckpointSaver[str]):
-    SELECT_SQL = SELECT_SQL
-    MIGRATIONS = MIGRATIONS
-    UPSERT_CHECKPOINT_BLOBS_SQL = UPSERT_CHECKPOINT_BLOBS_SQL
-    UPSERT_CHECKPOINTS_SQL = UPSERT_CHECKPOINTS_SQL
-    UPSERT_CHECKPOINT_WRITES_SQL = UPSERT_CHECKPOINT_WRITES_SQL
-    INSERT_CHECKPOINT_WRITES_SQL = INSERT_CHECKPOINT_WRITES_SQL
-
-    jsonplus_serde = JsonPlusSerializer()
-
-    def _load_checkpoint(
-        self,
-        checkpoint_json_str: str,
-        channel_values: list[tuple[bytes, bytes, bytes]],
-        pending_sends: list[tuple[bytes, bytes]],
-    ) -> Checkpoint:
-        checkpoint = json.loads(checkpoint_json_str)
-        return {
-            **checkpoint,
-            "pending_sends": [
-                self.serde.loads_typed((c.decode(), b)) for c, b in pending_sends or []
-            ],
-            "channel_values": self._load_blobs(channel_values),
-        }
-
-    def _dump_checkpoint(self, checkpoint: Checkpoint) -> dict[str, Any]:
-        return {**checkpoint, "pending_sends": []}
-
-    def _load_blobs(
-        self, blob_values: list[tuple[bytes, bytes, bytes]]
-    ) -> dict[str, Any]:
-        if not blob_values:
-            return {}
-        return {
-            k.decode(): self.serde.loads_typed((t.decode(), v))
-            for k, t, v in blob_values
-            if t.decode() != "empty"
-        }
-
-    def _dump_blobs(
-        self,
-        thread_id: str,
-        checkpoint_ns: str,
-        values: dict[str, Any],
-        versions: ChannelVersions,
-    ) -> list[tuple[str, str, str, str, str, Optional[bytes]]]:
-        if not versions:
-            return []
-
-        return [
-            (
-                thread_id,
-                checkpoint_ns,
-                k,
-                cast(str, ver),
-                *(
-                    self.serde.dumps_typed(values[k])
-                    if k in values
-                    else ("empty", None)
-                ),
-            )
-            for k, ver in versions.items()
-        ]
-
-    def _load_writes(
-        self, writes: list[tuple[bytes, bytes, bytes, bytes]]
-    ) -> list[tuple[str, str, Any]]:
-        return (
-            [
-                (
-                    tid.decode(),
-                    channel.decode(),
-                    self.serde.loads_typed((t.decode(), v)),
-                )
-                for tid, channel, t, v in writes
-            ]
-            if writes
-            else []
-        )
-
-    def _dump_writes(
-        self,
-        thread_id: str,
-        checkpoint_ns: str,
-        checkpoint_id: str,
-        task_id: str,
-        writes: Sequence[tuple[str, Any]],
-    ) -> list[tuple[str, str, str, str, int, str, str, bytes]]:
-        return [
-            (
-                thread_id,
-                checkpoint_ns,
-                checkpoint_id,
-                task_id,
-                WRITES_IDX_MAP.get(channel, idx),
-                channel,
-                *self.serde.dumps_typed(value),
-            )
-            for idx, (channel, value) in enumerate(writes)
-        ]
-
-    def _load_metadata(self, metadata_json_str: str) -> CheckpointMetadata:
-        return self.jsonplus_serde.loads(metadata_json_str.encode())
-
-    def _dump_metadata(self, metadata: CheckpointMetadata) -> str:
-        serialized_metadata = self.jsonplus_serde.dumps(metadata)
-        # NOTE: we're using JSON serializer (not msgpack), so we need to remove null characters before writing
-        return serialized_metadata.decode().replace("\\u0000", "")
-
-    def get_next_version(self, current: Optional[str], channel: ChannelProtocol) -> str:
-        if current is None:
-            current_v = 0
-        elif isinstance(current, int):
-            current_v = current
-        else:
-            current_v = int(current.split(".")[0])
-        next_v = current_v + 1
-        next_h = random.random()
-        return f"{next_v:032}.{next_h:016}"
-
-    def _search_where(
-        self,
-        config: Optional[RunnableConfig],
-        filter: MetadataInput,
-        before: Optional[RunnableConfig] = None,
-    ) -> Tuple[str, List[Any]]:
-        """Return WHERE clause predicates for alist() given config, filter, before.
-
-        This method returns a tuple of a string and a tuple of values. The string
-        is the parametered WHERE clause predicate (including the WHERE keyword):
-        "WHERE column1 = $1 AND column2 IS $2". The list of values contains the
-        values for each of the corresponding parameters.
-        """
-        wheres = []
-        param_values = []
-
-        # construct predicate for config filter
-        if config:
-            wheres.append("thread_id = ?")
-            param_values.append(config["configurable"]["thread_id"])
-            checkpoint_ns = config["configurable"].get("checkpoint_ns")
-            if checkpoint_ns is not None:
-                wheres.append("checkpoint_ns = ?")
-                param_values.append(checkpoint_ns)
-
-            if checkpoint_id := get_checkpoint_id(config):
-                wheres.append("checkpoint_id = ?")
-                param_values.append(checkpoint_id)
-
-        # construct predicate for metadata filter
-        if filter:
-            wheres.append("json_contains(metadata, ?)")
-            param_values.append(json.dumps(filter))
-
-        # construct predicate for `before`
-        if before is not None:
-            wheres.append("checkpoint_id < ?")
-            param_values.append(get_checkpoint_id(before))
-
-        return (
-            "WHERE " + " AND ".join(wheres) if wheres else "",
-            param_values,
-        )

libs/checkpoint-duckdb/langgraph/store/duckdb/__init__.py

@@ -1,4 +0,0 @@
-from langgraph.store.duckdb.aio import AsyncDuckDBStore
-from langgraph.store.duckdb.base import DuckDBStore
-
-__all__ = ["AsyncDuckDBStore", "DuckDBStore"]

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

@@ -1,195 +0,0 @@
-import asyncio
-import logging
-from contextlib import asynccontextmanager
-from typing import (
-    AsyncIterator,
-    Iterable,
-    Sequence,
-    cast,
-)
-
-import duckdb
-from langgraph.store.base import GetOp, ListNamespacesOp, Op, PutOp, Result, SearchOp
-from langgraph.store.base.batch import AsyncBatchedBaseStore
-from langgraph.store.duckdb.base import (
-    BaseDuckDBStore,
-    _convert_ns,
-    _group_ops,
-    _row_to_item,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class AsyncDuckDBStore(AsyncBatchedBaseStore, BaseDuckDBStore):
-    def __init__(
-        self,
-        conn: duckdb.DuckDBPyConnection,
-    ) -> None:
-        super().__init__()
-        self.conn = conn
-        self.loop = asyncio.get_running_loop()
-
-    async def abatch(self, ops: Iterable[Op]) -> list[Result]:
-        grouped_ops, num_ops = _group_ops(ops)
-        results: list[Result] = [None] * num_ops
-
-        tasks = []
-
-        if GetOp in grouped_ops:
-            tasks.append(
-                self._batch_get_ops(
-                    cast(Sequence[tuple[int, GetOp]], grouped_ops[GetOp]), results
-                )
-            )
-
-        if PutOp in grouped_ops:
-            tasks.append(
-                self._batch_put_ops(
-                    cast(Sequence[tuple[int, PutOp]], grouped_ops[PutOp])
-                )
-            )
-
-        if SearchOp in grouped_ops:
-            tasks.append(
-                self._batch_search_ops(
-                    cast(Sequence[tuple[int, SearchOp]], grouped_ops[SearchOp]),
-                    results,
-                )
-            )
-
-        if ListNamespacesOp in grouped_ops:
-            tasks.append(
-                self._batch_list_namespaces_ops(
-                    cast(
-                        Sequence[tuple[int, ListNamespacesOp]],
-                        grouped_ops[ListNamespacesOp],
-                    ),
-                    results,
-                )
-            )
-
-        await asyncio.gather(*tasks)
-
-        return results
-
-    def batch(self, ops: Iterable[Op]) -> list[Result]:
-        return asyncio.run_coroutine_threadsafe(self.abatch(ops), self.loop).result()
-
-    async def _batch_get_ops(
-        self,
-        get_ops: Sequence[tuple[int, GetOp]],
-        results: list[Result],
-    ) -> None:
-        cursors = []
-        for query, params, namespace, items in self._get_batch_GET_ops_queries(get_ops):
-            cur = self.conn.cursor()
-            await asyncio.to_thread(cur.execute, query, params)
-            cursors.append((cur, namespace, items))
-
-        for cur, namespace, items in cursors:
-            rows = await asyncio.to_thread(cur.fetchall)
-            key_to_row = {row[1]: row for row in rows}
-            for idx, key in items:
-                row = key_to_row.get(key)
-                if row:
-                    results[idx] = _row_to_item(namespace, row)
-                else:
-                    results[idx] = None
-
-    async def _batch_put_ops(
-        self,
-        put_ops: Sequence[tuple[int, PutOp]],
-    ) -> None:
-        queries = self._get_batch_PUT_queries(put_ops)
-        for query, params in queries:
-            cur = self.conn.cursor()
-            await asyncio.to_thread(cur.execute, query, params)
-
-    async def _batch_search_ops(
-        self,
-        search_ops: Sequence[tuple[int, SearchOp]],
-        results: list[Result],
-    ) -> None:
-        queries = self._get_batch_search_queries(search_ops)
-        cursors: list[tuple[duckdb.DuckDBPyConnection, int]] = []
-
-        for (query, params), (idx, _) in zip(queries, search_ops):
-            cur = self.conn.cursor()
-            await asyncio.to_thread(cur.execute, query, params)
-            cursors.append((cur, idx))
-
-        for cur, idx in cursors:
-            rows = await asyncio.to_thread(cur.fetchall)
-            items = [_row_to_item(_convert_ns(row[0]), row) for row in rows]
-            results[idx] = items
-
-    async def _batch_list_namespaces_ops(
-        self,
-        list_ops: Sequence[tuple[int, ListNamespacesOp]],
-        results: list[Result],
-    ) -> None:
-        queries = self._get_batch_list_namespaces_queries(list_ops)
-        cursors: list[tuple[duckdb.DuckDBPyConnection, int]] = []
-        for (query, params), (idx, _) in zip(queries, list_ops):
-            cur = self.conn.cursor()
-            await asyncio.to_thread(cur.execute, query, params)
-            cursors.append((cur, idx))
-
-        for cur, idx in cursors:
-            rows = cast(list[tuple], await asyncio.to_thread(cur.fetchall))
-            namespaces = [_convert_ns(row[0]) for row in rows]
-            results[idx] = namespaces
-
-    @classmethod
-    @asynccontextmanager
-    async def from_conn_string(
-        cls,
-        conn_string: str,
-    ) -> AsyncIterator["AsyncDuckDBStore"]:
-        """Create a new AsyncDuckDBStore instance from a connection string.
-
-        Args:
-            conn_string (str): The DuckDB connection info string.
-
-        Returns:
-            AsyncDuckDBStore: A new AsyncDuckDBStore instance.
-        """
-        with duckdb.connect(conn_string) as conn:
-            yield cls(conn)
-
-    async def setup(self) -> None:
-        """Set up the store database asynchronously.
-
-        This method creates the necessary tables in the DuckDB database if they don't
-        already exist and runs database migrations. It is called automatically when needed and should not be called
-        directly by the user.
-        """
-        cur = self.conn.cursor()
-        try:
-            await asyncio.to_thread(
-                cur.execute, "SELECT v FROM store_migrations ORDER BY v DESC LIMIT 1"
-            )
-            row = await asyncio.to_thread(cur.fetchone)
-            if row is None:
-                version = -1
-            else:
-                version = row[0]
-        except duckdb.CatalogException:
-            version = -1
-            # Create store_migrations table if it doesn't exist
-            await asyncio.to_thread(
-                cur.execute,
-                """
-                CREATE TABLE IF NOT EXISTS store_migrations (
-                    v INTEGER PRIMARY KEY
-                )
-                """,
-            )
-        for v, migration in enumerate(
-            self.MIGRATIONS[version + 1 :], start=version + 1
-        ):
-            await asyncio.to_thread(cur.execute, migration)
-            await asyncio.to_thread(
-                cur.execute, "INSERT INTO store_migrations (v) VALUES (?)", (v,)
-            )

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

@@ -1,408 +0,0 @@
-import asyncio
-import json
-import logging
-from collections import defaultdict
-from contextlib import contextmanager
-from typing import (
-    Any,
-    Generic,
-    Iterable,
-    Iterator,
-    Sequence,
-    TypeVar,
-    Union,
-    cast,
-)
-
-import duckdb
-from langgraph.store.base import (
-    BaseStore,
-    GetOp,
-    Item,
-    ListNamespacesOp,
-    Op,
-    PutOp,
-    Result,
-    SearchItem,
-    SearchOp,
-)
-
-logger = logging.getLogger(__name__)
-
-
-MIGRATIONS = [
-    """
-CREATE TABLE IF NOT EXISTS store (
-    prefix TEXT NOT NULL,
-    key TEXT NOT NULL,
-    value JSON NOT NULL,
-    created_at TIMESTAMP DEFAULT now(),
-    updated_at TIMESTAMP DEFAULT now(),
-    PRIMARY KEY (prefix, key)
-);
-""",
-    """
-CREATE INDEX IF NOT EXISTS store_prefix_idx ON store (prefix);
-""",
-]
-
-C = TypeVar("C", bound=duckdb.DuckDBPyConnection)
-
-
-class BaseDuckDBStore(Generic[C]):
-    MIGRATIONS = MIGRATIONS
-    conn: C
-
-    def _get_batch_GET_ops_queries(
-        self,
-        get_ops: Sequence[tuple[int, GetOp]],
-    ) -> list[tuple[str, tuple, tuple[str, ...], list]]:
-        namespace_groups = defaultdict(list)
-        for idx, op in get_ops:
-            namespace_groups[op.namespace].append((idx, op.key))
-        results = []
-        for namespace, items in namespace_groups.items():
-            _, keys = zip(*items)
-            keys_to_query = ",".join(["?"] * len(keys))
-            query = f"""
-                SELECT prefix, key, value, created_at, updated_at
-                FROM store
-                WHERE prefix = ? AND key IN ({keys_to_query})
-            """
-            params = (_namespace_to_text(namespace), *keys)
-            results.append((query, params, namespace, items))
-        return results
-
-    def _get_batch_PUT_queries(
-        self,
-        put_ops: Sequence[tuple[int, PutOp]],
-    ) -> list[tuple[str, Sequence]]:
-        inserts: list[PutOp] = []
-        deletes: list[PutOp] = []
-        for _, op in put_ops:
-            if op.value is None:
-                deletes.append(op)
-            else:
-                inserts.append(op)
-
-        queries: list[tuple[str, Sequence]] = []
-
-        if deletes:
-            namespace_groups: dict[tuple[str, ...], list[str]] = defaultdict(list)
-            for op in deletes:
-                namespace_groups[op.namespace].append(op.key)
-            for namespace, keys in namespace_groups.items():
-                placeholders = ",".join(["?"] * len(keys))
-                query = (
-                    f"DELETE FROM store WHERE prefix = ? AND key IN ({placeholders})"
-                )
-                params = (_namespace_to_text(namespace), *keys)
-                queries.append((query, params))
-        if inserts:
-            values = []
-            insertion_params = []
-            for op in inserts:
-                values.append("(?, ?, ?, now(), now())")
-                insertion_params.extend(
-                    [
-                        _namespace_to_text(op.namespace),
-                        op.key,
-                        json.dumps(op.value),
-                    ]
-                )
-            values_str = ",".join(values)
-            query = f"""
-                INSERT INTO store (prefix, key, value, created_at, updated_at)
-                VALUES {values_str}
-                ON CONFLICT (prefix, key) DO UPDATE
-                SET value = EXCLUDED.value, updated_at = now()
-            """
-            queries.append((query, insertion_params))
-
-        return queries
-
-    def _get_batch_search_queries(
-        self,
-        search_ops: Sequence[tuple[int, SearchOp]],
-    ) -> list[tuple[str, Sequence]]:
-        queries: list[tuple[str, Sequence]] = []
-        for _, op in search_ops:
-            query = """
-                SELECT prefix, key, value, created_at, updated_at
-                FROM store
-                WHERE prefix LIKE ?
-            """
-            params: list = [f"{_namespace_to_text(op.namespace_prefix)}%"]
-
-            if op.filter:
-                filter_conditions = []
-                for key, value in op.filter.items():
-                    filter_conditions.append(f"json_extract(value, '$.{key}') = ?")
-                    params.append(json.dumps(value))
-                query += " AND " + " AND ".join(filter_conditions)
-
-            query += " ORDER BY updated_at DESC LIMIT ? OFFSET ?"
-            params.extend([op.limit, op.offset])
-
-            queries.append((query, params))
-        return queries
-
-    def _get_batch_list_namespaces_queries(
-        self,
-        list_ops: Sequence[tuple[int, ListNamespacesOp]],
-    ) -> list[tuple[str, Sequence]]:
-        queries: list[tuple[str, Sequence]] = []
-        for _, op in list_ops:
-            query = """
-                WITH split_prefix AS (
-                    SELECT
-                        prefix,
-                        string_split(prefix, '.') AS parts
-                    FROM store
-                )
-                SELECT DISTINCT ON (truncated_prefix)
-                    CASE
-                        WHEN ? IS NOT NULL THEN
-                        array_to_string(array_slice(parts, 1, ?), '.')
-                        ELSE prefix
-                    END AS truncated_prefix,
-                    prefix
-                FROM split_prefix
-            """
-            params: list[Any] = [op.max_depth, op.max_depth]
-
-            conditions = []
-            if op.match_conditions:
-                for condition in op.match_conditions:
-                    if condition.match_type == "prefix":
-                        conditions.append("prefix LIKE ?")
-                        params.append(
-                            f"{_namespace_to_text(condition.path, handle_wildcards=True)}%"
-                        )
-                    elif condition.match_type == "suffix":
-                        conditions.append("prefix LIKE ?")
-                        params.append(
-                            f"%{_namespace_to_text(condition.path, handle_wildcards=True)}"
-                        )
-                    else:
-                        logger.warning(
-                            f"Unknown match_type in list_namespaces: {condition.match_type}"
-                        )
-
-            if conditions:
-                query += " WHERE " + " AND ".join(conditions)
-
-            query += " ORDER BY prefix LIMIT ? OFFSET ?"
-            params.extend([op.limit, op.offset])
-            queries.append((query, params))
-
-        return queries
-
-
-class DuckDBStore(BaseStore, BaseDuckDBStore[duckdb.DuckDBPyConnection]):
-    def __init__(
-        self,
-        conn: duckdb.DuckDBPyConnection,
-    ) -> None:
-        super().__init__()
-        self.conn = conn
-
-    def batch(self, ops: Iterable[Op]) -> list[Result]:
-        grouped_ops, num_ops = _group_ops(ops)
-        results: list[Result] = [None] * num_ops
-
-        if GetOp in grouped_ops:
-            self._batch_get_ops(
-                cast(Sequence[tuple[int, GetOp]], grouped_ops[GetOp]), results
-            )
-
-        if PutOp in grouped_ops:
-            self._batch_put_ops(cast(Sequence[tuple[int, PutOp]], grouped_ops[PutOp]))
-
-        if SearchOp in grouped_ops:
-            self._batch_search_ops(
-                cast(Sequence[tuple[int, SearchOp]], grouped_ops[SearchOp]),
-                results,
-            )
-
-        if ListNamespacesOp in grouped_ops:
-            self._batch_list_namespaces_ops(
-                cast(
-                    Sequence[tuple[int, ListNamespacesOp]],
-                    grouped_ops[ListNamespacesOp],
-                ),
-                results,
-            )
-
-        return results
-
-    async def abatch(self, ops: Iterable[Op]) -> list[Result]:
-        return await asyncio.get_running_loop().run_in_executor(None, self.batch, ops)
-
-    def _batch_get_ops(
-        self,
-        get_ops: Sequence[tuple[int, GetOp]],
-        results: list[Result],
-    ) -> None:
-        cursors = []
-        for query, params, namespace, items in self._get_batch_GET_ops_queries(get_ops):
-            cur = self.conn.cursor()
-            cur.execute(query, params)
-            cursors.append((cur, namespace, items))
-
-        for cur, namespace, items in cursors:
-            rows = cur.fetchall()
-            key_to_row = {row[1]: row for row in rows}
-            for idx, key in items:
-                row = key_to_row.get(key)
-                if row:
-                    results[idx] = _row_to_item(namespace, row)
-                else:
-                    results[idx] = None
-
-    def _batch_put_ops(
-        self,
-        put_ops: Sequence[tuple[int, PutOp]],
-    ) -> None:
-        queries = self._get_batch_PUT_queries(put_ops)
-        for query, params in queries:
-            cur = self.conn.cursor()
-            cur.execute(query, params)
-
-    def _batch_search_ops(
-        self,
-        search_ops: Sequence[tuple[int, SearchOp]],
-        results: list[Result],
-    ) -> None:
-        queries = self._get_batch_search_queries(search_ops)
-        cursors: list[tuple[duckdb.DuckDBPyConnection, int]] = []
-
-        for (query, params), (idx, _) in zip(queries, search_ops):
-            cur = self.conn.cursor()
-            cur.execute(query, params)
-            cursors.append((cur, idx))
-
-        for cur, idx in cursors:
-            rows = cur.fetchall()
-            items = [_row_to_search_item(_convert_ns(row[0]), row) for row in rows]
-            results[idx] = items
-
-    def _batch_list_namespaces_ops(
-        self,
-        list_ops: Sequence[tuple[int, ListNamespacesOp]],
-        results: list[Result],
-    ) -> None:
-        queries = self._get_batch_list_namespaces_queries(list_ops)
-        cursors: list[tuple[duckdb.DuckDBPyConnection, int]] = []
-        for (query, params), (idx, _) in zip(queries, list_ops):
-            cur = self.conn.cursor()
-            cur.execute(query, params)
-            cursors.append((cur, idx))
-
-        for cur, idx in cursors:
-            rows = cast(list[dict], cur.fetchall())
-            namespaces = [_convert_ns(row[0]) for row in rows]
-            results[idx] = namespaces
-
-    @classmethod
-    @contextmanager
-    def from_conn_string(
-        cls,
-        conn_string: str,
-    ) -> Iterator["DuckDBStore"]:
-        """Create a new BaseDuckDBStore instance from a connection string.
-
-        Args:
-            conn_string (str): The DuckDB connection info string.
-
-        Returns:
-            DuckDBStore: A new DuckDBStore instance.
-        """
-        with duckdb.connect(conn_string) as conn:
-            yield cls(conn=conn)
-
-    def setup(self) -> None:
-        """Set up the store database.
-
-        This method creates the necessary tables in the DuckDB database if they don't
-        already exist and runs database migrations. It is called automatically when needed and should not be called
-        directly by the user.
-        """
-        with self.conn.cursor() as cur:
-            try:
-                cur.execute("SELECT v FROM store_migrations ORDER BY v DESC LIMIT 1")
-                row = cast(dict, cur.fetchone())
-                if row is None:
-                    version = -1
-                else:
-                    version = row["v"]
-            except duckdb.CatalogException:
-                version = -1
-                # Create store_migrations table if it doesn't exist
-                cur.execute(
-                    """
-                    CREATE TABLE IF NOT EXISTS store_migrations (
-                        v INTEGER PRIMARY KEY
-                    )
-                """
-                )
-            for v, migration in enumerate(
-                self.MIGRATIONS[version + 1 :], start=version + 1
-            ):
-                cur.execute(migration)
-                cur.execute("INSERT INTO store_migrations (v) VALUES (?)", (v,))
-
-
-def _namespace_to_text(
-    namespace: tuple[str, ...], handle_wildcards: bool = False
-) -> str:
-    """Convert namespace tuple to text string."""
-    if handle_wildcards:
-        namespace = tuple("%" if val == "*" else val for val in namespace)
-    return ".".join(namespace)
-
-
-def _row_to_item(
-    namespace: tuple[str, ...],
-    row: tuple,
-) -> Item:
-    """Convert a row from the database into an Item."""
-    _, key, val, created_at, updated_at = row
-    return Item(
-        value=val if isinstance(val, dict) else json.loads(val),
-        key=key,
-        namespace=namespace,
-        created_at=created_at,
-        updated_at=updated_at,
-    )
-
-
-def _row_to_search_item(
-    namespace: tuple[str, ...],
-    row: tuple,
-) -> SearchItem:
-    """Convert a row from the database into an SearchItem."""
-    # TODO: Add support for search
-    _, key, val, created_at, updated_at = row
-    return SearchItem(
-        value=val if isinstance(val, dict) else json.loads(val),
-        key=key,
-        namespace=namespace,
-        created_at=created_at,
-        updated_at=updated_at,
-    )
-
-
-def _group_ops(ops: Iterable[Op]) -> tuple[dict[type, list[tuple[int, Op]]], int]:
-    grouped_ops: dict[type, list[tuple[int, Op]]] = defaultdict(list)
-    tot = 0
-    for idx, op in enumerate(ops):
-        grouped_ops[type(op)].append((idx, op))
-        tot += 1
-    return grouped_ops, tot
-
-
-def _convert_ns(namespace: Union[str, list]) -> tuple[str, ...]:
-    if isinstance(namespace, list):
-        return tuple(namespace)
-    return tuple(namespace.split("."))

libs/checkpoint-duckdb/pyproject.toml

@@ -1,60 +0,0 @@
-[tool.poetry]
-name = "langgraph-checkpoint-duckdb"
-version = "2.0.2"
-description = "Library with a DuckDB implementation of LangGraph checkpoint saver."
-authors = []
-license = "MIT"
-readme = "README.md"
-repository = "https://www.github.com/langchain-ai/langgraph"
-packages = [{ include = "langgraph" }]
-
-[tool.poetry.dependencies]
-python = "^3.9.0,<4.0"
-langgraph-checkpoint = "^2.0.2"
-duckdb = ">=1.1.2"
-
-[tool.poetry.group.dev.dependencies]
-ruff = "^0.6.2"
-codespell = "^2.2.0"
-pytest = "^7.2.1"
-anyio = "^4.4.0"
-pytest-asyncio = "^0.21.1"
-pytest-mock = "^3.11.1"
-pytest-watch = "^4.2.0"
-mypy = "^1.10.0"
-langgraph-checkpoint = {path = "../checkpoint", develop = true}
-
-[tool.pytest.ini_options]
-# --strict-markers will raise errors on unknown marks.
-# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
-#
-# https://docs.pytest.org/en/7.1.x/reference/reference.html
-# --strict-config       any warnings encountered while parsing the `pytest`
-#                       section of the configuration file raise errors.
-addopts = "--strict-markers --strict-config --durations=5 -vv"
-asyncio_mode = "auto"
-
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
-
-[tool.ruff]
-lint.select = [
-  "E",  # pycodestyle
-  "F",  # Pyflakes
-  "UP", # pyupgrade
-  "B",  # flake8-bugbear
-  "I",  # isort
-]
-lint.ignore = ["E501", "B008", "UP007", "UP006"]
-
-[tool.mypy]
-# https://mypy.readthedocs.io/en/stable/config_file.html
-disallow_untyped_defs = "True"
-explicit_package_bases = "True"
-warn_no_return = "False"
-warn_unused_ignores = "True"
-warn_redundant_casts = "True"
-allow_redefinition = "True"
-disable_error_code = "typeddict-item, return-value"

libs/checkpoint-duckdb/tests/test_async.py

@@ -1,112 +0,0 @@
-from typing import Any
-
-import pytest
-from langchain_core.runnables import RunnableConfig
-
-from langgraph.checkpoint.base import (
-    Checkpoint,
-    CheckpointMetadata,
-    create_checkpoint,
-    empty_checkpoint,
-)
-from langgraph.checkpoint.duckdb.aio import AsyncDuckDBSaver
-
-
-class TestAsyncDuckDBSaver:
-    @pytest.fixture(autouse=True)
-    async def setup(self) -> None:
-        # objects for test setup
-        self.config_1: RunnableConfig = {
-            "configurable": {
-                "thread_id": "thread-1",
-                # for backwards compatibility testing
-                "thread_ts": "1",
-                "checkpoint_ns": "",
-            }
-        }
-        self.config_2: RunnableConfig = {
-            "configurable": {
-                "thread_id": "thread-2",
-                "checkpoint_id": "2",
-                "checkpoint_ns": "",
-            }
-        }
-        self.config_3: RunnableConfig = {
-            "configurable": {
-                "thread_id": "thread-2",
-                "checkpoint_id": "2-inner",
-                "checkpoint_ns": "inner",
-            }
-        }
-
-        self.chkpnt_1: Checkpoint = empty_checkpoint()
-        self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1)
-        self.chkpnt_3: Checkpoint = empty_checkpoint()
-
-        self.metadata_1: CheckpointMetadata = {
-            "source": "input",
-            "step": 2,
-            "writes": {},
-            "score": 1,
-        }
-        self.metadata_2: CheckpointMetadata = {
-            "source": "loop",
-            "step": 1,
-            "writes": {"foo": "bar"},
-            "score": None,
-        }
-        self.metadata_3: CheckpointMetadata = {}
-
-    async def test_asearch(self) -> None:
-        async with AsyncDuckDBSaver.from_conn_string(":memory:") as saver:
-            await saver.setup()
-            await saver.aput(self.config_1, self.chkpnt_1, self.metadata_1, {})
-            await saver.aput(self.config_2, self.chkpnt_2, self.metadata_2, {})
-            await saver.aput(self.config_3, self.chkpnt_3, self.metadata_3, {})
-
-            # call method / assertions
-            query_1 = {"source": "input"}  # search by 1 key
-            query_2 = {
-                "step": 1,
-                "writes": {"foo": "bar"},
-            }  # search by multiple keys
-            query_3: dict[str, Any] = {}  # search by no keys, return all checkpoints
-            query_4 = {"source": "update", "step": 1}  # no match
-
-            search_results_1 = [c async for c in saver.alist(None, filter=query_1)]
-            assert len(search_results_1) == 1
-            assert search_results_1[0].metadata == self.metadata_1
-
-            search_results_2 = [c async for c in saver.alist(None, filter=query_2)]
-            assert len(search_results_2) == 1
-            assert search_results_2[0].metadata == self.metadata_2
-
-            search_results_3 = [c async for c in saver.alist(None, filter=query_3)]
-            assert len(search_results_3) == 3
-
-            search_results_4 = [c async for c in saver.alist(None, filter=query_4)]
-            assert len(search_results_4) == 0
-
-            # search by config (defaults to checkpoints across all namespaces)
-            search_results_5 = [
-                c
-                async for c in saver.alist({"configurable": {"thread_id": "thread-2"}})
-            ]
-            assert len(search_results_5) == 2
-            assert {
-                search_results_5[0].config["configurable"]["checkpoint_ns"],
-                search_results_5[1].config["configurable"]["checkpoint_ns"],
-            } == {"", "inner"}
-
-            # TODO: test before and limit params
-
-    async def test_null_chars(self) -> None:
-        async with AsyncDuckDBSaver.from_conn_string(":memory:") as saver:
-            await saver.setup()
-            config = await saver.aput(
-                self.config_1, self.chkpnt_1, {"my_key": "\x00abc"}, {}
-            )
-            assert (await saver.aget_tuple(config)).metadata["my_key"] == "abc"  # type: ignore
-            assert [c async for c in saver.alist(None, filter={"my_key": "abc"})][
-                0
-            ].metadata["my_key"] == "abc"

libs/checkpoint-duckdb/tests/test_async_store.py

@@ -1,517 +0,0 @@
-# type: ignore
-import uuid
-from datetime import datetime
-from typing import Any
-from unittest.mock import MagicMock
-
-import pytest
-
-from langgraph.store.base import GetOp, Item, ListNamespacesOp, PutOp, SearchOp
-from langgraph.store.duckdb import AsyncDuckDBStore
-
-
-class MockCursor:
-    def __init__(self, fetch_result: Any) -> None:
-        self.fetch_result = fetch_result
-        self.execute = MagicMock()
-        self.fetchall = MagicMock(return_value=self.fetch_result)
-
-
-class MockConnection:
-    def __init__(self) -> None:
-        self.cursor = MagicMock()
-
-
-@pytest.fixture
-def mock_connection() -> MockConnection:
-    return MockConnection()
-
-
-@pytest.fixture
-async def store(mock_connection: MockConnection) -> AsyncDuckDBStore:
-    duck_db_store = AsyncDuckDBStore(mock_connection)
-    await duck_db_store.setup()
-    return duck_db_store
-
-
-async def test_abatch_order(store: AsyncDuckDBStore) -> None:
-    mock_connection = store.conn
-    mock_get_cursor = MockCursor(
-        [
-            (
-                "test.foo",
-                "key1",
-                '{"data": "value1"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-            (
-                "test.bar",
-                "key2",
-                '{"data": "value2"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-        ]
-    )
-    mock_search_cursor = MockCursor(
-        [
-            (
-                "test.foo",
-                "key1",
-                '{"data": "value1"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-        ]
-    )
-    mock_list_namespaces_cursor = MockCursor(
-        [
-            ("test",),
-        ]
-    )
-
-    failures = []
-
-    def cursor_side_effect() -> Any:
-        cursor = MagicMock()
-
-        def execute_side_effect(query: str, *params: Any) -> None:
-            # My super sophisticated database.
-            if "WHERE prefix = ? AND key" in query:
-                cursor.fetchall = mock_get_cursor.fetchall
-            elif "SELECT prefix, key, value" in query:
-                cursor.fetchall = mock_search_cursor.fetchall
-            elif "SELECT DISTINCT ON (truncated_prefix)" in query:
-                cursor.fetchall = mock_list_namespaces_cursor.fetchall
-            elif "INSERT INTO " in query:
-                pass
-            else:
-                e = ValueError(f"Unmatched query: {query}")
-                failures.append(e)
-                raise e
-
-        cursor.execute = MagicMock(side_effect=execute_side_effect)
-        return cursor
-
-    mock_connection.cursor.side_effect = cursor_side_effect  # type: ignore
-
-    ops = [
-        GetOp(namespace=("test",), key="key1"),
-        PutOp(namespace=("test",), key="key2", value={"data": "value2"}),
-        SearchOp(
-            namespace_prefix=("test",), filter={"data": "value1"}, limit=10, offset=0
-        ),
-        ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0),
-        GetOp(namespace=("test",), key="key3"),
-    ]
-    results = await store.abatch(ops)
-    assert not failures
-    assert len(results) == 5
-    assert isinstance(results[0], Item)
-    assert isinstance(results[0].value, dict)
-    assert results[0].value == {"data": "value1"}
-    assert results[0].key == "key1"
-    assert results[1] is None
-    assert isinstance(results[2], list)
-    assert len(results[2]) == 1
-    assert isinstance(results[3], list)
-    assert results[3] == [("test",)]
-    assert results[4] is None
-
-    ops_reordered = [
-        SearchOp(namespace_prefix=("test",), filter=None, limit=5, offset=0),
-        GetOp(namespace=("test",), key="key2"),
-        ListNamespacesOp(match_conditions=None, max_depth=None, limit=5, offset=0),
-        PutOp(namespace=("test",), key="key3", value={"data": "value3"}),
-        GetOp(namespace=("test",), key="key1"),
-    ]
-
-    results_reordered = await store.abatch(ops_reordered)
-    assert not failures
-    assert len(results_reordered) == 5
-    assert isinstance(results_reordered[0], list)
-    assert len(results_reordered[0]) == 1
-    assert isinstance(results_reordered[1], Item)
-    assert results_reordered[1].value == {"data": "value2"}
-    assert results_reordered[1].key == "key2"
-    assert isinstance(results_reordered[2], list)
-    assert results_reordered[2] == [("test",)]
-    assert results_reordered[3] is None
-    assert isinstance(results_reordered[4], Item)
-    assert results_reordered[4].value == {"data": "value1"}
-    assert results_reordered[4].key == "key1"
-
-
-async def test_batch_get_ops(store: AsyncDuckDBStore) -> None:
-    mock_connection = store.conn
-    mock_cursor = MockCursor(
-        [
-            (
-                "test.foo",
-                "key1",
-                '{"data": "value1"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-            (
-                "test.bar",
-                "key2",
-                '{"data": "value2"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-        ]
-    )
-    mock_connection.cursor.return_value = mock_cursor
-
-    ops = [
-        GetOp(namespace=("test",), key="key1"),
-        GetOp(namespace=("test",), key="key2"),
-        GetOp(namespace=("test",), key="key3"),
-    ]
-
-    results = await store.abatch(ops)
-
-    assert len(results) == 3
-    assert results[0] is not None
-    assert results[1] is not None
-    assert results[2] is None
-    assert results[0].key == "key1"
-    assert results[1].key == "key2"
-
-
-async def test_batch_put_ops(store: AsyncDuckDBStore) -> None:
-    mock_connection = store.conn
-    mock_cursor = MockCursor([])
-    mock_connection.cursor.return_value = mock_cursor
-
-    ops = [
-        PutOp(namespace=("test",), key="key1", value={"data": "value1"}),
-        PutOp(namespace=("test",), key="key2", value={"data": "value2"}),
-        PutOp(namespace=("test",), key="key3", value=None),
-    ]
-
-    results = await store.abatch(ops)
-
-    assert len(results) == 3
-    assert all(result is None for result in results)
-    assert mock_cursor.execute.call_count == 2
-
-
-async def test_batch_search_ops(store: AsyncDuckDBStore) -> None:
-    mock_connection = store.conn
-    mock_cursor = MockCursor(
-        [
-            (
-                "test.foo",
-                "key1",
-                '{"data": "value1"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-            (
-                "test.bar",
-                "key2",
-                '{"data": "value2"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-        ]
-    )
-    mock_connection.cursor.return_value = mock_cursor
-
-    ops = [
-        SearchOp(
-            namespace_prefix=("test",), filter={"data": "value1"}, limit=10, offset=0
-        ),
-        SearchOp(namespace_prefix=("test",), filter=None, limit=5, offset=0),
-    ]
-
-    results = await store.abatch(ops)
-
-    assert len(results) == 2
-    assert len(results[0]) == 2
-    assert len(results[1]) == 2
-
-
-async def test_batch_list_namespaces_ops(store: AsyncDuckDBStore) -> None:
-    mock_connection = store.conn
-    mock_cursor = MockCursor([("test.namespace1",), ("test.namespace2",)])
-    mock_connection.cursor.return_value = mock_cursor
-
-    ops = [ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0)]
-
-    results = await store.abatch(ops)
-
-    assert len(results) == 1
-    assert results[0] == [("test", "namespace1"), ("test", "namespace2")]
-
-
-# The following use the actual DB connection
-
-
-async def test_basic_store_ops() -> None:
-    async with AsyncDuckDBStore.from_conn_string(":memory:") as store:
-        await store.setup()
-        namespace = ("test", "documents")
-        item_id = "doc1"
-        item_value = {"title": "Test Document", "content": "Hello, World!"}
-
-        await store.aput(namespace, item_id, item_value)
-        item = await store.aget(namespace, item_id)
-
-        assert item
-        assert item.namespace == namespace
-        assert item.key == item_id
-        assert item.value == item_value
-
-        updated_value = {
-            "title": "Updated Test Document",
-            "content": "Hello, LangGraph!",
-        }
-        await store.aput(namespace, item_id, updated_value)
-        updated_item = await store.aget(namespace, item_id)
-
-        assert updated_item.value == updated_value
-        assert updated_item.updated_at > item.updated_at
-        different_namespace = ("test", "other_documents")
-        item_in_different_namespace = await store.aget(different_namespace, item_id)
-        assert item_in_different_namespace is None
-
-        new_item_id = "doc2"
-        new_item_value = {"title": "Another Document", "content": "Greetings!"}
-        await store.aput(namespace, new_item_id, new_item_value)
-
-        search_results = await store.asearch(["test"], limit=10)
-        items = search_results
-        assert len(items) == 2
-        assert any(item.key == item_id for item in items)
-        assert any(item.key == new_item_id for item in items)
-
-        namespaces = await store.alist_namespaces(prefix=["test"])
-        assert ("test", "documents") in namespaces
-
-        await store.adelete(namespace, item_id)
-        await store.adelete(namespace, new_item_id)
-        deleted_item = await store.aget(namespace, item_id)
-        assert deleted_item is None
-
-        deleted_item = await store.aget(namespace, new_item_id)
-        assert deleted_item is None
-
-        empty_search_results = await store.asearch(["test"], limit=10)
-        assert len(empty_search_results) == 0
-
-
-async def test_list_namespaces() -> None:
-    async with AsyncDuckDBStore.from_conn_string(":memory:") as store:
-        await store.setup()
-        test_pref = str(uuid.uuid4())
-        test_namespaces = [
-            (test_pref, "test", "documents", "public", test_pref),
-            (test_pref, "test", "documents", "private", test_pref),
-            (test_pref, "test", "images", "public", test_pref),
-            (test_pref, "test", "images", "private", test_pref),
-            (test_pref, "prod", "documents", "public", test_pref),
-            (
-                test_pref,
-                "prod",
-                "documents",
-                "some",
-                "nesting",
-                "public",
-                test_pref,
-            ),
-            (test_pref, "prod", "documents", "private", test_pref),
-        ]
-
-        for namespace in test_namespaces:
-            await store.aput(namespace, "dummy", {"content": "dummy"})
-
-        prefix_result = await store.alist_namespaces(prefix=[test_pref, "test"])
-        assert len(prefix_result) == 4
-        assert all([ns[1] == "test" for ns in prefix_result])
-
-        specific_prefix_result = await store.alist_namespaces(
-            prefix=[test_pref, "test", "documents"]
-        )
-        assert len(specific_prefix_result) == 2
-        assert all([ns[1:3] == ("test", "documents") for ns in specific_prefix_result])
-
-        suffix_result = await store.alist_namespaces(suffix=["public", test_pref])
-        assert len(suffix_result) == 4
-        assert all(ns[-2] == "public" for ns in suffix_result)
-
-        prefix_suffix_result = await store.alist_namespaces(
-            prefix=[test_pref, "test"], suffix=["public", test_pref]
-        )
-        assert len(prefix_suffix_result) == 2
-        assert all(
-            ns[1] == "test" and ns[-2] == "public" for ns in prefix_suffix_result
-        )
-
-        wildcard_prefix_result = await store.alist_namespaces(
-            prefix=[test_pref, "*", "documents"]
-        )
-        assert len(wildcard_prefix_result) == 5
-        assert all(ns[2] == "documents" for ns in wildcard_prefix_result)
-
-        wildcard_suffix_result = await store.alist_namespaces(
-            suffix=["*", "public", test_pref]
-        )
-        assert len(wildcard_suffix_result) == 4
-        assert all(ns[-2] == "public" for ns in wildcard_suffix_result)
-        wildcard_single = await store.alist_namespaces(
-            suffix=["some", "*", "public", test_pref]
-        )
-        assert len(wildcard_single) == 1
-        assert wildcard_single[0] == (
-            test_pref,
-            "prod",
-            "documents",
-            "some",
-            "nesting",
-            "public",
-            test_pref,
-        )
-
-        max_depth_result = await store.alist_namespaces(max_depth=3)
-        assert all([len(ns) <= 3 for ns in max_depth_result])
-        max_depth_result = await store.alist_namespaces(
-            max_depth=4, prefix=[test_pref, "*", "documents"]
-        )
-        assert (
-            len(set(tuple(res) for res in max_depth_result))
-            == len(max_depth_result)
-            == 5
-        )
-
-        limit_result = await store.alist_namespaces(prefix=[test_pref], limit=3)
-        assert len(limit_result) == 3
-
-        offset_result = await store.alist_namespaces(prefix=[test_pref], offset=3)
-        assert len(offset_result) == len(test_namespaces) - 3
-
-        empty_prefix_result = await store.alist_namespaces(prefix=[test_pref])
-        assert len(empty_prefix_result) == len(test_namespaces)
-        assert set(tuple(ns) for ns in empty_prefix_result) == set(
-            tuple(ns) for ns in test_namespaces
-        )
-
-        for namespace in test_namespaces:
-            await store.adelete(namespace, "dummy")
-
-
-async def test_search():
-    async with AsyncDuckDBStore.from_conn_string(":memory:") as store:
-        await store.setup()
-        test_namespaces = [
-            ("test_search", "documents", "user1"),
-            ("test_search", "documents", "user2"),
-            ("test_search", "reports", "department1"),
-            ("test_search", "reports", "department2"),
-        ]
-        test_items = [
-            {"title": "Doc 1", "author": "John Doe", "tags": ["important"]},
-            {"title": "Doc 2", "author": "Jane Smith", "tags": ["draft"]},
-            {"title": "Report A", "author": "John Doe", "tags": ["final"]},
-            {"title": "Report B", "author": "Alice Johnson", "tags": ["draft"]},
-        ]
-        empty = await store.asearch(
-            (
-                "scoped",
-                "assistant_id",
-                "shared",
-                "6c5356f6-63ab-4158-868d-cd9fd14c736e",
-            ),
-            limit=10,
-            offset=0,
-        )
-        assert len(empty) == 0
-
-        for namespace, item in zip(test_namespaces, test_items):
-            await store.aput(namespace, f"item_{namespace[-1]}", item)
-
-        docs_result = await store.asearch(["test_search", "documents"])
-        assert len(docs_result) == 2
-        assert all([item.namespace[1] == "documents" for item in docs_result]), [
-            item.namespace for item in docs_result
-        ]
-
-        reports_result = await store.asearch(["test_search", "reports"])
-        assert len(reports_result) == 2
-        assert all(item.namespace[1] == "reports" for item in reports_result)
-
-        limited_result = await store.asearch(["test_search"], limit=2)
-        assert len(limited_result) == 2
-        offset_result = await store.asearch(["test_search"])
-        assert len(offset_result) == 4
-
-        offset_result = await store.asearch(["test_search"], offset=2)
-        assert len(offset_result) == 2
-        assert all(item not in limited_result for item in offset_result)
-
-        john_doe_result = await store.asearch(
-            ["test_search"], filter={"author": "John Doe"}
-        )
-        assert len(john_doe_result) == 2
-        assert all(item.value["author"] == "John Doe" for item in john_doe_result)
-
-        draft_result = await store.asearch(["test_search"], filter={"tags": ["draft"]})
-        assert len(draft_result) == 2
-        assert all("draft" in item.value["tags"] for item in draft_result)
-
-        page1 = await store.asearch(["test_search"], limit=2, offset=0)
-        page2 = await store.asearch(["test_search"], limit=2, offset=2)
-        all_items = page1 + page2
-        assert len(all_items) == 4
-        assert len(set(item.key for item in all_items)) == 4
-        empty = await store.asearch(
-            (
-                "scoped",
-                "assistant_id",
-                "shared",
-                "again",
-                "maybe",
-                "some-long",
-                "6be5cb0e-2eb4-42e6-bb6b-fba3c269db25",
-            ),
-            limit=10,
-            offset=0,
-        )
-        assert len(empty) == 0
-
-        # Test with a namespace beginning with a number (like a UUID)
-        uuid_namespace = (str(uuid.uuid4()), "documents")
-        uuid_item_id = "uuid_doc"
-        uuid_item_value = {
-            "title": "UUID Document",
-            "content": "This document has a UUID namespace.",
-        }
-
-        # Insert the item with the UUID namespace
-        await store.aput(uuid_namespace, uuid_item_id, uuid_item_value)
-
-        # Retrieve the item to verify it was stored correctly
-        retrieved_item = await store.aget(uuid_namespace, uuid_item_id)
-        assert retrieved_item is not None
-        assert retrieved_item.namespace == uuid_namespace
-        assert retrieved_item.key == uuid_item_id
-        assert retrieved_item.value == uuid_item_value
-
-        # Search for the item using the UUID namespace
-        search_result = await store.asearch([uuid_namespace[0]])
-        assert len(search_result) == 1
-        assert search_result[0].key == uuid_item_id
-        assert search_result[0].value == uuid_item_value
-
-        # Clean up: delete the item with the UUID namespace
-        await store.adelete(uuid_namespace, uuid_item_id)
-
-        # Verify the item was deleted
-        deleted_item = await store.aget(uuid_namespace, uuid_item_id)
-        assert deleted_item is None
-
-        for namespace in test_namespaces:
-            await store.adelete(namespace, f"item_{namespace[-1]}")

libs/checkpoint-duckdb/tests/test_store.py

@@ -1,457 +0,0 @@
-# type: ignore
-import uuid
-from datetime import datetime
-from typing import Any
-from unittest.mock import MagicMock
-
-import pytest
-
-from langgraph.store.base import GetOp, Item, ListNamespacesOp, PutOp, SearchOp
-from langgraph.store.duckdb import DuckDBStore
-
-
-class MockCursor:
-    def __init__(self, fetch_result: Any) -> None:
-        self.fetch_result = fetch_result
-        self.execute = MagicMock()
-        self.fetchall = MagicMock(return_value=self.fetch_result)
-
-
-class MockConnection:
-    def __init__(self) -> None:
-        self.cursor = MagicMock()
-
-
-@pytest.fixture
-def mock_connection() -> MockConnection:
-    return MockConnection()
-
-
-@pytest.fixture
-def store(mock_connection: MockConnection) -> DuckDBStore:
-    duck_db_store = DuckDBStore(mock_connection)
-    duck_db_store.setup()
-    return duck_db_store
-
-
-def test_batch_order(store: DuckDBStore) -> None:
-    mock_connection = store.conn
-    mock_get_cursor = MockCursor(
-        [
-            (
-                "test.foo",
-                "key1",
-                '{"data": "value1"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-            (
-                "test.bar",
-                "key2",
-                '{"data": "value2"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-        ]
-    )
-    mock_search_cursor = MockCursor(
-        [
-            (
-                "test.foo",
-                "key1",
-                '{"data": "value1"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-        ]
-    )
-    mock_list_namespaces_cursor = MockCursor(
-        [
-            ("test",),
-        ]
-    )
-
-    failures = []
-
-    def cursor_side_effect() -> Any:
-        cursor = MagicMock()
-
-        def execute_side_effect(query: str, *params: Any) -> None:
-            # My super sophisticated database.
-            if "WHERE prefix = ? AND key" in query:
-                cursor.fetchall = mock_get_cursor.fetchall
-            elif "SELECT prefix, key, value" in query:
-                cursor.fetchall = mock_search_cursor.fetchall
-            elif "SELECT DISTINCT ON (truncated_prefix)" in query:
-                cursor.fetchall = mock_list_namespaces_cursor.fetchall
-            elif "INSERT INTO " in query:
-                pass
-            else:
-                e = ValueError(f"Unmatched query: {query}")
-                failures.append(e)
-                raise e
-
-        cursor.execute = MagicMock(side_effect=execute_side_effect)
-        return cursor
-
-    mock_connection.cursor.side_effect = cursor_side_effect
-
-    ops = [
-        GetOp(namespace=("test",), key="key1"),
-        PutOp(namespace=("test",), key="key2", value={"data": "value2"}),
-        SearchOp(
-            namespace_prefix=("test",), filter={"data": "value1"}, limit=10, offset=0
-        ),
-        ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0),
-        GetOp(namespace=("test",), key="key3"),
-    ]
-    results = store.batch(ops)
-    assert not failures
-    assert len(results) == 5
-    assert isinstance(results[0], Item)
-    assert isinstance(results[0].value, dict)
-    assert results[0].value == {"data": "value1"}
-    assert results[0].key == "key1"
-    assert results[1] is None
-    assert isinstance(results[2], list)
-    assert len(results[2]) == 1
-    assert isinstance(results[3], list)
-    assert results[3] == [("test",)]
-    assert results[4] is None
-
-    ops_reordered = [
-        SearchOp(namespace_prefix=("test",), filter=None, limit=5, offset=0),
-        GetOp(namespace=("test",), key="key2"),
-        ListNamespacesOp(match_conditions=None, max_depth=None, limit=5, offset=0),
-        PutOp(namespace=("test",), key="key3", value={"data": "value3"}),
-        GetOp(namespace=("test",), key="key1"),
-    ]
-
-    results_reordered = store.batch(ops_reordered)
-    assert not failures
-    assert len(results_reordered) == 5
-    assert isinstance(results_reordered[0], list)
-    assert len(results_reordered[0]) == 1
-    assert isinstance(results_reordered[1], Item)
-    assert results_reordered[1].value == {"data": "value2"}
-    assert results_reordered[1].key == "key2"
-    assert isinstance(results_reordered[2], list)
-    assert results_reordered[2] == [("test",)]
-    assert results_reordered[3] is None
-    assert isinstance(results_reordered[4], Item)
-    assert results_reordered[4].value == {"data": "value1"}
-    assert results_reordered[4].key == "key1"
-
-
-def test_batch_get_ops(store: DuckDBStore) -> None:
-    mock_connection = store.conn
-    mock_cursor = MockCursor(
-        [
-            (
-                "test.foo",
-                "key1",
-                '{"data": "value1"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-            (
-                "test.bar",
-                "key2",
-                '{"data": "value2"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-        ]
-    )
-    mock_connection.cursor.return_value = mock_cursor
-
-    ops = [
-        GetOp(namespace=("test",), key="key1"),
-        GetOp(namespace=("test",), key="key2"),
-        GetOp(namespace=("test",), key="key3"),
-    ]
-
-    results = store.batch(ops)
-
-    assert len(results) == 3
-    assert results[0] is not None
-    assert results[1] is not None
-    assert results[2] is None
-    assert results[0].key == "key1"
-    assert results[1].key == "key2"
-
-
-def test_batch_put_ops(store: DuckDBStore) -> None:
-    mock_connection = store.conn
-    mock_cursor = MockCursor([])
-    mock_connection.cursor.return_value = mock_cursor
-
-    ops = [
-        PutOp(namespace=("test",), key="key1", value={"data": "value1"}),
-        PutOp(namespace=("test",), key="key2", value={"data": "value2"}),
-        PutOp(namespace=("test",), key="key3", value=None),
-    ]
-
-    results = store.batch(ops)
-
-    assert len(results) == 3
-    assert all(result is None for result in results)
-    assert mock_cursor.execute.call_count == 2
-
-
-def test_batch_search_ops(store: DuckDBStore) -> None:
-    mock_connection = store.conn
-    mock_cursor = MockCursor(
-        [
-            (
-                "test.foo",
-                "key1",
-                '{"data": "value1"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-            (
-                "test.bar",
-                "key2",
-                '{"data": "value2"}',
-                datetime.now(),
-                datetime.now(),
-            ),
-        ]
-    )
-    mock_connection.cursor.return_value = mock_cursor
-
-    ops = [
-        SearchOp(
-            namespace_prefix=("test",), filter={"data": "value1"}, limit=10, offset=0
-        ),
-        SearchOp(namespace_prefix=("test",), filter=None, limit=5, offset=0),
-    ]
-
-    results = store.batch(ops)
-
-    assert len(results) == 2
-    assert len(results[0]) == 2
-    assert len(results[1]) == 2
-
-
-def test_batch_list_namespaces_ops(store: DuckDBStore) -> None:
-    mock_connection = store.conn
-    mock_cursor = MockCursor([("test.namespace1",), ("test.namespace2",)])
-    mock_connection.cursor.return_value = mock_cursor
-
-    ops = [ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0)]
-
-    results = store.batch(ops)
-
-    assert len(results) == 1
-    assert results[0] == [("test", "namespace1"), ("test", "namespace2")]
-
-
-def test_basic_store_ops() -> None:
-    with DuckDBStore.from_conn_string(":memory:") as store:
-        store.setup()
-        namespace = ("test", "documents")
-        item_id = "doc1"
-        item_value = {"title": "Test Document", "content": "Hello, World!"}
-
-        store.put(namespace, item_id, item_value)
-        item = store.get(namespace, item_id)
-
-        assert item
-        assert item.namespace == namespace
-        assert item.key == item_id
-        assert item.value == item_value
-
-        updated_value = {
-            "title": "Updated Test Document",
-            "content": "Hello, LangGraph!",
-        }
-        store.put(namespace, item_id, updated_value)
-        updated_item = store.get(namespace, item_id)
-
-        assert updated_item.value == updated_value
-        assert updated_item.updated_at > item.updated_at
-        different_namespace = ("test", "other_documents")
-        item_in_different_namespace = store.get(different_namespace, item_id)
-        assert item_in_different_namespace is None
-
-        new_item_id = "doc2"
-        new_item_value = {"title": "Another Document", "content": "Greetings!"}
-        store.put(namespace, new_item_id, new_item_value)
-
-        search_results = store.search(["test"], limit=10)
-        items = search_results
-        assert len(items) == 2
-        assert any(item.key == item_id for item in items)
-        assert any(item.key == new_item_id for item in items)
-
-        namespaces = store.list_namespaces(prefix=["test"])
-        assert ("test", "documents") in namespaces
-
-        store.delete(namespace, item_id)
-        store.delete(namespace, new_item_id)
-        deleted_item = store.get(namespace, item_id)
-        assert deleted_item is None
-
-        deleted_item = store.get(namespace, new_item_id)
-        assert deleted_item is None
-
-        empty_search_results = store.search(["test"], limit=10)
-        assert len(empty_search_results) == 0
-
-
-def test_list_namespaces() -> None:
-    with DuckDBStore.from_conn_string(":memory:") as store:
-        store.setup()
-        test_pref = str(uuid.uuid4())
-        test_namespaces = [
-            (test_pref, "test", "documents", "public", test_pref),
-            (test_pref, "test", "documents", "private", test_pref),
-            (test_pref, "test", "images", "public", test_pref),
-            (test_pref, "test", "images", "private", test_pref),
-            (test_pref, "prod", "documents", "public", test_pref),
-            (
-                test_pref,
-                "prod",
-                "documents",
-                "some",
-                "nesting",
-                "public",
-                test_pref,
-            ),
-            (test_pref, "prod", "documents", "private", test_pref),
-        ]
-
-        for namespace in test_namespaces:
-            store.put(namespace, "dummy", {"content": "dummy"})
-
-        prefix_result = store.list_namespaces(prefix=[test_pref, "test"])
-        assert len(prefix_result) == 4
-        assert all([ns[1] == "test" for ns in prefix_result])
-
-        specific_prefix_result = store.list_namespaces(
-            prefix=[test_pref, "test", "documents"]
-        )
-        assert len(specific_prefix_result) == 2
-        assert all([ns[1:3] == ("test", "documents") for ns in specific_prefix_result])
-
-        suffix_result = store.list_namespaces(suffix=["public", test_pref])
-        assert len(suffix_result) == 4
-        assert all(ns[-2] == "public" for ns in suffix_result)
-
-        prefix_suffix_result = store.list_namespaces(
-            prefix=[test_pref, "test"], suffix=["public", test_pref]
-        )
-        assert len(prefix_suffix_result) == 2
-        assert all(
-            ns[1] == "test" and ns[-2] == "public" for ns in prefix_suffix_result
-        )
-
-        wildcard_prefix_result = store.list_namespaces(
-            prefix=[test_pref, "*", "documents"]
-        )
-        assert len(wildcard_prefix_result) == 5
-        assert all(ns[2] == "documents" for ns in wildcard_prefix_result)
-
-        wildcard_suffix_result = store.list_namespaces(
-            suffix=["*", "public", test_pref]
-        )
-        assert len(wildcard_suffix_result) == 4
-        assert all(ns[-2] == "public" for ns in wildcard_suffix_result)
-        wildcard_single = store.list_namespaces(
-            suffix=["some", "*", "public", test_pref]
-        )
-        assert len(wildcard_single) == 1
-        assert wildcard_single[0] == (
-            test_pref,
-            "prod",
-            "documents",
-            "some",
-            "nesting",
-            "public",
-            test_pref,
-        )
-
-        max_depth_result = store.list_namespaces(max_depth=3)
-        assert all([len(ns) <= 3 for ns in max_depth_result])
-
-        max_depth_result = store.list_namespaces(
-            max_depth=4, prefix=[test_pref, "*", "documents"]
-        )
-        assert (
-            len(set(tuple(res) for res in max_depth_result))
-            == len(max_depth_result)
-            == 5
-        )
-
-        limit_result = store.list_namespaces(prefix=[test_pref], limit=3)
-        assert len(limit_result) == 3
-
-        offset_result = store.list_namespaces(prefix=[test_pref], offset=3)
-        assert len(offset_result) == len(test_namespaces) - 3
-
-        empty_prefix_result = store.list_namespaces(prefix=[test_pref])
-        assert len(empty_prefix_result) == len(test_namespaces)
-        assert set(tuple(ns) for ns in empty_prefix_result) == set(
-            tuple(ns) for ns in test_namespaces
-        )
-
-        for namespace in test_namespaces:
-            store.delete(namespace, "dummy")
-
-
-def test_search():
-    with DuckDBStore.from_conn_string(":memory:") as store:
-        store.setup()
-        test_namespaces = [
-            ("test_search", "documents", "user1"),
-            ("test_search", "documents", "user2"),
-            ("test_search", "reports", "department1"),
-            ("test_search", "reports", "department2"),
-        ]
-        test_items = [
-            {"title": "Doc 1", "author": "John Doe", "tags": ["important"]},
-            {"title": "Doc 2", "author": "Jane Smith", "tags": ["draft"]},
-            {"title": "Report A", "author": "John Doe", "tags": ["final"]},
-            {"title": "Report B", "author": "Alice Johnson", "tags": ["draft"]},
-        ]
-
-        for namespace, item in zip(test_namespaces, test_items):
-            store.put(namespace, f"item_{namespace[-1]}", item)
-
-        docs_result = store.search(["test_search", "documents"])
-        assert len(docs_result) == 2
-        assert all(
-            [item.namespace[1] == "documents" for item in docs_result]
-        ), docs_result
-
-        reports_result = store.search(["test_search", "reports"])
-        assert len(reports_result) == 2
-        assert all(item.namespace[1] == "reports" for item in reports_result)
-
-        limited_result = store.search(["test_search"], limit=2)
-        assert len(limited_result) == 2
-        offset_result = store.search(["test_search"])
-        assert len(offset_result) == 4
-
-        offset_result = store.search(["test_search"], offset=2)
-        assert len(offset_result) == 2
-        assert all(item not in limited_result for item in offset_result)
-
-        john_doe_result = store.search(["test_search"], filter={"author": "John Doe"})
-        assert len(john_doe_result) == 2
-        assert all(item.value["author"] == "John Doe" for item in john_doe_result)
-
-        draft_result = store.search(["test_search"], filter={"tags": ["draft"]})
-        assert len(draft_result) == 2
-        assert all("draft" in item.value["tags"] for item in draft_result)
-
-        page1 = store.search(["test_search"], limit=2, offset=0)
-        page2 = store.search(["test_search"], limit=2, offset=2)
-        all_items = page1 + page2
-        assert len(all_items) == 4
-        assert len(set(item.key for item in all_items)) == 4
-
-        for namespace in test_namespaces:
-            store.delete(namespace, f"item_{namespace[-1]}")

libs/checkpoint-duckdb/tests/test_sync.py

@@ -1,111 +0,0 @@
-from typing import Any
-
-import pytest
-from langchain_core.runnables import RunnableConfig
-
-from langgraph.checkpoint.base import (
-    Checkpoint,
-    CheckpointMetadata,
-    create_checkpoint,
-    empty_checkpoint,
-)
-from langgraph.checkpoint.duckdb import DuckDBSaver
-
-
-class TestDuckDBSaver:
-    @pytest.fixture(autouse=True)
-    def setup(self) -> None:
-        # objects for test setup
-        self.config_1: RunnableConfig = {
-            "configurable": {
-                "thread_id": "thread-1",
-                # for backwards compatibility testing
-                "thread_ts": "1",
-                "checkpoint_ns": "",
-            }
-        }
-        self.config_2: RunnableConfig = {
-            "configurable": {
-                "thread_id": "thread-2",
-                "checkpoint_id": "2",
-                "checkpoint_ns": "",
-            }
-        }
-        self.config_3: RunnableConfig = {
-            "configurable": {
-                "thread_id": "thread-2",
-                "checkpoint_id": "2-inner",
-                "checkpoint_ns": "inner",
-            }
-        }
-
-        self.chkpnt_1: Checkpoint = empty_checkpoint()
-        self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1)
-        self.chkpnt_3: Checkpoint = empty_checkpoint()
-
-        self.metadata_1: CheckpointMetadata = {
-            "source": "input",
-            "step": 2,
-            "writes": {},
-            "score": 1,
-        }
-        self.metadata_2: CheckpointMetadata = {
-            "source": "loop",
-            "step": 1,
-            "writes": {"foo": "bar"},
-            "score": None,
-        }
-        self.metadata_3: CheckpointMetadata = {}
-
-    def test_search(self) -> None:
-        with DuckDBSaver.from_conn_string(":memory:") as saver:
-            saver.setup()
-            # save checkpoints
-            saver.put(self.config_1, self.chkpnt_1, self.metadata_1, {})
-            saver.put(self.config_2, self.chkpnt_2, self.metadata_2, {})
-            saver.put(self.config_3, self.chkpnt_3, self.metadata_3, {})
-
-            # call method / assertions
-            query_1 = {"source": "input"}  # search by 1 key
-            query_2 = {
-                "step": 1,
-                "writes": {"foo": "bar"},
-            }  # search by multiple keys
-            query_3: dict[str, Any] = {}  # search by no keys, return all checkpoints
-            query_4 = {"source": "update", "step": 1}  # no match
-
-            search_results_1 = list(saver.list(None, filter=query_1))
-            assert len(search_results_1) == 1
-            assert search_results_1[0].metadata == self.metadata_1
-
-            search_results_2 = list(saver.list(None, filter=query_2))
-            assert len(search_results_2) == 1
-            assert search_results_2[0].metadata == self.metadata_2
-
-            search_results_3 = list(saver.list(None, filter=query_3))
-            assert len(search_results_3) == 3
-
-            search_results_4 = list(saver.list(None, filter=query_4))
-            assert len(search_results_4) == 0
-
-            # search by config (defaults to checkpoints across all namespaces)
-            search_results_5 = list(
-                saver.list({"configurable": {"thread_id": "thread-2"}})
-            )
-            assert len(search_results_5) == 2
-            assert {
-                search_results_5[0].config["configurable"]["checkpoint_ns"],
-                search_results_5[1].config["configurable"]["checkpoint_ns"],
-            } == {"", "inner"}
-
-            # TODO: test before and limit params
-
-    def test_null_chars(self) -> None:
-        with DuckDBSaver.from_conn_string(":memory:") as saver:
-            saver.setup()
-            config = saver.put(self.config_1, self.chkpnt_1, {"my_key": "\x00abc"}, {})
-            assert saver.get_tuple(config).metadata["my_key"] == "abc"  # type: ignore
-            assert (
-                list(saver.list(None, filter={"my_key": "abc"}))[0].metadata["my_key"]  # type: ignore
-                == "abc"
-            )

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

@@ -327,6 +327,7 @@ class PostgresSaver(BasePostgresSaver):
         config: RunnableConfig,
         writes: Sequence[tuple[str, Any]],
         task_id: str,
+        task_path: str = "",
     ) -> None:
         """Store intermediate writes linked to a checkpoint.
 
@@ -350,6 +351,7 @@ class PostgresSaver(BasePostgresSaver):
                     config["configurable"]["checkpoint_ns"],
                     config["configurable"]["checkpoint_id"],
                     task_id,
+                    task_path,
                     writes,
                 ),
             )

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

@@ -285,6 +285,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
         config: RunnableConfig,
         writes: Sequence[tuple[str, Any]],
         task_id: str,
+        task_path: str = "",
     ) -> None:
         """Store intermediate writes linked to a checkpoint asynchronously.
 
@@ -306,6 +307,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
             config["configurable"]["checkpoint_ns"],
             config["configurable"]["checkpoint_id"],
             task_id,
+            task_path,
             writes,
         )
         async with self._cursor(pipeline=True) as cur:
@@ -462,6 +464,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
         config: RunnableConfig,
         writes: Sequence[tuple[str, Any]],
         task_id: str,
+        task_path: str = "",
     ) -> None:
         """Store intermediate writes linked to a checkpoint.
 
@@ -471,9 +474,10 @@ class AsyncPostgresSaver(BasePostgresSaver):
             config (RunnableConfig): Configuration of the related checkpoint.
             writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.
             task_id (str): Identifier for the task creating the writes.
+            task_path (str): Path of the task creating the writes.
         """
         return asyncio.run_coroutine_threadsafe(
-            self.aput_writes(config, writes, task_id), self.loop
+            self.aput_writes(config, writes, task_id, task_path), self.loop
         ).result()
 
 

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

@@ -57,6 +57,9 @@ MIGRATIONS = [
     PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx)
 );""",
     "ALTER TABLE checkpoint_blobs ALTER COLUMN blob DROP not null;",
+    # NOTE: this is a no-op migration to ensure that the versions in the migrations table are correct.
+    # This is necessary due to an empty migration previously added to the list.
+    "SELECT 1;",
     """
     CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoints_thread_id_idx ON checkpoints(thread_id);
     """,
@@ -66,6 +69,7 @@ MIGRATIONS = [
     """
     CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_writes_thread_id_idx ON checkpoint_writes(thread_id);
     """,
+    """ALTER TABLE checkpoint_writes ADD COLUMN task_path TEXT NOT NULL DEFAULT '';""",
 ]
 
 SELECT_SQL = f"""
@@ -94,7 +98,7 @@ select
             and cw.checkpoint_id = checkpoints.checkpoint_id
     ) as pending_writes,
     (
-        select array_agg(array[cw.type::bytea, cw.blob] order by cw.task_id, cw.idx)
+        select array_agg(array[cw.type::bytea, cw.blob] order by cw.task_path, cw.task_id, cw.idx)
         from checkpoint_writes cw
         where cw.thread_id = checkpoints.thread_id
             and cw.checkpoint_ns = checkpoints.checkpoint_ns
@@ -119,8 +123,8 @@ UPSERT_CHECKPOINTS_SQL = """
 """
 
 UPSERT_CHECKPOINT_WRITES_SQL = """
-    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, blob)
-    VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
+    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, task_path, idx, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
     ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO UPDATE SET
         channel = EXCLUDED.channel,
         type = EXCLUDED.type,
@@ -128,8 +132,8 @@ UPSERT_CHECKPOINT_WRITES_SQL = """
 """
 
 INSERT_CHECKPOINT_WRITES_SQL = """
-    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, blob)
-    VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
+    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, task_path, idx, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
     ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING
 """
 
@@ -220,14 +224,16 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
         checkpoint_ns: str,
         checkpoint_id: str,
         task_id: str,
+        task_path: str,
         writes: Sequence[tuple[str, Any]],
-    ) -> list[tuple[str, str, str, str, int, str, str, bytes]]:
+    ) -> list[tuple[str, str, str, str, str, int, str, str, bytes]]:
         return [
             (
                 thread_id,
                 checkpoint_ns,
                 checkpoint_id,
                 task_id,
+                task_path,
                 WRITES_IDX_MAP.get(channel, idx),
                 channel,
                 *self.serde.dumps_typed(value),

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

@@ -74,6 +74,9 @@ MIGRATIONS = [
     """
     CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_writes_thread_id_idx ON checkpoint_writes(thread_id);
     """,
+    """
+    ALTER TABLE checkpoint_writes ADD COLUMN task_path TEXT NOT NULL DEFAULT '';
+    """,
 ]
 
 SELECT_SQL = f"""
@@ -99,7 +102,7 @@ select
             and cw.checkpoint_id = (checkpoint->>'id')
     ) as pending_writes,
     (
-        select array_agg(array[cw.type::bytea, cw.blob] order by cw.task_id, cw.idx)
+        select array_agg(array[cw.type::bytea, cw.blob] order by cw.task_path, cw.task_id, cw.idx)
         from checkpoint_writes cw
         where cw.thread_id = checkpoints.thread_id
             and cw.checkpoint_ns = checkpoints.checkpoint_ns
@@ -125,8 +128,8 @@ UPSERT_CHECKPOINTS_SQL = """
 """
 
 UPSERT_CHECKPOINT_WRITES_SQL = """
-    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, blob)
-    VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
+    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, task_path, idx, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
     ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO UPDATE SET
         channel = EXCLUDED.channel,
         type = EXCLUDED.type,
@@ -134,8 +137,8 @@ UPSERT_CHECKPOINT_WRITES_SQL = """
 """
 
 INSERT_CHECKPOINT_WRITES_SQL = """
-    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, blob)
-    VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
+    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, task_path, idx, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
     ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING
 """
 
@@ -430,6 +433,7 @@ class ShallowPostgresSaver(BasePostgresSaver):
         config: RunnableConfig,
         writes: Sequence[tuple[str, Any]],
         task_id: str,
+        task_path: str = "",
     ) -> None:
         """Store intermediate writes linked to a checkpoint.
 
@@ -453,6 +457,7 @@ class ShallowPostgresSaver(BasePostgresSaver):
                     config["configurable"]["checkpoint_ns"],
                     config["configurable"]["checkpoint_id"],
                     task_id,
+                    task_path,
                     writes,
                 ),
             )
@@ -747,6 +752,7 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
         config: RunnableConfig,
         writes: Sequence[tuple[str, Any]],
         task_id: str,
+        task_path: str = "",
     ) -> None:
         """Store intermediate writes linked to a checkpoint asynchronously.
 
@@ -768,6 +774,7 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
             config["configurable"]["checkpoint_ns"],
             config["configurable"]["checkpoint_id"],
             task_id,
+            task_path,
             writes,
         )
         async with self._cursor(pipeline=True) as cur:
@@ -903,6 +910,7 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
         config: RunnableConfig,
         writes: Sequence[tuple[str, Any]],
         task_id: str,
+        task_path: str = "",
     ) -> None:
         """Store intermediate writes linked to a checkpoint.
 
@@ -912,7 +920,8 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
             config (RunnableConfig): Configuration of the related checkpoint.
             writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.
             task_id (str): Identifier for the task creating the writes.
+            task_path (str): Path of the task creating the writes.
         """
         return asyncio.run_coroutine_threadsafe(
-            self.aput_writes(config, writes, task_id), self.loop
+            self.aput_writes(config, writes, task_id, task_path), self.loop
         ).result()

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

@@ -39,14 +39,14 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
     """Asynchronous Postgres-backed store with optional vector search using pgvector.
 
     !!! example "Examples"
-        Basic setup and key-value storage:
+        Basic setup and usage:
         ```python
         from langgraph.store.postgres import AsyncPostgresStore
 
-        async with AsyncPostgresStore.from_conn_string(
-            "postgresql://user:pass@localhost:5432/dbname"
-        ) as store:
-            await store.setup()
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
+        async with AsyncPostgresStore.from_conn_string(conn_string) as store:
+            await store.setup()  # Run migrations. Done once
 
             # Store and retrieve data
             await store.aput(("users", "123"), "prefs", {"theme": "dark"})
@@ -58,38 +58,41 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
         from langchain.embeddings import init_embeddings
         from langgraph.store.postgres import AsyncPostgresStore
 
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
         async with AsyncPostgresStore.from_conn_string(
-            "postgresql://user:pass@localhost:5432/dbname",
+            conn_string,
             index={
                 "dims": 1536,
                 "embed": init_embeddings("openai:text-embedding-3-small"),
                 "fields": ["text"]  # specify which fields to embed. Default is the whole serialized value
             }
         ) as store:
-            await store.setup()  # Do this once to run migrations
+            await store.setup()  # Run migrations. Done once
 
             # Store documents
             await store.aput(("docs",), "doc1", {"text": "Python tutorial"})
             await store.aput(("docs",), "doc2", {"text": "TypeScript guide"})
-            # Don't index the following
-            await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False)
+            await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False)  # don't index
 
             # Search by similarity
-            results = await store.asearch(("docs",), query="python programming")
+            results = await store.asearch(("docs",), "programming guides", limit=2)
         ```
 
         Using connection pooling for better performance:
         ```python
         from langgraph.store.postgres import AsyncPostgresStore, PoolConfig
 
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
         async with AsyncPostgresStore.from_conn_string(
-            "postgresql://user:pass@localhost:5432/dbname",
+            conn_string,
             pool_config=PoolConfig(
                 min_size=5,
                 max_size=20
             )
         ) as store:
-            await store.setup()
+            await store.setup()  # Run migrations. Done once
             # Use store with connection pooling...
         ```
 
@@ -102,7 +105,7 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
     Note:
         Semantic search is disabled by default. You can enable it by providing an `index` configuration
         when creating the store. Without this configuration, all `index` arguments passed to
-        `put` or `aput`will have no effect.
+        `put` or `aput` will have no effect.
     """
 
     __slots__ = (

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

@@ -536,18 +536,35 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
     """Postgres-backed store with optional vector search using pgvector.
 
     !!! example "Examples"
-        Basic setup and key-value storage:
+        Basic setup and usage:
+        ```python
+        from langgraph.store.postgres import PostgresStore
+        from psycopg import Connection
+
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
+        # Using direct connection
+        with Connection.connect(conn_string) as conn:
+            store = PostgresStore(conn)
+            store.setup() # Run migrations. Done once
+
+            # Store and retrieve data
+            store.put(("users", "123"), "prefs", {"theme": "dark"})
+            item = store.get(("users", "123"), "prefs")
+        ```
+
+        Or using the convenient from_conn_string helper:
         ```python
         from langgraph.store.postgres import PostgresStore
 
-        store = PostgresStore(
-            connection_string="postgresql://user:pass@localhost:5432/dbname"
-        )
-        store.setup()
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
 
-        # Store and retrieve data
-        store.put(("users", "123"), "prefs", {"theme": "dark"})
-        item = store.get(("users", "123"), "prefs")
+        with PostgresStore.from_conn_string(conn_string) as store:
+            store.setup()
+
+            # Store and retrieve data
+            store.put(("users", "123"), "prefs", {"theme": "dark"})
+            item = store.get(("users", "123"), "prefs")
         ```
 
         Vector search using LangChain embeddings:
@@ -555,23 +572,25 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
         from langchain.embeddings import init_embeddings
         from langgraph.store.postgres import PostgresStore
 
-        store = PostgresStore(
-            connection_string="postgresql://user:pass@localhost:5432/dbname",
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
+        with PostgresStore.from_conn_string(
+            conn_string,
             index={
                 "dims": 1536,
                 "embed": init_embeddings("openai:text-embedding-3-small"),
                 "fields": ["text"]  # specify which fields to embed. Default is the whole serialized value
             }
-        )
-        store.setup() # Do this once to run migrations
+        ) as store:
+            store.setup() # Do this once to run migrations
 
-        # Store documents
-        store.put(("docs",), "doc1", {"text": "Python tutorial"})
-        store.put(("docs",), "doc2", {"text": "TypeScript guide"})
-        store.put(("docs",), "doc2", {"text": "Other guide"}, index=False) # don't index
+            # Store documents
+            store.put(("docs",), "doc1", {"text": "Python tutorial"})
+            store.put(("docs",), "doc2", {"text": "TypeScript guide"})
+            store.put(("docs",), "doc2", {"text": "Other guide"}, index=False) # don't index
 
-        # Search by similarity
-        results = store.search(("docs",), query="python programming")
+            # Search by similarity
+            results = store.search(("docs",), "programming guides", limit=2)
         ```
 
     Note:

libs/checkpoint-postgres/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -6,6 +6,7 @@ version = "0.7.0"
 description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
@@ -17,6 +18,7 @@ version = "4.7.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
+groups = ["main", "dev"]
 files = [
     {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"},
     {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"},
@@ -39,6 +41,7 @@ version = "2024.8.30"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
 files = [
     {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
     {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
@@ -50,6 +53,7 @@ version = "3.4.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
+groups = ["main", "dev"]
 files = [
     {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:4f9fc98dad6c2eaa32fc3af1417d95b5e3d08aff968df0cd320066def971f9a6"},
     {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0de7b687289d3c1b3e8660d0741874abe7888100efe14bd0f9fd7141bcbda92b"},
@@ -164,6 +168,7 @@ version = "2.3.0"
 description = "Codespell"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "codespell-2.3.0-py3-none-any.whl", hash = "sha256:a9c7cef2501c9cfede2110fd6d4e5e62296920efe9abfb84648df866e47f58d1"},
     {file = "codespell-2.3.0.tar.gz", hash = "sha256:360c7d10f75e65f67bad720af7007e1060a5d395670ec11a7ed1fed9dd17471f"},
@@ -181,6 +186,7 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["dev"]
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
@@ -192,6 +198,7 @@ version = "0.6.2"
 description = "Pythonic argument parser, that will make you smile"
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
     {file = "docopt-0.6.2.tar.gz", hash = "sha256:49b3a825280bd66b3aa83585ef59c4a8c82f2c8a522dbe754a8bc8d08c85c491"},
 ]
@@ -202,6 +209,8 @@ version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
+markers = "python_version < \"3.11\""
 files = [
     {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
     {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
@@ -216,6 +225,7 @@ version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
     {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
@@ -227,6 +237,7 @@ version = "1.0.7"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "httpcore-1.0.7-py3-none-any.whl", hash = "sha256:a3fff8f43dc260d5bd363d9f9cf1830fa3a458b332856f34282de498ed420edd"},
     {file = "httpcore-1.0.7.tar.gz", hash = "sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c"},
@@ -248,6 +259,7 @@ version = "0.28.0"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "httpx-0.28.0-py3-none-any.whl", hash = "sha256:dc0b419a0cfeb6e8b34e85167c0da2671206f5095f1baa9663d23bcfd6b535fc"},
     {file = "httpx-0.28.0.tar.gz", hash = "sha256:0858d3bab51ba7e386637f22a61d8ccddaeec5f3fe4209da3a6168dbb91573e0"},
@@ -272,6 +284,7 @@ version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
 files = [
     {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
     {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
@@ -286,6 +299,7 @@ version = "2.0.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
@@ -297,6 +311,7 @@ version = "1.33"
 description = "Apply JSON-Patches (RFC 6902)"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+groups = ["main", "dev"]
 files = [
     {file = "jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade"},
     {file = "jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c"},
@@ -311,6 +326,7 @@ version = "3.0.0"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
     {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
@@ -322,6 +338,7 @@ version = "0.3.21"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
+groups = ["main", "dev"]
 files = [
     {file = "langchain_core-0.3.21-py3-none-any.whl", hash = "sha256:7e723dff80946a1198976c6876fea8326dc82566ef9bcb5f8d9188f738733665"},
     {file = "langchain_core-0.3.21.tar.gz", hash = "sha256:561b52b258ffa50a9fb11d7a1940ebfd915654d1ec95b35e81dfd5ee84143411"},
@@ -341,10 +358,11 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.8"
+version = "2.0.10"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
+groups = ["main", "dev"]
 files = []
 develop = true
 
@@ -362,6 +380,7 @@ version = "0.1.147"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
+groups = ["main", "dev"]
 files = [
     {file = "langsmith-0.1.147-py3-none-any.whl", hash = "sha256:7166fc23b965ccf839d64945a78e9f1157757add228b086141eb03a60d699a15"},
     {file = "langsmith-0.1.147.tar.gz", hash = "sha256:2e933220318a4e73034657103b3b1a3a6109cc5db3566a7e8e03be8d6d7def7a"},
@@ -386,6 +405,7 @@ version = "1.1.0"
 description = "MessagePack serializer"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7ad442d527a7e358a469faf43fda45aaf4ac3249c8310a82f0ccff9164e5dccd"},
     {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:74bed8f63f8f14d75eec75cf3d04ad581da6b914001b474a5d3cd3372c8cc27d"},
@@ -459,6 +479,7 @@ version = "1.13.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "mypy-1.13.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6607e0f1dd1fb7f0aca14d936d13fd19eba5e17e1cd2a14f808fa5f8f6d8f60a"},
     {file = "mypy-1.13.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8a21be69bd26fa81b1f80a61ee7ab05b076c674d9b18fb56239d72e21d9f4c80"},
@@ -512,6 +533,7 @@ version = "1.0.0"
 description = "Type system extensions for programs checked with the mypy type checker."
 optional = false
 python-versions = ">=3.5"
+groups = ["dev"]
 files = [
     {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"},
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
@@ -523,6 +545,7 @@ version = "3.10.12"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "orjson-3.10.12-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:ece01a7ec71d9940cc654c482907a6b65df27251255097629d0dea781f255c6d"},
     {file = "orjson-3.10.12-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c34ec9aebc04f11f4b978dd6caf697a2df2dd9b47d35aa4cc606cabcb9df69d7"},
@@ -607,6 +630,7 @@ version = "24.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759"},
     {file = "packaging-24.2.tar.gz", hash = "sha256:c228a6dc5e932d346bc5739379109d49e8853dd8223571c7c5b55260edc0b97f"},
@@ -618,6 +642,7 @@ version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"},
     {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"},
@@ -633,6 +658,7 @@ version = "3.2.3"
 description = "PostgreSQL database adapter for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "psycopg-3.2.3-py3-none-any.whl", hash = "sha256:644d3973fe26908c73d4be746074f6e5224b03c1101d302d9a53bf565ad64907"},
     {file = "psycopg-3.2.3.tar.gz", hash = "sha256:a5764f67c27bec8bfac85764d23c534af2c27b893550377e37ce59c12aac47a2"},
@@ -657,6 +683,8 @@ version = "3.2.3"
 description = "PostgreSQL database adapter for Python -- C optimisation distribution"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "implementation_name != \"pypy\""
 files = [
     {file = "psycopg_binary-3.2.3-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:965455eac8547f32b3181d5ec9ad8b9be500c10fe06193543efaaebe3e4ce70c"},
     {file = "psycopg_binary-3.2.3-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:71adcc8bc80a65b776510bc39992edf942ace35b153ed7a9c6c573a6849ce308"},
@@ -730,6 +758,7 @@ version = "3.2.4"
 description = "Connection Pool for Psycopg"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "psycopg_pool-3.2.4-py3-none-any.whl", hash = "sha256:f6a22cff0f21f06d72fb2f5cb48c618946777c49385358e0c88d062c59cbd224"},
     {file = "psycopg_pool-3.2.4.tar.gz", hash = "sha256:61774b5bbf23e8d22bedc7504707135aaf744679f8ef9b3fe29942920746a6ed"},
@@ -744,6 +773,7 @@ version = "2.10.3"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "pydantic-2.10.3-py3-none-any.whl", hash = "sha256:be04d85bbc7b65651c5f8e6b9976ed9c6f41782a55524cef079a34a0bb82144d"},
     {file = "pydantic-2.10.3.tar.gz", hash = "sha256:cb5ac360ce894ceacd69c403187900a02c4b20b693a9dd1d643e1effab9eadf9"},
@@ -764,6 +794,7 @@ version = "2.27.1"
 description = "Core functionality for Pydantic validation and serialization"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "pydantic_core-2.27.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:71a5e35c75c021aaf400ac048dacc855f000bdfed91614b4a726f7432f1f3d6a"},
     {file = "pydantic_core-2.27.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f82d068a2d6ecfc6e054726080af69a6764a10015467d7d7b9f66d6ed5afa23b"},
@@ -876,6 +907,7 @@ version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
     {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
@@ -898,6 +930,7 @@ version = "0.21.2"
 description = "Pytest support for asyncio"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "pytest_asyncio-0.21.2-py3-none-any.whl", hash = "sha256:ab664c88bb7998f711d8039cacd4884da6430886ae8bbd4eded552ed2004f16b"},
     {file = "pytest_asyncio-0.21.2.tar.gz", hash = "sha256:d67738fc232b94b326b9d060750beb16e0074210b98dd8b58a5239fa2a154f45"},
@@ -916,6 +949,7 @@ version = "3.14.0"
 description = "Thin-wrapper around the mock package for easier use with pytest"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "pytest-mock-3.14.0.tar.gz", hash = "sha256:2719255a1efeceadbc056d6bf3df3d1c5015530fb40cf347c0f9afac88410bd0"},
     {file = "pytest_mock-3.14.0-py3-none-any.whl", hash = "sha256:0b72c38033392a5f4621342fe11e9219ac11ec9d375f8e2a0c164539e0d70f6f"},
@@ -933,6 +967,7 @@ version = "4.2.0"
 description = "Local continuous test runner with pytest and watchdog."
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
     {file = "pytest-watch-4.2.0.tar.gz", hash = "sha256:06136f03d5b361718b8d0d234042f7b2f203910d8568f63df2f866b547b3d4b9"},
 ]
@@ -949,6 +984,7 @@ version = "6.0.2"
 description = "YAML parser and emitter for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086"},
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:29717114e51c84ddfba879543fb232a6ed60086602313ca38cce623c1d62cfbf"},
@@ -1011,6 +1047,7 @@ version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
     {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
@@ -1032,6 +1069,7 @@ version = "1.0.0"
 description = "A utility belt for advanced users of python-requests"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["main", "dev"]
 files = [
     {file = "requests-toolbelt-1.0.0.tar.gz", hash = "sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6"},
     {file = "requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06"},
@@ -1046,6 +1084,7 @@ version = "0.6.9"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "ruff-0.6.9-py3-none-linux_armv6l.whl", hash = "sha256:064df58d84ccc0ac0fcd63bc3090b251d90e2a372558c0f057c3f75ed73e1ccd"},
     {file = "ruff-0.6.9-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:140d4b5c9f5fc7a7b074908a78ab8d384dd7f6510402267bc76c37195c02a7ec"},
@@ -1073,6 +1112,7 @@ version = "1.3.1"
 description = "Sniff out which async library your code is running under"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
     {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
@@ -1084,6 +1124,7 @@ version = "9.0.0"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"},
     {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"},
@@ -1099,6 +1140,8 @@ version = "2.2.1"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version < \"3.11\""
 files = [
     {file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
     {file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"},
@@ -1140,6 +1183,7 @@ version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
@@ -1151,6 +1195,8 @@ version = "2024.2"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
+groups = ["main", "dev"]
+markers = "sys_platform == \"win32\""
 files = [
     {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
     {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
@@ -1162,6 +1208,7 @@ version = "2.2.3"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
     {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
@@ -1179,6 +1226,7 @@ version = "6.0.0"
 description = "Filesystem events monitoring"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
     {file = "watchdog-6.0.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d1cdb490583ebd691c012b3d6dae011000fe42edb7a82ece80965b42abd61f26"},
     {file = "watchdog-6.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bc64ab3bdb6a04d69d4023b29422170b74681784ffb9463ed4870cf2f3e66112"},
@@ -1216,6 +1264,6 @@ files = [
 watchmedo = ["PyYAML (>=3.10)"]
 
 [metadata]
-lock-version = "2.0"
+lock-version = "2.1"
 python-versions = "^3.9.0,<4.0"
-content-hash = "d64fe96797a79103d952c13d4dc0a0296c468b6615b4872aa01cb34479ca4104"
+content-hash = "61326e4e81a4e8854763a119f39d4f5d0a54cee868b4dbc91b95ce7d2cebba5b"

libs/checkpoint-postgres/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.11"
+version = "2.0.13"
 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 = "^2.0.7"
+langgraph-checkpoint = "^2.0.10"
 orjson = ">=3.10.1"
 psycopg = "^3.2.0"
 psycopg-pool = "^3.2.0"

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

@@ -424,6 +424,7 @@ class SqliteSaver(BaseCheckpointSaver[str]):
         config: RunnableConfig,
         writes: Sequence[Tuple[str, Any]],
         task_id: str,
+        task_path: str = "",
     ) -> None:
         """Store intermediate writes linked to a checkpoint.
 
@@ -433,6 +434,7 @@ class SqliteSaver(BaseCheckpointSaver[str]):
             config (RunnableConfig): Configuration of the related checkpoint.
             writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.
             task_id (str): Identifier for the task creating the writes.
+            task_path (str): Path of the task creating the writes.
         """
         query = (
             "INSERT OR REPLACE INTO writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, value) VALUES (?, ?, ?, ?, ?, ?, ?, ?)"

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

@@ -1,17 +1,8 @@
 import asyncio
 import random
+from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import asynccontextmanager
-from typing import (
-    Any,
-    AsyncIterator,
-    Callable,
-    Dict,
-    Iterator,
-    Optional,
-    Sequence,
-    Tuple,
-    TypeVar,
-)
+from typing import Any, Callable, Optional, TypeVar
 
 import aiosqlite
 from langchain_core.runnables import RunnableConfig
@@ -173,7 +164,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         self,
         config: Optional[RunnableConfig],
         *,
-        filter: Optional[Dict[str, Any]] = None,
+        filter: Optional[dict[str, Any]] = None,
         before: Optional[RunnableConfig] = None,
         limit: Optional[int] = None,
     ) -> Iterator[CheckpointTuple]:
@@ -207,7 +198,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         while True:
             try:
                 yield asyncio.run_coroutine_threadsafe(
-                    anext(aiter_),
+                    anext(aiter_),  # noqa: F821
                     self.loop,
                 ).result()
             except StopAsyncIteration:
@@ -239,10 +230,14 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         ).result()
 
     def put_writes(
-        self, config: RunnableConfig, writes: Sequence[Tuple[str, Any]], task_id: str
+        self,
+        config: RunnableConfig,
+        writes: Sequence[tuple[str, Any]],
+        task_id: str,
+        task_path: str = "",
     ) -> None:
         return asyncio.run_coroutine_threadsafe(
-            self.aput_writes(config, writes, task_id), self.loop
+            self.aput_writes(config, writes, task_id, task_path), self.loop
         ).result()
 
     async def setup(self) -> None:
@@ -372,7 +367,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         self,
         config: Optional[RunnableConfig],
         *,
-        filter: Optional[Dict[str, Any]] = None,
+        filter: Optional[dict[str, Any]] = None,
         before: Optional[RunnableConfig] = None,
         limit: Optional[int] = None,
     ) -> AsyncIterator[CheckpointTuple]:
@@ -398,9 +393,11 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         ORDER BY checkpoint_id DESC"""
         if limit:
             query += f" LIMIT {limit}"
-        async with self.lock, self.conn.execute(
-            query, params
-        ) as cur, self.conn.cursor() as wcur:
+        async with (
+            self.lock,
+            self.conn.execute(query, params) as cur,
+            self.conn.cursor() as wcur,
+        ):
             async for (
                 thread_id,
                 checkpoint_ns,
@@ -467,16 +464,19 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         checkpoint_ns = config["configurable"]["checkpoint_ns"]
         type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)
         serialized_metadata = self.jsonplus_serde.dumps(metadata)
-        async with self.lock, self.conn.execute(
-            "INSERT OR REPLACE INTO checkpoints (thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata) VALUES (?, ?, ?, ?, ?, ?, ?)",
-            (
-                str(config["configurable"]["thread_id"]),
-                checkpoint_ns,
-                checkpoint["id"],
-                config["configurable"].get("checkpoint_id"),
-                type_,
-                serialized_checkpoint,
-                serialized_metadata,
+        async with (
+            self.lock,
+            self.conn.execute(
+                "INSERT OR REPLACE INTO checkpoints (thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata) VALUES (?, ?, ?, ?, ?, ?, ?)",
+                (
+                    str(config["configurable"]["thread_id"]),
+                    checkpoint_ns,
+                    checkpoint["id"],
+                    config["configurable"].get("checkpoint_id"),
+                    type_,
+                    serialized_checkpoint,
+                    serialized_metadata,
+                ),
             ),
         ):
             await self.conn.commit()
@@ -491,8 +491,9 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
     async def aput_writes(
         self,
         config: RunnableConfig,
-        writes: Sequence[Tuple[str, Any]],
+        writes: Sequence[tuple[str, Any]],
         task_id: str,
+        task_path: str = "",
     ) -> None:
         """Store intermediate writes linked to a checkpoint asynchronously.
 
@@ -502,6 +503,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
             config (RunnableConfig): Configuration of the related checkpoint.
             writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.
             task_id (str): Identifier for the task creating the writes.
+            task_path (str): Path of the task creating the writes.
         """
         query = (
             "INSERT OR REPLACE INTO writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, value) VALUES (?, ?, ?, ?, ?, ?, ?, ?)"

libs/checkpoint-sqlite/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.
 
 [[package]]
 name = "aiosqlite"
@@ -6,6 +6,7 @@ version = "0.20.0"
 description = "asyncio bridge to the standard sqlite3 module"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "aiosqlite-0.20.0-py3-none-any.whl", hash = "sha256:36a1deaca0cac40ebe32aac9977a6e2bbc7f5189f23f4a54d5908986729e5bd6"},
     {file = "aiosqlite-0.20.0.tar.gz", hash = "sha256:6d35c8c256637f4672f843c31021464090805bf925385ac39473fb16eaaca3d7"},
@@ -24,6 +25,7 @@ version = "0.7.0"
 description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
@@ -35,6 +37,7 @@ version = "4.4.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
     {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
@@ -57,6 +60,7 @@ version = "2024.7.4"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
 files = [
     {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
     {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
@@ -68,6 +72,7 @@ version = "3.3.2"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
+groups = ["main", "dev"]
 files = [
     {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
     {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
@@ -167,6 +172,7 @@ version = "2.3.0"
 description = "Codespell"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "codespell-2.3.0-py3-none-any.whl", hash = "sha256:a9c7cef2501c9cfede2110fd6d4e5e62296920efe9abfb84648df866e47f58d1"},
     {file = "codespell-2.3.0.tar.gz", hash = "sha256:360c7d10f75e65f67bad720af7007e1060a5d395670ec11a7ed1fed9dd17471f"},
@@ -184,6 +190,8 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["dev"]
+markers = "sys_platform == \"win32\""
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
@@ -195,6 +203,8 @@ version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
+markers = "python_version < \"3.11\""
 files = [
     {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
     {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
@@ -209,6 +219,7 @@ version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
     {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
@@ -220,6 +231,7 @@ version = "1.0.5"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
     {file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
@@ -241,6 +253,7 @@ version = "0.27.2"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "httpx-0.27.2-py3-none-any.whl", hash = "sha256:7bb2708e112d8fdd7829cd4243970f0c223274051cb35ee80c03301ee29a3df0"},
     {file = "httpx-0.27.2.tar.gz", hash = "sha256:f7c2be1d2f3c3c3160d441802406b206c2b76f5947b11115e6df10c6c65e66c2"},
@@ -266,6 +279,7 @@ version = "3.7"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
+groups = ["main", "dev"]
 files = [
     {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
     {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
@@ -277,6 +291,7 @@ version = "2.0.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
@@ -288,6 +303,7 @@ version = "1.33"
 description = "Apply JSON-Patches (RFC 6902)"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+groups = ["main", "dev"]
 files = [
     {file = "jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade"},
     {file = "jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c"},
@@ -302,6 +318,7 @@ version = "3.0.0"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
     {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
@@ -313,6 +330,7 @@ version = "0.3.0"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
+groups = ["main", "dev"]
 files = [
     {file = "langchain_core-0.3.0-py3-none-any.whl", hash = "sha256:bee6dae2366d037ef0c5b87401fed14b5497cad26f97724e8c9ca7bc9239e847"},
     {file = "langchain_core-0.3.0.tar.gz", hash = "sha256:1249149ea3ba24c9c761011483c14091573a5eb1a773aa0db9c8ad155dd4a69d"},
@@ -332,10 +350,11 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.2"
+version = "2.0.10"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
+groups = ["main", "dev"]
 files = []
 develop = true
 
@@ -353,6 +372,7 @@ version = "0.1.120"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
+groups = ["main", "dev"]
 files = [
     {file = "langsmith-0.1.120-py3-none-any.whl", hash = "sha256:54d2785e301646c0988e0a69ebe4d976488c87b41928b358cb153b6ddd8db62b"},
     {file = "langsmith-0.1.120.tar.gz", hash = "sha256:25499ca187b41bd89d784b272b97a8d76f60e0e21bdf20336e8a2aa6a9b23ac9"},
@@ -373,6 +393,7 @@ version = "1.1.0"
 description = "MessagePack serializer"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7ad442d527a7e358a469faf43fda45aaf4ac3249c8310a82f0ccff9164e5dccd"},
     {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:74bed8f63f8f14d75eec75cf3d04ad581da6b914001b474a5d3cd3372c8cc27d"},
@@ -446,6 +467,7 @@ version = "1.11.2"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "mypy-1.11.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d42a6dd818ffce7be66cce644f1dff482f1d97c53ca70908dff0b9ddc120b77a"},
     {file = "mypy-1.11.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:801780c56d1cdb896eacd5619a83e427ce436d86a3bdf9112527f24a66618fef"},
@@ -493,6 +515,7 @@ version = "1.0.0"
 description = "Type system extensions for programs checked with the mypy type checker."
 optional = false
 python-versions = ">=3.5"
+groups = ["dev"]
 files = [
     {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"},
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
@@ -504,6 +527,7 @@ version = "3.10.6"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "orjson-3.10.6-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:fb0ee33124db6eaa517d00890fc1a55c3bfe1cf78ba4a8899d71a06f2d6ff5c7"},
     {file = "orjson-3.10.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9c1c4b53b24a4c06547ce43e5fee6ec4e0d8fe2d597f4647fc033fd205707365"},
@@ -566,6 +590,7 @@ version = "24.1"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "packaging-24.1-py3-none-any.whl", hash = "sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124"},
     {file = "packaging-24.1.tar.gz", hash = "sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002"},
@@ -577,6 +602,7 @@ version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"},
     {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"},
@@ -592,6 +618,7 @@ version = "2.8.2"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "pydantic-2.8.2-py3-none-any.whl", hash = "sha256:73ee9fddd406dc318b885c7a2eab8a6472b68b8fb5ba8150949fc3db939f23c8"},
     {file = "pydantic-2.8.2.tar.gz", hash = "sha256:6f62c13d067b0755ad1c21a34bdd06c0c12625a22b0fc09c6b149816604f7c2a"},
@@ -614,6 +641,7 @@ version = "2.20.1"
 description = "Core functionality for Pydantic validation and serialization"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "pydantic_core-2.20.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:3acae97ffd19bf091c72df4d726d552c473f3576409b2a7ca36b2f535ffff4a3"},
     {file = "pydantic_core-2.20.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:41f4c96227a67a013e7de5ff8f20fb496ce573893b7f4f2707d065907bffdbd6"},
@@ -715,6 +743,7 @@ version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
     {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
@@ -737,6 +766,7 @@ version = "0.21.2"
 description = "Pytest support for asyncio"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "pytest_asyncio-0.21.2-py3-none-any.whl", hash = "sha256:ab664c88bb7998f711d8039cacd4884da6430886ae8bbd4eded552ed2004f16b"},
     {file = "pytest_asyncio-0.21.2.tar.gz", hash = "sha256:d67738fc232b94b326b9d060750beb16e0074210b98dd8b58a5239fa2a154f45"},
@@ -755,6 +785,7 @@ version = "3.14.0"
 description = "Thin-wrapper around the mock package for easier use with pytest"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "pytest-mock-3.14.0.tar.gz", hash = "sha256:2719255a1efeceadbc056d6bf3df3d1c5015530fb40cf347c0f9afac88410bd0"},
     {file = "pytest_mock-3.14.0-py3-none-any.whl", hash = "sha256:0b72c38033392a5f4621342fe11e9219ac11ec9d375f8e2a0c164539e0d70f6f"},
@@ -772,6 +803,7 @@ version = "0.4.2"
 description = "Automatically rerun your tests on file modifications"
 optional = false
 python-versions = "<4.0.0,>=3.7.0"
+groups = ["dev"]
 files = [
     {file = "pytest_watcher-0.4.2-py3-none-any.whl", hash = "sha256:a43949ba67dd8d7e1fd0de5eea44a999081f0aec9f93b4e744264b4c6a3d9bbe"},
     {file = "pytest_watcher-0.4.2.tar.gz", hash = "sha256:7b292f025ca19617cd7567c228c6187b5087f2da9e4d2cf6e144e5764a0471b0"},
@@ -787,6 +819,7 @@ version = "6.0.1"
 description = "YAML parser and emitter for Python"
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
 files = [
     {file = "PyYAML-6.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d858aa552c999bc8a8d57426ed01e40bef403cd8ccdd0fc5f6f04a00414cac2a"},
     {file = "PyYAML-6.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fd66fc5d0da6d9815ba2cebeb4205f95818ff4b79c3ebe268e75d961704af52f"},
@@ -847,6 +880,7 @@ version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
     {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
@@ -868,6 +902,7 @@ version = "0.6.2"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "ruff-0.6.2-py3-none-linux_armv6l.whl", hash = "sha256:5c8cbc6252deb3ea840ad6a20b0f8583caab0c5ef4f9cca21adc5a92b8f79f3c"},
     {file = "ruff-0.6.2-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:17002fe241e76544448a8e1e6118abecbe8cd10cf68fde635dad480dba594570"},
@@ -895,6 +930,7 @@ version = "1.3.1"
 description = "Sniff out which async library your code is running under"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
     {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
@@ -906,6 +942,7 @@ version = "8.5.0"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "tenacity-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687"},
     {file = "tenacity-8.5.0.tar.gz", hash = "sha256:8bc6c0c8a09b31e6cad13c47afbed1a567518250a9a171418582ed8d9c20ca78"},
@@ -921,6 +958,8 @@ version = "2.0.1"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version < \"3.11\""
 files = [
     {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
     {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
@@ -932,6 +971,7 @@ version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
@@ -943,6 +983,7 @@ version = "2.2.2"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
     {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
@@ -960,6 +1001,7 @@ version = "4.0.1"
 description = "Filesystem events monitoring"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "watchdog-4.0.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:da2dfdaa8006eb6a71051795856bedd97e5b03e57da96f98e375682c48850645"},
     {file = "watchdog-4.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e93f451f2dfa433d97765ca2634628b789b49ba8b504fdde5837cdcf25fdb53b"},
@@ -999,6 +1041,6 @@ files = [
 watchmedo = ["PyYAML (>=3.10)"]
 
 [metadata]
-lock-version = "2.0"
+lock-version = "2.1"
 python-versions = "^3.9.0"
-content-hash = "927b49b9ba72a301980237d7adc2e73cdacfbe127a174c7488136a9af9372796"
+content-hash = "03c697eae6f550f3c7e29f1d61f4c409dabe04ae8d43281728e549174d2fc670"

libs/checkpoint-sqlite/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint-sqlite"
-version = "2.0.2"
+version = "2.0.3"
 description = "Library with a SQLite implementation of LangGraph checkpoint saver."
 authors = []
 license = "MIT"
@@ -10,7 +10,7 @@ packages = [{ include = "langgraph" }]
 
 [tool.poetry.dependencies]
 python = "^3.9.0"
-langgraph-checkpoint = "^2.0.2"
+langgraph-checkpoint = "^2.0.10"
 aiosqlite = "^0.20.0"
 
 [tool.poetry.group.dev.dependencies]

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

@@ -1,16 +1,13 @@
+from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
 from datetime import datetime, timezone
-from typing import (
+from typing import (  # noqa: UP035
     Any,
-    AsyncIterator,
     Dict,
     Generic,
-    Iterator,
     List,
     Literal,
-    Mapping,
     NamedTuple,
     Optional,
-    Sequence,
     Tuple,
     TypedDict,
     TypeVar,
@@ -305,6 +302,7 @@ class BaseCheckpointSaver(Generic[V]):
         config: RunnableConfig,
         writes: Sequence[Tuple[str, Any]],
         task_id: str,
+        task_path: str = "",
     ) -> None:
         """Store intermediate writes linked to a checkpoint.
 
@@ -312,6 +310,7 @@ class BaseCheckpointSaver(Generic[V]):
             config (RunnableConfig): Configuration of the related checkpoint.
             writes (List[Tuple[str, Any]]): List of writes to store.
             task_id (str): Identifier for the task creating the writes.
+            task_path (str): Path of the task creating the writes.
 
         Raises:
             NotImplementedError: Implement this method in your custom checkpoint saver.
@@ -397,6 +396,7 @@ class BaseCheckpointSaver(Generic[V]):
         config: RunnableConfig,
         writes: Sequence[Tuple[str, Any]],
         task_id: str,
+        task_path: str = "",
     ) -> None:
         """Asynchronously store intermediate writes linked to a checkpoint.
 
@@ -404,6 +404,7 @@ class BaseCheckpointSaver(Generic[V]):
             config (RunnableConfig): Configuration of the related checkpoint.
             writes (List[Tuple[str, Any]]): List of writes to store.
             task_id (str): Identifier for the task creating the writes.
+            task_path (str): Path of the task creating the writes.
 
         Raises:
             NotImplementedError: Implement this method in your custom checkpoint saver.

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

@@ -4,9 +4,10 @@ import pickle
 import random
 import shutil
 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, AsyncIterator, Dict, Iterator, Optional, Sequence, Tuple, Type
+from typing import Any, Optional
 
 from langchain_core.runnables import RunnableConfig
 
@@ -65,14 +66,15 @@ class MemorySaver(
         ],
     ]
     writes: defaultdict[
-        tuple[str, str, str], dict[tuple[str, int], tuple[str, str, tuple[str, bytes]]]
+        tuple[str, str, str],
+        dict[tuple[str, int], tuple[str, str, tuple[str, bytes], str]],
     ]
 
     def __init__(
         self,
         *,
         serde: Optional[SerializerProtocol] = None,
-        factory: Type[defaultdict] = defaultdict,
+        factory: type[defaultdict] = defaultdict,
     ) -> None:
         super().__init__(serde=serde)
         self.storage = factory(lambda: defaultdict(dict))
@@ -125,24 +127,27 @@ class MemorySaver(
                 checkpoint, metadata, parent_checkpoint_id = saved
                 writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
                 if parent_checkpoint_id:
-                    sends = [
-                        w[2]
-                        for w in self.writes[
-                            (thread_id, checkpoint_ns, parent_checkpoint_id)
-                        ].values()
-                        if w[1] == TASKS
-                    ]
+                    sends = sorted(
+                        (
+                            (*w, k[1])
+                            for k, w in self.writes[
+                                (thread_id, checkpoint_ns, parent_checkpoint_id)
+                            ].items()
+                            if w[1] == TASKS
+                        ),
+                        key=lambda w: (w[3], w[0], w[4]),
+                    )
                 else:
                     sends = []
                 return CheckpointTuple(
                     config=config,
                     checkpoint={
                         **self.serde.loads_typed(checkpoint),
-                        "pending_sends": [self.serde.loads_typed(s) for s in sends],
+                        "pending_sends": [self.serde.loads_typed(s[2]) for s in sends],
                     },
                     metadata=self.serde.loads_typed(metadata),
                     pending_writes=[
-                        (id, c, self.serde.loads_typed(v)) for id, c, v in writes
+                        (id, c, self.serde.loads_typed(v)) for id, c, v, _ in writes
                     ],
                     parent_config={
                         "configurable": {
@@ -160,13 +165,16 @@ class MemorySaver(
                 checkpoint, metadata, parent_checkpoint_id = checkpoints[checkpoint_id]
                 writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
                 if parent_checkpoint_id:
-                    sends = [
-                        w[2]
-                        for w in self.writes[
-                            (thread_id, checkpoint_ns, parent_checkpoint_id)
-                        ].values()
-                        if w[1] == TASKS
-                    ]
+                    sends = sorted(
+                        (
+                            (*w, k[1])
+                            for k, w in self.writes[
+                                (thread_id, checkpoint_ns, parent_checkpoint_id)
+                            ].items()
+                            if w[1] == TASKS
+                        ),
+                        key=lambda w: (w[3], w[0], w[4]),
+                    )
                 else:
                     sends = []
                 return CheckpointTuple(
@@ -179,11 +187,11 @@ class MemorySaver(
                     },
                     checkpoint={
                         **self.serde.loads_typed(checkpoint),
-                        "pending_sends": [self.serde.loads_typed(s) for s in sends],
+                        "pending_sends": [self.serde.loads_typed(s[2]) for s in sends],
                     },
                     metadata=self.serde.loads_typed(metadata),
                     pending_writes=[
-                        (id, c, self.serde.loads_typed(v)) for id, c, v in writes
+                        (id, c, self.serde.loads_typed(v)) for id, c, v, _ in writes
                     ],
                     parent_config={
                         "configurable": {
@@ -200,7 +208,7 @@ class MemorySaver(
         self,
         config: Optional[RunnableConfig],
         *,
-        filter: Optional[Dict[str, Any]] = None,
+        filter: Optional[dict[str, Any]] = None,
         before: Optional[RunnableConfig] = None,
         limit: Optional[int] = None,
     ) -> Iterator[CheckpointTuple]:
@@ -271,13 +279,16 @@ class MemorySaver(
                     ].values()
 
                     if parent_checkpoint_id:
-                        sends = [
-                            w[2]
-                            for w in self.writes[
-                                (thread_id, checkpoint_ns, parent_checkpoint_id)
-                            ].values()
-                            if w[1] == TASKS
-                        ]
+                        sends = sorted(
+                            (
+                                (*w, k[1])
+                                for k, w in self.writes[
+                                    (thread_id, checkpoint_ns, parent_checkpoint_id)
+                                ].items()
+                                if w[1] == TASKS
+                            ),
+                            key=lambda w: (w[3], w[0], w[4]),
+                        )
                     else:
                         sends = []
 
@@ -291,7 +302,9 @@ class MemorySaver(
                         },
                         checkpoint={
                             **self.serde.loads_typed(checkpoint),
-                            "pending_sends": [self.serde.loads_typed(s) for s in sends],
+                            "pending_sends": [
+                                self.serde.loads_typed(s[2]) for s in sends
+                            ],
                         },
                         metadata=metadata,
                         parent_config={
@@ -304,7 +317,7 @@ class MemorySaver(
                         if parent_checkpoint_id
                         else None,
                         pending_writes=[
-                            (id, c, self.serde.loads_typed(v)) for id, c, v in writes
+                            (id, c, self.serde.loads_typed(v)) for id, c, v, _ in writes
                         ],
                     )
 
@@ -353,8 +366,9 @@ class MemorySaver(
     def put_writes(
         self,
         config: RunnableConfig,
-        writes: Sequence[Tuple[str, Any]],
+        writes: Sequence[tuple[str, Any]],
         task_id: str,
+        task_path: str = "",
     ) -> None:
         """Save a list of writes to the in-memory storage.
 
@@ -365,6 +379,7 @@ class MemorySaver(
             config (RunnableConfig): The config to associate with the writes.
             writes (list[tuple[str, Any]]): The writes to save.
             task_id (str): Identifier for the task creating the writes.
+            task_path (str): Path of the task creating the writes.
 
         Returns:
             RunnableConfig: The updated config containing the saved writes' timestamp.
@@ -379,7 +394,12 @@ class MemorySaver(
             if inner_key[1] >= 0 and outer_writes_ and inner_key in outer_writes_:
                 continue
 
-            self.writes[outer_key][inner_key] = (task_id, c, self.serde.dumps_typed(v))
+            self.writes[outer_key][inner_key] = (
+                task_id,
+                c,
+                self.serde.dumps_typed(v),
+                task_path,
+            )
 
     async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
         """Asynchronous version of get_tuple.
@@ -399,7 +419,7 @@ class MemorySaver(
         self,
         config: Optional[RunnableConfig],
         *,
-        filter: Optional[Dict[str, Any]] = None,
+        filter: Optional[dict[str, Any]] = None,
         before: Optional[RunnableConfig] = None,
         limit: Optional[int] = None,
     ) -> AsyncIterator[CheckpointTuple]:
@@ -440,8 +460,9 @@ class MemorySaver(
     async def aput_writes(
         self,
         config: RunnableConfig,
-        writes: Sequence[Tuple[str, Any]],
+        writes: Sequence[tuple[str, Any]],
         task_id: str,
+        task_path: str = "",
     ) -> None:
         """Asynchronous version of put_writes.
 
@@ -452,9 +473,12 @@ class MemorySaver(
             config (RunnableConfig): The config to associate with the writes.
             writes (List[Tuple[str, Any]]): The writes to save, each as a (channel, value) pair.
             task_id (str): Identifier for the task creating the writes.
-        return self.put_writes(config, writes, task_id)
+            task_path (str): Path of the task creating the writes.
+
+        Returns:
+            None
         """
-        return self.put_writes(config, writes, task_id)
+        return self.put_writes(config, writes, task_id, task_path)
 
     def get_next_version(self, current: Optional[str], channel: ChannelProtocol) -> str:
         if current is None:

libs/checkpoint/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint"
-version = "2.0.9"
+version = "2.0.10"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 license = "MIT"

libs/cli/langgraph_cli/cli.py

@@ -605,6 +605,11 @@ def dev(
         ) from None
 
     config_json = langgraph_cli.config.validate_config_file(pathlib.Path(config))
+    if config_json.get("node_version"):
+        raise click.UsageError(
+            "In-mem server for JS graphs is not supported in this version of the LangGraph CLI. Please use `npx @langchain/langgraph-cli` instead."
+        ) from None
+
     cwd = os.getcwd()
     sys.path.append(cwd)
     dependencies = config_json.get("dependencies", [])

libs/cli/langgraph_cli/config.py

@@ -469,10 +469,11 @@ def node_config_to_docker(config_path: pathlib.Path, config: Config, base_image:
         except OSError:
             return False
 
-    npm, yarn, pnpm = [
+    npm, yarn, pnpm, bun = [
         test_file("package-lock.json"),
         test_file("yarn.lock"),
         test_file("pnpm-lock.yaml"),
+        test_file("bun.lockb"),
     ]
 
     if yarn:
@@ -481,6 +482,8 @@ def node_config_to_docker(config_path: pathlib.Path, config: Config, base_image:
         install_cmd = "pnpm i --frozen-lockfile"
     elif npm:
         install_cmd = "npm ci"
+    elif bun:
+        install_cmd = "bun i"
     else:
         install_cmd = "npm i"
     store_config = config.get("store")

libs/cli/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-cli"
-version = "0.1.67"
+version = "0.1.68"
 description = "CLI for interacting with LangGraph API"
 authors = []
 license = "MIT"

libs/langgraph/README.md

@@ -12,25 +12,48 @@
 
 ## Overview
 
-[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
+[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building
+stateful, multi-actor applications with LLMs, used to create agent and multi-agent
+workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraph/tutorials/introduction/).
+
 
 LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
 
-[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger),
+### Why use LangGraph?
 
-To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
+LangGraph provides fine-grained control over both the flow and state of your
+agent applications. It implements a central
+[persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/),
+enabling features that are common to most agent architectures:
 
-### Key Features
+- **Memory**: LangGraph persists arbitrary aspects of your application's state,
+supporting memory of conversations and other updates within and across user
+interactions;
+- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted
+and resumed, allowing for decisions, validation, and corrections at key stages via
+human input.
 
-- **Cycles and Branching**: Implement loops and conditionals in your apps.
-- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
-- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
-- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
-- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+Standardizing these components allows individuals and teams to focus on the behavior
+of their agent, instead of its supporting infrastructure.
+
+Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for
+the development, deployment, debugging, and monitoring of your applications.
+
+LangGraph integrates seamlessly with
+[LangChain](https://python.langchain.com/docs/introduction/) and
+[LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+
+To learn more about LangGraph, check out our first LangChain Academy
+course, *Introduction to LangGraph*, available for free
+[here](https://academy.langchain.com/courses/intro-to-langgraph).
 
 ### LangGraph Platform
 
-LangGraph Platform is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework.
+[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger).
+
+See deployment options [here](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
+(includes a free tier).
+
 Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
 
 - **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
@@ -47,9 +70,7 @@ pip install -U langgraph
 
 ## Example
 
-One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
-
-Let's take a look at a simple example of an agent that can use a search tool.
+Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
 
 ```shell
 pip install langchain-anthropic
@@ -66,10 +87,72 @@ export LANGSMITH_TRACING=true
 export LANGSMITH_API_KEY=lsv2_sk_...
 ```
 
-```python
-from typing import Annotated, Literal, TypedDict
+The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
+
+<details open>
+  <summary>High-level implementation</summary>
+
+```python
+from langgraph.prebuilt import create_react_agent
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+
+# Define the tools for the agent to use
+@tool
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."
+
+
+tools = [search]
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
+
+# Initialize memory to persist state between graph runs
+checkpointer = MemorySaver()
+
+app = create_react_agent(model, tools, checkpointer=checkpointer)
+
+# Use the agent
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+```
+"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
+```
+
+Now when we pass the same <code>"thread_id"</code>, the conversation context is retained via the saved state (i.e. stored list of messages)
+
+```python
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what about ny"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+
+```
+"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
+```
+</details>
+
+> [!TIP]
+> LangGraph is a **low-level** framework that allows you to implement any custom agent
+architectures. Click on the low-level implementation below to see how to implement a
+tool-calling agent from scratch.
+
+<details>
+<summary>Low-level implementation</summary>
+
+```python
+from typing import Literal
 
-from langchain_core.messages import HumanMessage
 from langchain_anthropic import ChatAnthropic
 from langchain_core.tools import tool
 from langgraph.checkpoint.memory import MemorySaver
@@ -91,7 +174,7 @@ tools = [search]
 
 tool_node = ToolNode(tools)
 
-model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0).bind_tools(tools)
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
 
 # Define the function that determines whether to continue or not
 def should_continue(state: MessagesState) -> Literal["tools", END]:
@@ -145,92 +228,102 @@ checkpointer = MemorySaver()
 # Note that we're (optionally) passing the memory when compiling the graph
 app = workflow.compile(checkpointer=checkpointer)
 
-# Use the Runnable
+# Use the agent
 final_state = app.invoke(
-    {"messages": [HumanMessage(content="what is the weather in sf")]},
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
     config={"configurable": {"thread_id": 42}}
 )
 final_state["messages"][-1].content
 ```
 
-```
-"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
-```
+<b>Step-by-step Breakdown</b>:
 
-Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
+<details>
+<summary>Initialize the model and tools.</summary>
+<ul>
+  <li>
+    We use <code>ChatAnthropic</code> as our LLM. <strong>NOTE:</strong> we need to make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the <code>.bind_tools()</code> method.
+  </li>
+  <li>
+    We define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that <a href="https://python.langchain.com/docs/modules/agents/tools/custom_tools">here</a>.
+  </li>
+</ul>
+</details>
 
-```python
-final_state = app.invoke(
-    {"messages": [HumanMessage(content="what about ny")]},
-    config={"configurable": {"thread_id": 42}}
-)
-final_state["messages"][-1].content
-```
+<details>
+<summary>Initialize graph with state.</summary>
 
-```
-"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
-```
+<ul>
+    <li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
+    <li><code>MessagesState</code> is a prebuilt state schema that has one attribute -- a list of LangChain <code>Message</code> objects, as well as logic for merging the updates from each node into the state.</li>
+</ul>
+</details>
 
-### Step-by-step Breakdown
+<details>
+<summary>Define graph nodes.</summary>
 
-1. <details>
-    <summary>Initialize the model and tools.</summary>
+There are two main nodes we need:
 
-    - we use `ChatAnthropic` as our LLM. **NOTE:** we need make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the `.bind_tools()` method.
-    - we define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
-   </details>
+<ul>
+    <li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
+    <li>The <code>tools</code> node that invokes tools: if the agent decides to take an action, this node will then execute that action.</li>
+</ul>
+</details>
 
-2. <details>
-    <summary>Initialize graph with state.</summary>
+<details>
+<summary>Define entry point and graph edges.</summary>
 
-    - we initialize graph (`StateGraph`) by passing state schema (in our case `MessagesState`)
-    - `MessagesState` is a prebuilt state schema that has one attribute -- a list of LangChain `Message` objects, as well as logic for merging the updates from each node into the state
-   </details>
+First, we need to set the entry point for graph execution - <code>agent</code> node.
 
-3. <details>
-    <summary>Define graph nodes.</summary>
+Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (<code>MessagesState</code>). In our case, the destination is not known until the agent (LLM) decides.
 
-    There are two main nodes we need:
+<ul>
+  <li>Conditional edge: after the agent is called, we should either:
+    <ul>
+      <li>a. Run tools if the agent said to take an action, OR</li>
+      <li>b. Finish (respond to the user) if the agent did not ask to run tools</li>
+    </ul>
+  </li>
+  <li>Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next</li>
+</ul>
+</details>
 
-      - The `agent` node: responsible for deciding what (if any) actions to take.
-      - The `tools` node that invokes tools: if the agent decides to take an action, this node will then execute that action.
-   </details>
+<details>
+<summary>Compile the graph.</summary>
 
-4. <details>
-    <summary>Define entry point and graph edges.</summary>
+<ul>
+  <li>
+    When we compile the graph, we turn it into a LangChain 
+    <a href="https://python.langchain.com/v0.2/docs/concepts/#runnable-interface">Runnable</a>, 
+    which automatically enables calling <code>.invoke()</code>, <code>.stream()</code> and <code>.batch()</code> 
+    with your inputs
+  </li>
+  <li>
+    We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, 
+    human-in-the-loop workflows, time travel and more. In our case we use <code>MemorySaver</code> - 
+    a simple in-memory checkpointer
+  </li>
+</ul>
+</details>
 
-      First, we need to set the entry point for graph execution - `agent` node.
+<details>
+<summary>Execute the graph.</summary>
 
-      Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (`MessageState`). In our case, the destination is not known until the agent (LLM) decides.
-
-      - Conditional edge: after the agent is called, we should either:
-        - a. Run tools if the agent said to take an action, OR
-        - b. Finish (respond to the user) if the agent did not ask to run tools
-      - Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
-   </details>
-
-5. <details>
-    <summary>Compile the graph.</summary>
-
-    - When we compile the graph, we turn it into a LangChain [Runnable](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface), which automatically enables calling `.invoke()`, `.stream()` and `.batch()` with your inputs
-    - We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, human-in-the-loop workflows, time travel and more. In our case we use `MemorySaver` - a simple in-memory checkpointer
-    </details>
-
-6. <details>
-   <summary>Execute the graph.</summary>
-
-    1. LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, `"agent"`.
-    2. The `"agent"` node executes, invoking the chat model.
-    3. The chat model returns an `AIMessage`. LangGraph adds this to the state.
-    4. Graph cycles the following steps until there are no more `tool_calls` on `AIMessage`:
-
-        - If `AIMessage` has `tool_calls`, `"tools"` node executes
-        - The `"agent"` node executes again and returns `AIMessage`
-
-    5. Execution progresses to the special `END` value and outputs the final state.
-    And as a result, we get a list of all our chat messages as output.
-   </details>
+<ol>
+  <li>LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, <code>"agent"</code>.</li>
+  <li>The <code>"agent"</code> node executes, invoking the chat model.</li>
+  <li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
+  <li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
+    <ul>
+      <li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
+      <li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
+    </ul>
+  </li>
+  <li>Execution progresses to the special <code>END</code> value and outputs the final state. And as a result, we get a list of all our chat messages as output.</li>
+</ol>
+</details>
 
+</details>
 
 ## Documentation