Update inmem

(sdk-js): Release 0.0.26

docs/_scripts/prepare_notebooks_for_ci.py

@@ -36,10 +36,11 @@ NOTEBOOKS_NO_EXECUTION = [
     "docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb",
     # this loads a massive dataset from gcp
     "docs/docs/tutorials/usaco/usaco.ipynb",
+    # TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
+    "docs/docs/how-tos/autogen-integration.ipynb",
     # TODO: need to update these notebooks to make sure they are runnable in CI
     "docs/docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
     "docs/docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
-    "docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb",  # taking a very long time to run
     "docs/docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
     "docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # Cannot create a consistent method resolution error from VCR
     "docs/docs/how-tos/map-reduce.ipynb"  # flakiness from structured output, only when running with VCR

docs/docs/cloud/deployment/test_locally.md

@@ -21,7 +21,7 @@ Install the proper packages:
 Ensure you have an API key, which you can create from the [LangSmith UI](https://smith.langchain.com) (Settings > API Keys). This is required to authenticate that you have LangGraph Cloud access. After you have saved the key to a safe place, place the following line in your `.env` file:
 
 ```python
-LANGCHAIN_API_KEY = *********
+LANGSMITH_API_KEY = *********
 ```
 
 ## Start the API server
@@ -54,7 +54,7 @@ You can either initialize by passing authentication or by setting an environment
     from langgraph_sdk import get_client
 
     # only pass the url argument to get_client() if you changed the default port when calling langgraph up
-    client = get_client(url=<DEPLOYMENT_URL>,api_key=<LANGCHAIN_API_KEY>)
+    client = get_client(url=<DEPLOYMENT_URL>,api_key=<LANGSMITH_API_KEY>)
     # Using the graph deployed with the name "agent"
     assistant_id = "agent"
     thread = await client.threads.create()
@@ -66,7 +66,7 @@ You can either initialize by passing authentication or by setting an environment
     import { Client } from "@langchain/langgraph-sdk";
 
     // only set the apiUrl if you changed the default port when calling langgraph up
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <LANGCHAIN_API_KEY> });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <LANGSMITH_API_KEY> });
     // Using the graph deployed with the name "agent"
     const assistantId = "agent";
     const thread = await client.threads.create();
@@ -78,13 +78,13 @@ You can either initialize by passing authentication or by setting an environment
     curl --request POST \
       --url <DEPLOYMENT_URL>/threads \
       --header 'Content-Type: application/json'
-      --header 'x-api-key: <LANGCHAIN_API_KEY>'
+      --header 'x-api-key: <LANGSMITH_API_KEY>'
     ```
   
 
 #### Initialize with environment variables
 
-If you have a `LANGCHAIN_API_KEY` set in your environment, you do not need to explicitly pass authentication to the client
+If you have a `LANGSMITH_API_KEY` set in your environment, you do not need to explicitly pass authentication to the client
 
 === "Python"
 
@@ -154,7 +154,7 @@ Now we can invoke our graph to ensure it is working. Make sure to change the inp
     }
     ```
 
-  === "CURL"
+=== "CURL"
 
     ```bash
     curl --request POST \

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

@@ -94,6 +94,7 @@ Now we can start our two runs and join the second on euntil it has completed:
         assistant_id,
         input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
     )
+    # sleep a bit to get partial outputs from the first run
     await asyncio.sleep(2)
     run = await client.runs.create(
         thread["thread_id"],
@@ -114,6 +115,7 @@ Now we can start our two runs and join the second on euntil it has completed:
       assistantId,
       { input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
     );
+    // sleep a bit to get partial outputs from the first run
     await new Promise(resolve => setTimeout(resolve, 2000)); 
 
     let run = await client.runs.create(

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

@@ -95,7 +95,6 @@ Now let's run a thread with the multitask parameter set to "rollback":
         assistant_id,
         input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
     )
-    await asyncio.sleep(2)
     run = await client.runs.create(
         thread["thread_id"],
         assistant_id,
@@ -115,7 +114,6 @@ Now let's run a thread with the multitask parameter set to "rollback":
       assistantId,
       { input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
     );
-    await new Promise(resolve => setTimeout(resolve, 2000)); 
 
     let run = await client.runs.create(
       thread["thread_id"],
@@ -139,7 +137,7 @@ Now let's run a thread with the multitask parameter set to "rollback":
     --data "{
       \"assistant_id\": \"agent\",
       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
-    }" && sleep 2 && curl --request POST \
+    }" && curl --request POST \
     --url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
     --header 'Content-Type: application/json' \
     --data "{

docs/docs/cloud/reference/api/openapi.json

@@ -2868,9 +2868,18 @@
             "description": "The cron schedule to execute this job on."
           },
           "assistant_id": {
-            "type": "string",
-            "format": "uuid",
-            "title": "Assistant Id"
+            "anyOf": [
+              {
+                "type": "string",
+                "format": "uuid",
+                "title": "Assistant Id"
+              },
+              {
+                "type": "string",
+                "title": "Graph Id"
+              }
+            ],
+            "description": "The assistant ID or graph name to run. If using graph name, will default to the assistant automatically created from that graph by the server."
           },
           "input": {
             "anyOf": [
@@ -3171,6 +3180,66 @@
         ],
         "title": "Run"
       },
+      "Send": {
+        "type": "object",
+        "title": "Send",
+        "description": "A message to send to a node.",
+        "properties": {
+          "node": {
+            "type": "string",
+            "title": "Node",
+            "description": "The node to send the message to."
+          },
+          "input": {
+            "type": "object",
+            "title": "Message",
+            "description": "The message to send."
+          }
+        },
+        "required": [
+          "node",
+          "input"
+        ]
+      },
+      "Command": {
+        "type": "object",
+        "title": "Command",
+        "description": "The command to run.",
+        "properties": {
+          "update": {
+            "type": "object",
+            "title": "Update",
+            "description": "An update to the state."
+          },
+          "resume": {
+            "type": [
+              "object",
+              "array",
+              "number",
+              "string",
+              "null"
+            ],
+            "title": "Resume",
+            "description": "A value to pass to an interrupted node."
+          },
+          "send": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/Send"
+              },
+              {
+                "type": "array",
+                "items": {
+                  "$ref": "#/components/schemas/Send"
+                }
+              },
+              {
+                "type": "null"
+              }
+            ]
+          }
+        }
+      },
       "RunCreateStateful": {
         "properties": {
           "assistant_id": {
@@ -3196,13 +3265,19 @@
           "input": {
             "anyOf": [
               {
-                "items": {
-                  "type": "object"
-                },
-                "type": "array"
+                "type": "object"
               },
               {
-                "type": "object"
+                "type": "null"
+              }
+            ],
+            "title": "Input",
+            "description": "The input to the graph."
+          },
+          "command": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/Command"
               },
               {
                 "type": "null"
@@ -3405,13 +3480,19 @@
           "input": {
             "anyOf": [
               {
-                "items": {
-                  "type": "object"
-                },
-                "type": "array"
+                "type": "object"
               },
               {
-                "type": "object"
+                "type": "null"
+              }
+            ],
+            "title": "Input",
+            "description": "The input to the graph."
+          },
+          "command": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/Command"
               },
               {
                 "type": "null"

docs/docs/cloud/reference/cli.md

@@ -53,7 +53,7 @@ Example:
 }
 ```
 
-Example:
+Example with environment variables:
 
 ```json
 {
@@ -78,6 +78,37 @@ The base command for the LangGraph CLI is `langgraph`.
 langgraph [OPTIONS] COMMAND [ARGS]
 ```
 
+### `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.
+
+**Installation**
+
+This command requires the "inmem" extra to be installed:
+
+```bash
+pip install -U "langgraph-cli[inmem]"
+```
+
+**Usage**
+
+```
+langgraph 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                                                  |
+| `--no-browser`             |                  | Disable automatic browser opening                                                          |
+| `--debug-port INTEGER`     |                  | Port for debugger to listen on                                                            |
+| `--help`                   |                  | Display command documentation                                                              |
+
 ### `build`
 
 Build LangGraph Cloud API server Docker image.
@@ -100,7 +131,7 @@ langgraph build [OPTIONS]
 
 ### `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.
+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**
 
@@ -120,8 +151,8 @@ langgraph up [OPTIONS]
 | `--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 test --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` |
+| `-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.                                                                                        |
 
@@ -148,9 +179,9 @@ Example:
 langgraph dockerfile -c langgraph.json Dockerfile
 ```
 
-Would generate something like the following:
+This generates a Dockerfile that looks similar to:
 
-```text
+```dockerfile
 FROM langchain/langgraph-api:3.11
 
 ADD ./pipconf.txt /pipconfig.txt
@@ -170,6 +201,3 @@ RUN set -ex && \
 RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
 
 ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
-```
-
-You can then customize, build images, push, and deploy from this file.

docs/docs/concepts/double_texting.md

@@ -15,10 +15,12 @@ If you do not want to use LangGraph Platform, we describe the options we have im
 ![](img/double_texting.png)
 
 ## Reject
+
 This is the simplest option, this just rejects any follow up runs and does not allow double texting. 
 See the [how-to guide](../cloud/how-tos/reject_concurrent.md) for configuring the reject double text option.
 
 ## Enqueue
+
 This is a relatively simple option which continues the first run until it completes the whole run, then sends the new input as a separate run. 
 See the [how-to guide](../cloud/how-tos/enqueue_concurrent.md) for configuring the enqueue double text option.
 
@@ -35,10 +37,6 @@ See the [how-to guide](../cloud/how-tos/interrupt_concurrent.md) for configuring
 
 ## Rollback
 
-This option rolls back all work done up until that point. 
-It then sends the user input in, basically as if it just followed the original run input. 
-
-This may create some weird states - for example, you may have two `User` messages in a row, with no `Asssitant` message in between them. 
-You will need to make sure the LLM you are calling can handle that, or combine those into a single `User` message.
+This option interrupts the current execution AND rolls back all work done up until that point, including the original run input. It then sends the new user input in, basically as if it was the original input.
 
 See the [how-to guide](../cloud/how-tos/rollback_concurrent.md) for configuring the rollback double text option.

docs/docs/concepts/high_level.md

@@ -2,13 +2,13 @@
 
 LLMs are extremely powerful, particularly when connected to other systems such as a retriever or APIs. This is why many LLM applications use a control flow of steps before and / or after LLM calls. As an example [RAG](https://github.com/langchain-ai/rag-from-scratch) performs retrieval of relevant documents to a question, and passes those documents to an LLM in order to ground the response. Often a control flow of steps before and / or after an LLM is called a "chain." Chains are a popular paradigm for programming with LLMs and offer a high degree of reliability; the same set of steps runs with each chain invocation.
 
-However, we often want LLM systems that can pick their own control flow! This is one definition of an [agent](https://blog.langchain.dev/what-is-an-agent/): an agent is a system that uses an LLM to decide the control flow of an application. Unlike a chain, an agent given an LLM some degree of control over the sequence of steps in the application. Examples of using an LLM to decide the control of an application:
+However, we often want LLM systems that can pick their own control flow! This is one definition of an [agent](https://blog.langchain.dev/what-is-an-agent/): an agent is a system that uses an LLM to decide the control flow of an application. Unlike a chain, an agent gives an LLM some degree of control over the sequence of steps in the application. Examples of using an LLM to decide the control of an application:
 
 - Using an LLM to route between two potential paths
 - Using an LLM to decide which of many tools to call
 - Using an LLM to decide whether the generated answer is sufficient or more work is need
 
-There are many different types of [agent architectures](https://blog.langchain.dev/what-is-a-cognitive-architecture/) to consider, which given an LLM varying levels of control. On one extreme, a router allows an LLM to select a single step from a specified set of options and, on the other extreme, a fully autonomous long-running agent may have complete freedom to select any sequence of steps that it wants for a given problem. 
+There are many different types of [agent architectures](https://blog.langchain.dev/what-is-a-cognitive-architecture/) to consider, which give an LLM varying levels of control. On one extreme, a router allows an LLM to select a single step from a specified set of options and, on the other extreme, a fully autonomous long-running agent may have complete freedom to select any sequence of steps that it wants for a given problem. 
 
 ![Agent Types](img/agent_types.png)
 

docs/docs/concepts/langgraph_cli.md

@@ -28,9 +28,28 @@ The CLI provides the following core functionality:
 
 The `langgraph build` command builds a Docker image for the [LangGraph API server](./langgraph_server.md) that can be directly deployed.
 
+### `dev`
+
+!!! note "New in version 0.1.55"
+    The `langgraph dev` command was introduced in langgraph-cli version 0.1.55.
+
+The `langgraph dev` command starts a lightweight development server that requires no Docker installation. This server is ideal for rapid development and testing, with features like:
+
+- Hot reloading: Changes to your code are automatically detected and reloaded
+- Debugger support: Attach your IDE's debugger for line-by-line debugging
+- In-memory state with local persistence: Server state is stored in memory for speed but persisted locally between restarts
+
+To use this command, you need to install the CLI with the "inmem" extra:
+
+```bash
+pip install -U "langgraph-cli[inmem]"
+```
+
+**Note**: This command is intended for local development and testing only. It is not recommended for production use. Since it does not use Docker, we recommend using virtual environments to manage your project's dependencies.
+
 ### `up`
 
-The `langgraph up` command starts an instance of the [LangGraph API server](./langgraph_server.md) locally. This requires docker to be installed and running locally. It also requires a LangSmith API key for local development or a license key for production use.
+The `langgraph up` command starts an instance of the [LangGraph API server](./langgraph_server.md) locally in a docker container. This requires thedocker server to be running locally. It also requires a LangSmith API key for local development or a license key for production use.
 
 The server includes all API endpoints for your graph's runs, threads, assistants, etc. as well as the other services required to run your agent, including a managed database for checkpointing and storage.
 

docs/docs/concepts/langgraph_studio.md

@@ -35,6 +35,16 @@ While in Beta, LangGraph Studio is available for free to all [LangSmith](https:/
 
 If you have deployed your LangGraph application on LangGraph Platform (Cloud), you can access the studio as part of that
 
+### Development server
+
+LangGraph CLI also contains a command for running an in-memory development server that can be used to connect a local LangGraph app with the studio.
+See [instructions here](../cloud/reference/cli.md#dev) for more information.
+
+The way this works is that it runs inside your local environment.
+It will spin up an in-memory, development server to deploy the graph.
+You can then connect to the studio via the Cloud hosted version of LangGraph Platform.
+To be clear, the web studio will connect to your locally running server - your agent is still running locally and never leaves your device.
+
 ## Studio FAQs
 
 ### Why is my project failing to start?

docs/docs/concepts/low_level.md

@@ -391,7 +391,7 @@ Read [this how-to](https://langchain-ai.github.io/langgraph/how-tos/recursion-li
 
 It can often be useful to set breakpoints before or after certain nodes execute. This can be used to wait for human approval before continuing. These can be set when you ["compile" a graph](#compiling-your-graph). You can set breakpoints either _before_ a node executes (using `interrupt_before`) or after a node executes (using `interrupt_after`.)
 
-You **MUST** use a [checkpoiner](./persistence.md) when using breakpoints. This is because your graph needs to be able to resume execution.
+You **MUST** use a [checkpointer](./persistence.md) when using breakpoints. This is because your graph needs to be able to resume execution.
 
 In order to resume execution, you can just invoke your graph with `None` as the input.
 

docs/docs/concepts/persistence.md

@@ -159,7 +159,7 @@ You must pass these when invoking the graph as part of the `configurable` portio
 # {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}  # also valid config
 
 config = {"configurable": {"thread_id": "1"}}
-graph.invoke(inputs, config=config)
+graph.invoke(None, config=config)
 ```
 
 Importantly, LangGraph knows whether a particular checkpoint has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.ipynb).

docs/docs/concepts/template_applications.md

@@ -6,22 +6,14 @@
 
 Templates are open source reference applications designed to help you get started quickly when building with LangGraph. They provide working examples of common agentic workflows that can be customized to your needs.
 
-Templates can be accessed via [LangGraph Studio](langgraph_studio.md), or cloned directly from Github. You can download LangGraph Studio and see available templates [here](https://studio.langchain.com/).
+Templates can be accessed via [LangGraph Studio (macOS only)](langgraph_studio.md), or cloned directly from Github. You can download LangGraph Studio and see available templates [here](https://studio.langchain.com/).
 
 ## Available templates
 
-- **New LangGraph Project**: A simple, minimal chatbot with memory.
-  - [Python](https://github.com/langchain-ai/new-langgraph-project)
-  - [JS/TS](https://github.com/langchain-ai/new-langgraphjs-project)
-- **ReAct Agent**: A simple agent that can be flexibly extended to many tools.
-  - [Python](https://github.com/langchain-ai/react-agent)
-  - [JS/TS](https://github.com/langchain-ai/react-agent-js)
-- **Memory Agent**: A ReAct-style agent with an additional tool to store memories for use across conversational threads.
-  - [Python](https://github.com/langchain-ai/memory-agent)
-  - [JS/TS](https://github.com/langchain-ai/memory-agent-js)
-- **Retrieval Agent**: An agent that includes a retrieval-based question-answering system.
-  - [Python](https://github.com/langchain-ai/retrieval-agent-template)
-  - [JS/TS](https://github.com/langchain-ai/retrieval-agent-template-js)
-- **Data-enrichment Agent**: An agent that performs web searches and organizes its findings into a structured format.
-  - [Python](https://github.com/langchain-ai/data-enrichment)
-  - [JS/TS](https://github.com/langchain-ai/data-enrichment-js)
+| Template                  | Description                                                                              | Python                                                           | JS/TS                                                               |
+|---------------------------|------------------------------------------------------------------------------------------|------------------------------------------------------------------|---------------------------------------------------------------------|
+| **New LangGraph Project** | A simple, minimal chatbot with memory.                                                   | [Repo](https://github.com/langchain-ai/new-langgraph-project)    | [Repo](https://github.com/langchain-ai/new-langgraphjs-project)     |
+| **ReAct Agent**           | A simple agent that can be flexibly extended to many tools.                              | [Repo](https://github.com/langchain-ai/react-agent)              | [Repo](https://github.com/langchain-ai/react-agent-js)              |
+| **Memory Agent**          | A ReAct-style agent with an additional tool to store memories for use across threads.    | [Repo](https://github.com/langchain-ai/memory-agent)             | [Repo](https://github.com/langchain-ai/memory-agent-js)             |
+| **Retrieval Agent**       | An agent that includes a retrieval-based question-answering system.                      | [Repo](https://github.com/langchain-ai/retrieval-agent-template) | [Repo](https://github.com/langchain-ai/retrieval-agent-template-js) |
+| **Data-Enrichment Agent** | An agent that performs web searches and organizes its findings into a structured format. | [Repo](https://github.com/langchain-ai/data-enrichment)          | [Repo](https://github.com/langchain-ai/data-enrichment-js)          |

docs/docs/how-tos/autogen-langgraph-platform.ipynb

@@ -0,0 +1,171 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8381b6e0-29a6-48c5-b451-5d2549351249",
+   "metadata": {},
+   "source": [
+    "# How to use LangGraph Platform to deploy CrewAI, AutoGen, and other frameworks\n",
+    "\n",
+    "[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) provides infrastructure for deploying agents. This integrates seamlessly with LangGraph, but can also work with other frameworks. The way to make this work is to wrap the agent in a single LangGraph node, and have that be the entire graph.\n",
+    "\n",
+    "Doing so will allow you to deploy to LangGraph Platform, and allows you to get a lot of the [benefits](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/). You get horizontally scalable infrastructure, a task queue to handle bursty operations, a persistence layer to power short term memory, and long term memory support.\n",
+    "\n",
+    "In this guide we show how to do this with an AutoGen agent, but this method should work for agents defined in other frameworks like CrewAI, LlamaIndex, and others as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1113cb16-b538-448c-924c-85731ce96ebd",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f05993fa-9d03-4f45-bc13-0a8d87260d86",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "%pip install autogen langgraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4e0ca12-1714-4776-a30a-9527e519799b",
+   "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": "1926bbc3-6b06-41e0-9604-860a2bbf8fa3",
+   "metadata": {},
+   "source": [
+    "## Define autogen agent\n",
+    "\n",
+    "Here we define our AutoGen agent. From https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4a14dc7-d565-4207-8788-525f85b9fb27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import autogen\n",
+    "import os\n",
+    "\n",
+    "config_list = [{\"model\": \"gpt-4o\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n",
+    "\n",
+    "llm_config = {\n",
+    "    \"timeout\": 600,\n",
+    "    \"cache_seed\": 42,\n",
+    "    \"config_list\": config_list,\n",
+    "    \"temperature\": 0,\n",
+    "}\n",
+    "\n",
+    "autogen_agent = autogen.AssistantAgent(\n",
+    "    name=\"assistant\",\n",
+    "    llm_config=llm_config,\n",
+    ")\n",
+    "\n",
+    "user_proxy = autogen.UserProxyAgent(\n",
+    "    name=\"user_proxy\",\n",
+    "    human_input_mode=\"NEVER\",\n",
+    "    max_consecutive_auto_reply=10,\n",
+    "    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
+    "    code_execution_config={\n",
+    "        \"work_dir\": \"web\",\n",
+    "        \"use_docker\": False,\n",
+    "    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
+    "    llm_config=llm_config,\n",
+    "    system_message=\"Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1170836-f23e-4e4c-ab83-ce791cd7fbd2",
+   "metadata": {},
+   "source": [
+    "## Wrap in LangGraph\n",
+    "\n",
+    "We now wrap the AutoGen agent in a single LangGraph node, and make that the entire graph.\n",
+    "The main thing this involves is defining an Input and Output schema for the node, which you would need to do if deploying this manually, so it's no extra work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7b417c16-ff4e-4d5c-a9a9-0aaeeef6ede5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import StateGraph, MessagesState\n",
+    "\n",
+    "\n",
+    "def call_autogen_agent(state: MessagesState):\n",
+    "    last_message = state[\"messages\"][-1]\n",
+    "    response = user_proxy.initiate_chat(autogen_agent, message=last_message.content)\n",
+    "    # get the final response from the agent\n",
+    "    content = response.chat_history[-1][\"content\"]\n",
+    "    return {\"messages\": {\"role\": \"assistant\", \"content\": content}}\n",
+    "\n",
+    "\n",
+    "graph = StateGraph(MessagesState)\n",
+    "graph.add_node(call_autogen_agent)\n",
+    "graph.set_entry_point(\"call_autogen_agent\")\n",
+    "graph = graph.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6a18377-ac29-478f-a76a-b213f1a3c85d",
+   "metadata": {},
+   "source": [
+    "## Deploy with LangGraph Platform\n",
+    "\n",
+    "You can now deploy this as you normally would with LangGraph Platform. See [these instructions](https://langchain-ai.github.io/langgraph/concepts/deployment_options/) for more details."
+   ]
+  }
+ ],
+ "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/configuration.ipynb

@@ -345,7 +345,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.1"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/deploy-self-hosted.md

@@ -23,8 +23,8 @@ You will eventually need to pass in the following environment variables to the L
 
 - `REDIS_URI`: Connection details to a Redis instance. Redis will be used as a pub-sub broker to enable streaming real time output from background runs.
 - `DATABASE_URI`: Postgres connection details. Postgres will be used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics.
-- `LANGSMITH_API_KEY`: (If using [Self-Hosted Lite]) LangSmith API key. This will be used to authenticate ONCE at server start up.
-- `LANGGRAPH_CLOUD_LICENSE_KEY`: (If using Self-Hosted Enterprise) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
+- `LANGSMITH_API_KEY`: (If using [Self-Hosted Lite](../concepts/deployment_options.md#self-hosted-lite)) LangSmith API key. This will be used to authenticate ONCE at server start up.
+- `LANGGRAPH_CLOUD_LICENSE_KEY`: (If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
 
 
 ## Build the Docker Image
@@ -70,7 +70,7 @@ If you want to run this quickly without setting up a separate Redis and Postgres
     * You need to replace `my-image` with the name of the image you built in the previous step (from `langgraph build`).
     and you should provide appropriate values for `REDIS_URI`, `DATABASE_URI`, and `LANGSMITH_API_KEY`.
     * If your application requires additional environment variables, you can pass them in a similar way.
-    * If using Self-Hosted Enterprise, you must provide `LANGGRAPH_CLOUD_LICENSE_KEY` as an additional environment variable.
+    * If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise), you must provide `LANGGRAPH_CLOUD_LICENSE_KEY` as an additional environment variable.
 
 
 ### Using Docker Compose

docs/docs/how-tos/index.md

@@ -103,6 +103,7 @@ These how-to guides show common patterns for tool calling with LangGraph:
 - [How to force function 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)
 
 ### Prebuilt ReAct Agent
 
@@ -141,6 +142,7 @@ Learn how to set up your app for deployment to LangGraph Platform:
 - [How to customize Dockerfile](../cloud/deployment/custom_docker.md)
 - [How to test locally](../cloud/deployment/test_locally.md)
 - [How to rebuild graph at runtime](../cloud/deployment/graph_rebuild.md)
+- [How to use LangGraph Platform to deploy CrewAI, AutoGen, and other frameworks](autogen-langgraph-platform.ipynb)
 
 ### Deployment
 

docs/docs/how-tos/local-studio.md

@@ -0,0 +1,86 @@
+# How to connect a local agent to LangGraph Studio
+
+This guide shows you how to connect your local agent to [LangGraph Studio](../concepts/langgraph_studio.md) for visualization, interaction, and debugging.
+
+## Connection Options
+
+There are two ways to connect your local agent to LangGraph Studio:
+
+- [LangGraph Desktop](../concepts/langgraph_studio.md#desktop-app): Application, Mac only, requires Docker
+- [Development Server](../concepts/langgraph_studio.md#dev-server): Python package, all platforms, no Docker
+
+In this guide we will cover how to use the development server as that is generally an easier and better experience.
+
+## Setup your application
+
+First, you will need to setup your application in the proper format.
+This means defining a `langgraph.json` file which contains paths to your agent(s).
+See [this guide](../concepts/application_structure.md) for information on how to do so.
+
+## Install langgraph-cli
+
+You will need to install [`langgraph-cli`](../cloud/reference/cli.md#langgraph-cli) (version `0.1.55` or higher).
+You will need to make sure to install the `inmem` extras.
+
+```shell
+pip install "langgraph-cli[inmem]==0.1.55"
+```
+
+## Run the development server
+
+1. Navigate to your project directory (where `langgraph.json` is located)
+
+2. Start the server:
+   ```bash
+   langgraph dev
+   ```
+
+This will look for the `langgraph.json` file in your current directory. 
+In there, it will find the paths to the graph(s), and start those up.
+It will then automatically connect to the cloud-hosted studio.
+
+## Use the studio
+
+After connecting to the studio, a browser window should automatically pop up.
+This will use the cloud hosted studio UI to connect to your local development server.
+Your graph is still running locally, the UI is connecting to visualizing the agent and threads that are defined locally.
+
+The graph will always use the most up-to-date code, so you will be able to change the underlying code and have it automatically reflected in the studio.
+This is useful for debugging workflows.
+You can run your graph in the UI until it messes up, go in and change your code, and then rerun from the node that failed.
+
+# (Optional) Attach a debugger
+
+For step-by-step debugging with breakpoints and variable inspection:
+
+```bash
+# Install debugpy package
+pip install debugpy
+
+# Start server with debugging enabled
+langgraph dev --debug-port 5678
+```
+
+Then attach your preferred debugger:
+
+=== "VS Code"
+    Add this configuration to `launch.json`:
+    ```json
+    {
+      "name": "Attach to LangGraph",
+      "type": "debugpy",
+      "request": "attach",
+      "connect": {
+        "host": "0.0.0.0",
+        "port": 5678
+      }
+    }
+    ```
+    Specify the port number you chose in the previous step.
+
+=== "PyCharm"
+    1. Go to Run → Edit Configurations
+    2. Click + and select "Python Debug Server"
+    3. Set IDE host name: `localhost`
+    4. Set port: `5678` (or the port number you chose in the previous step)
+    5. Click "OK" and start debugging

docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb

@@ -102,7 +102,7 @@
     "from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader\n",
     "\n",
     "# LCEL docs\n",
-    "url = \"https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel\"\n",
+    "url = \"https://python.langchain.com/docs/concepts/lcel/\"\n",
     "loader = RecursiveUrlLoader(\n",
     "    url=url, max_depth=20, extractor=lambda x: Soup(x, \"html.parser\").text\n",
     ")\n",

docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb

@@ -112,7 +112,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "_set_env(\"LANGCHAIN_API_KEY\")\n",
+    "_set_env(\"LANGSMITH_API_KEY\")\n",
     "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "os.environ[\"LANGCHAIN_PROJECT\"] = \"local-llama32-rag\""
    ]

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

@@ -3,7 +3,7 @@ from contextlib import contextmanager
 from typing import Any, Iterator, Optional, Sequence, Union
 
 from langchain_core.runnables import RunnableConfig
-from psycopg import Connection, Cursor, Pipeline
+from psycopg import Capabilities, Connection, Cursor, Pipeline
 from psycopg.errors import UndefinedTable
 from psycopg.rows import DictRow, dict_row
 from psycopg.types.json import Jsonb
@@ -52,6 +52,7 @@ class PostgresSaver(BasePostgresSaver):
         self.conn = conn
         self.pipe = pipe
         self.lock = threading.Lock()
+        self.supports_pipeline = Capabilities().has_pipeline()
 
     @classmethod
     @contextmanager
@@ -365,6 +366,13 @@ class PostgresSaver(BasePostgresSaver):
 
     @contextmanager
     def _cursor(self, *, pipeline: bool = False) -> Iterator[Cursor[DictRow]]:
+        """Create a database cursor as a context manager.
+
+        Args:
+            pipeline (bool): whether to use pipeline for the DB operations inside the context manager.
+                Will be applied regardless of whether the PostgresSaver instance was initialized with a pipeline.
+                If pipeline mode is not supported, will fall back to using transaction context manager.
+        """
         with _get_connection(self.conn) as conn:
             if self.pipe:
                 # a connection in pipeline mode can be used concurrently
@@ -379,10 +387,17 @@ class PostgresSaver(BasePostgresSaver):
             elif pipeline:
                 # a connection not in pipeline mode can only be used by one
                 # thread/coroutine at a time, so we acquire a lock
-                with self.lock, conn.pipeline(), conn.cursor(
-                    binary=True, row_factory=dict_row
-                ) as cur:
-                    yield cur
+                if self.supports_pipeline:
+                    with self.lock, conn.pipeline(), conn.cursor(
+                        binary=True, row_factory=dict_row
+                    ) as cur:
+                        yield cur
+                else:
+                    # Use connection's transaction context manager when pipeline mode not supported
+                    with self.lock, conn.transaction(), conn.cursor(
+                        binary=True, row_factory=dict_row
+                    ) as cur:
+                        yield cur
             else:
                 with self.lock, conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur

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

@@ -3,7 +3,7 @@ from contextlib import asynccontextmanager
 from typing import Any, AsyncIterator, Iterator, Optional, Sequence, Union
 
 from langchain_core.runnables import RunnableConfig
-from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline
+from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
 from psycopg.errors import UndefinedTable
 from psycopg.rows import DictRow, dict_row
 from psycopg.types.json import Jsonb
@@ -55,6 +55,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
         self.pipe = pipe
         self.lock = asyncio.Lock()
         self.loop = asyncio.get_running_loop()
+        self.supports_pipeline = Capabilities().has_pipeline()
 
     @classmethod
     @asynccontextmanager
@@ -323,6 +324,13 @@ class AsyncPostgresSaver(BasePostgresSaver):
     async def _cursor(
         self, *, pipeline: bool = False
     ) -> AsyncIterator[AsyncCursor[DictRow]]:
+        """Create a database cursor as a context manager.
+
+        Args:
+            pipeline (bool): whether to use pipeline for the DB operations inside the context manager.
+                Will be applied regardless of whether the AsyncPostgresSaver instance was initialized with a pipeline.
+                If pipeline mode is not supported, will fall back to using transaction context manager.
+        """
         async with _get_connection(self.conn) as conn:
             if self.pipe:
                 # a connection in pipeline mode can be used concurrently
@@ -337,10 +345,17 @@ class AsyncPostgresSaver(BasePostgresSaver):
             elif pipeline:
                 # a connection not in pipeline mode can only be used by one
                 # thread/coroutine at a time, so we acquire a lock
-                async with self.lock, conn.pipeline(), conn.cursor(
-                    binary=True, row_factory=dict_row
-                ) as cur:
-                    yield cur
+                if self.supports_pipeline:
+                    async with self.lock, conn.pipeline(), conn.cursor(
+                        binary=True, row_factory=dict_row
+                    ) as cur:
+                        yield cur
+                else:
+                    # Use connection's transaction context manager when pipeline mode not supported
+                    async with self.lock, conn.transaction(), conn.cursor(
+                        binary=True, row_factory=dict_row
+                    ) as cur:
+                        yield cur
             else:
                 async with self.lock, conn.cursor(
                     binary=True, row_factory=dict_row

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

@@ -133,6 +133,7 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
     INSERT_CHECKPOINT_WRITES_SQL = INSERT_CHECKPOINT_WRITES_SQL
 
     jsonplus_serde = JsonPlusSerializer()
+    supports_pipeline: bool
 
     def _load_checkpoint(
         self,

libs/checkpoint-postgres/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.2"
+version = "2.0.3"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 license = "MIT"

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

@@ -39,12 +39,13 @@ PendingWrite = Tuple[str, str, Any]
 class CheckpointMetadata(TypedDict, total=False):
     """Metadata associated with a checkpoint."""
 
-    source: Literal["input", "loop", "update"]
+    source: Literal["input", "loop", "update", "fork"]
     """The source of the checkpoint.
 
     - "input": The checkpoint was created from an input to invoke/stream/batch.
     - "loop": The checkpoint was created from inside the pregel loop.
     - "update": The checkpoint was created from a manual state update.
+    - "fork": The checkpoint was created as a copy of another checkpoint.
     """
     step: int
     """The step number of the checkpoint.

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

@@ -1,10 +1,14 @@
 import asyncio
+import logging
+import os
+import pickle
 import random
+import shutil
 from collections import defaultdict
-from contextlib import AbstractAsyncContextManager, AbstractContextManager
+from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
 from functools import partial
 from types import TracebackType
-from typing import Any, AsyncIterator, Dict, Iterator, Optional, Sequence, Tuple
+from typing import Any, AsyncIterator, Dict, Iterator, Optional, Sequence, Tuple, Type
 
 from langchain_core.runnables import RunnableConfig
 
@@ -20,6 +24,8 @@ from langgraph.checkpoint.base import (
 )
 from langgraph.checkpoint.serde.types import TASKS, ChannelProtocol
 
+logger = logging.getLogger(__name__)
+
 
 class MemorySaver(
     BaseCheckpointSaver[str], AbstractContextManager, AbstractAsyncContextManager
@@ -68,13 +74,18 @@ class MemorySaver(
         self,
         *,
         serde: Optional[SerializerProtocol] = None,
+        factory: Type[defaultdict] = defaultdict,
     ) -> None:
         super().__init__(serde=serde)
-        self.storage = defaultdict(lambda: defaultdict(dict))
-        self.writes = defaultdict(dict)
+        self.storage = factory(lambda: defaultdict(dict))
+        self.writes = factory(dict)
+        self.stack = ExitStack()
+        if factory is not defaultdict:
+            self.stack.enter_context(self.storage)  # type: ignore[arg-type]
+            self.stack.enter_context(self.writes)  # type: ignore[arg-type]
 
     def __enter__(self) -> "MemorySaver":
-        return self
+        return self.stack.__enter__()
 
     def __exit__(
         self,
@@ -82,10 +93,10 @@ class MemorySaver(
         exc_value: Optional[BaseException],
         traceback: Optional[TracebackType],
     ) -> Optional[bool]:
-        return
+        return self.stack.__exit__(exc_type, exc_value, traceback)
 
     async def __aenter__(self) -> "MemorySaver":
-        return self
+        return self.stack.__enter__()
 
     async def __aexit__(
         self,
@@ -93,7 +104,7 @@ class MemorySaver(
         __exc_value: Optional[BaseException],
         __traceback: Optional[TracebackType],
     ) -> Optional[bool]:
-        return
+        return self.stack.__exit__(__exc_type, __exc_value, __traceback)
 
     def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
         """Get a checkpoint tuple from the in-memory storage.
@@ -361,7 +372,7 @@ class MemorySaver(
             RunnableConfig: The updated config containing the saved writes' timestamp.
         """
         thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"]["checkpoint_ns"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
         checkpoint_id = config["configurable"]["checkpoint_id"]
         outer_key = (thread_id, checkpoint_ns, checkpoint_id)
         outer_writes_ = self.writes.get(outer_key)
@@ -478,3 +489,76 @@ class MemorySaver(
         next_v = current_v + 1
         next_h = random.random()
         return f"{next_v:032}.{next_h:016}"
+
+
+class PersistentDict(defaultdict):
+    """Persistent dictionary with an API compatible with shelve and anydbm.
+
+    The dict is kept in memory, so the dictionary operations run as fast as
+    a regular dictionary.
+
+    Write to disk is delayed until close or sync (similar to gdbm's fast mode).
+
+    Input file format is automatically discovered.
+    Output file format is selectable between pickle, json, and csv.
+    All three serialization formats are backed by fast C implementations.
+
+    Adapted from https://code.activestate.com/recipes/576642-persistent-dict-with-multiple-standard-file-format/
+
+    """
+
+    def __init__(self, *args: Any, filename: str, **kwds: Any) -> None:
+        self.flag = "c"  # r=readonly, c=create, or n=new
+        self.mode = None  # None or an octal triple like 0644
+        self.format = "pickle"  # 'csv', 'json', or 'pickle'
+        self.filename = filename
+        super().__init__(*args, **kwds)
+
+    def sync(self) -> None:
+        "Write dict to disk"
+        if self.flag == "r":
+            return
+        tempname = self.filename + ".tmp"
+        fileobj = open(tempname, "wb" if self.format == "pickle" else "w")
+        try:
+            self.dump(fileobj)
+        except Exception:
+            os.remove(tempname)
+            raise
+        finally:
+            fileobj.close()
+        shutil.move(tempname, self.filename)  # atomic commit
+        if self.mode is not None:
+            os.chmod(self.filename, self.mode)
+
+    def close(self) -> None:
+        self.sync()
+        self.clear()
+
+    def __enter__(self) -> "PersistentDict":
+        return self
+
+    def __exit__(self, *exc_info: Any) -> None:
+        self.close()
+
+    def dump(self, fileobj: Any) -> None:
+        if self.format == "pickle":
+            pickle.dump(dict(self), fileobj, 2)
+        else:
+            raise NotImplementedError("Unknown format: " + repr(self.format))
+
+    def load(self) -> None:
+        # try formats from most restrictive to least restrictive
+        if self.flag == "n":
+            return
+        with open(self.filename, "rb" if self.format == "pickle" else "r") as fileobj:
+            for loader in (pickle.load,):
+                fileobj.seek(0)
+                try:
+                    return self.update(loader(fileobj))
+                except EOFError:
+                    return
+                except Exception:
+                    logging.error(f"Failed to load file: {fileobj.name}")
+                    raise
+            raise ValueError("File not in a supported f ormat")

libs/checkpoint/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint"
-version = "2.0.3"
+version = "2.0.5"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 license = "MIT"

libs/checkpoint/tests/test_store.py

@@ -314,7 +314,7 @@ async def test_cannot_put_empty_namespace() -> None:
     assert store.get(("langgraph", "foo"), "bar") is None
 
     class MockAsyncBatchedStore(AsyncBatchedBaseStore):
-        def __init__(self):
+        def __init__(self) -> None:
             super().__init__()
             self._store = InMemoryStore()
 
@@ -340,13 +340,17 @@ async def test_cannot_put_empty_namespace() -> None:
         await async_store.aput(("langgraph", "foo"), "bar", doc)
 
     await async_store.aput(("foo", "langgraph", "foo"), "bar", doc)
-    assert (await async_store.aget(("foo", "langgraph", "foo"), "bar")).value == doc
+    val = await async_store.aget(("foo", "langgraph", "foo"), "bar")
+    assert val is not None
+    assert val.value == doc
     assert (await async_store.asearch(("foo", "langgraph", "foo")))[0].value == doc
     await async_store.adelete(("foo", "langgraph", "foo"), "bar")
     assert (await async_store.aget(("foo", "langgraph", "foo"), "bar")) is None
 
     await async_store.abatch([PutOp(("valid", "namespace"), "key", doc)])
-    assert (await async_store.aget(("valid", "namespace"), "key")).value == doc
+    val = await async_store.aget(("valid", "namespace"), "key")
+    assert val is not None
+    assert val.value == doc
     assert (await async_store.asearch(("valid", "namespace")))[0].value == doc
     await async_store.adelete(("valid", "namespace"), "key")
     assert (await async_store.aget(("valid", "namespace"), "key")) is None

libs/cli/README.md

@@ -1,10 +1,105 @@
-# langchain-cli
+# LangGraph CLI
 
-This package implements the official CLI for LangGraph API.
+The official command-line interface for LangGraph, providing tools to create, develop, and deploy LangGraph applications.
 
-## How to Test CLI Changes Locally
-These instructions are for CLI development and testing. Use the CLI examples to test CLI changes locally.
-1. Make changes to the CLI code.
-1. Navigate to the `libs/cli/examples`: `cd libs/cli/examples`
-1. Install CLI examples dependencies: `poetry install`
-1. Run/test CLI command (e.g. `langgraph build`).
+## Installation
+
+Install via pip:
+```bash
+pip install langgraph-cli
+```
+
+For development mode with hot reloading:
+```bash
+pip install "langgraph-cli[inmem]"
+```
+
+## Commands
+
+### `langgraph new` 🌱
+Create a new LangGraph project from a template
+```bash
+langgraph new [PATH] --template TEMPLATE_NAME
+```
+
+### `langgraph dev` 🏃‍♀️
+Run LangGraph API server in development mode with hot reloading
+```bash
+langgraph dev [OPTIONS]
+  --host TEXT                 Host to bind to (default: 127.0.0.1)
+  --port INTEGER             Port to bind to (default: 2024)
+  --no-reload               Disable auto-reload
+  --debug-port INTEGER      Enable remote debugging
+  --no-browser             Skip opening browser window
+  -c, --config FILE        Config file path (default: langgraph.json)
+```
+
+### `langgraph up` 🚀
+Launch LangGraph API server in Docker
+```bash
+langgraph up [OPTIONS]
+  -p, --port INTEGER        Port to expose (default: 8123)
+  --wait                   Wait for services to start
+  --watch                  Restart on file changes
+  --verbose               Show detailed logs
+  -c, --config FILE       Config file path
+  -d, --docker-compose    Additional services file
+```
+
+### `langgraph build`
+Build a Docker image for your LangGraph application
+```bash
+langgraph build -t IMAGE_TAG [OPTIONS]
+  --platform TEXT          Target platforms (e.g., linux/amd64,linux/arm64)
+  --pull / --no-pull      Use latest/local base image
+  -c, --config FILE       Config file path
+```
+
+### `langgraph dockerfile`
+Generate a Dockerfile for custom deployments
+```bash
+langgraph dockerfile SAVE_PATH [OPTIONS]
+  -c, --config FILE       Config file path
+```
+
+## Configuration
+
+The CLI uses a `langgraph.json` configuration file with these key settings:
+
+```json
+{
+  "dependencies": ["langchain_openai", "./your_package"],  // Required: Package dependencies
+  "graphs": {
+    "my_graph": "./your_package/file.py:graph"            // Required: Graph definitions
+  },
+  "env": "./.env",                                        // Optional: Environment variables
+  "python_version": "3.11",                               // Optional: Python version (3.11/3.12)
+  "pip_config_file": "./pip.conf",                        // Optional: pip configuration
+  "dockerfile_lines": []                                  // Optional: Additional Dockerfile commands
+}
+```
+
+See the [full documentation](https://langchain-ai.github.io/langgraph/docs/cloud/reference/cli.html) for detailed configuration options.
+
+## Development
+
+To develop the CLI itself:
+
+1. Clone the repository
+2. Navigate to the CLI directory: `cd libs/cli`
+3. Install development dependencies: `poetry install`
+4. Make your changes to the CLI code
+5. Test your changes:
+   ```bash
+   # Run CLI commands directly
+   poetry run langgraph --help
+   
+   # Or use the examples
+   cd examples
+   poetry install
+   poetry run langgraph dev  # or other commands
+   ```
+
+## License
+
+This project is licensed under the terms specified in the repository's LICENSE file.

libs/cli/langgraph_cli/cli.py

@@ -285,9 +285,11 @@ def _build(
             subp_exec(
                 "docker",
                 "pull",
-                f"{base_image}:{config_json['node_version']}"
-                if config_json.get("node_version")
-                else f"{base_image}:{config_json['python_version']}",
+                (
+                    f"{base_image}:{config_json['node_version']}"
+                    if config_json.get("node_version")
+                    else f"{base_image}:{config_json['python_version']}"
+                ),
                 verbose=True,
             )
         )
@@ -443,9 +445,11 @@ def dockerfile(save_path: str, config: pathlib.Path, add_docker_compose: bool) -
             langgraph_cli.config.config_to_docker(
                 config,
                 config_json,
-                "langchain/langgraphjs-api"
-                if config_json.get("node_version")
-                else "langchain/langgraph-api",
+                (
+                    "langchain/langgraphjs-api"
+                    if config_json.get("node_version")
+                    else "langchain/langgraph-api"
+                ),
             )
         )
     secho("✅ Created: Dockerfile", fg="green")
@@ -523,6 +527,97 @@ def new(path: Optional[str], template: Optional[str]) -> None:
     return create_new(path, template)
 
 
+@click.option(
+    "--host",
+    default="127.0.0.1",
+    help="Network interface to bind the development server to. Default 127.0.0.1 is recommended for security. Only use 0.0.0.0 in trusted networks",
+)
+@click.option(
+    "--port",
+    default=2024,
+    type=int,
+    help="Port number to bind the development server to. Example: langgraph dev --port 8000",
+)
+@click.option(
+    "--no-reload",
+    is_flag=True,
+    help="Disable automatic reloading when code changes are detected",
+)
+@click.option(
+    "--config",
+    type=click.Path(exists=True),
+    default="langgraph.json",
+    help="Path to configuration file declaring dependencies, graphs and environment variables",
+)
+@click.option(
+    "--n-jobs-per-worker",
+    default=None,
+    type=int,
+    help="Maximum number of concurrent jobs each worker process can handle. Default: 10",
+)
+@click.option(
+    "--no-browser",
+    is_flag=True,
+    help="Skip automatically opening the browser when the server starts",
+)
+@click.option(
+    "--debug-port",
+    default=None,
+    type=int,
+    help="Enable remote debugging by listening on specified port. Requires debugpy to be installed",
+)
+@cli.command(
+    "dev",
+    help="🏃‍♀️‍➡️ Run LangGraph API server in development mode with hot reloading and debugging support",
+)
+@log_command
+def dev(
+    host: str,
+    port: int,
+    no_reload: bool,
+    config: str,
+    n_jobs_per_worker: Optional[int],
+    no_browser: bool,
+    debug_port: Optional[int],
+):
+    """CLI entrypoint for running the LangGraph API server."""
+    try:
+        from langgraph_api.cli import run_server
+    except ImportError:
+        try:
+            import pkg_resources
+
+            pkg_resources.require("langgraph-api-inmem")
+        except (ImportError, pkg_resources.DistributionNotFound):
+            raise click.UsageError(
+                "Required package 'langgraph-api-inmem' is not installed.\n"
+                "Please install it with:\n\n"
+                '    pip install -U "langgraph-cli[inmem]"\n\n'
+                "If you're developing the langgraph-cli package locally, you can install in development mode:\n"
+                "    pip install -e ."
+            ) from None
+        raise click.UsageError(
+            "Could not import run_server. This likely means your installation is incomplete.\n"
+            "Please ensure langgraph-cli is installed with the 'inmem' extra: pip install -U \"langgraph-cli[inmem]\""
+        ) from None
+
+    import json
+
+    with open(config, encoding="utf-8") as f:
+        config_data = json.load(f)
+
+    graphs = config_data.get("graphs", {})
+    run_server(
+        host,
+        port,
+        not no_reload,
+        graphs,
+        n_jobs_per_worker=n_jobs_per_worker,
+        open_browser=not no_browser,
+        debug_port=debug_port,
+    )
+
+
 def prepare_args_and_stdin(
     *,
     capabilities: DockerCapabilities,
@@ -556,9 +651,11 @@ def prepare_args_and_stdin(
         config_path,
         config,
         watch=watch,
-        base_image="langchain/langgraphjs-api"
-        if config.get("node_version")
-        else "langchain/langgraph-api",
+        base_image=(
+            "langchain/langgraphjs-api"
+            if config.get("node_version")
+            else "langchain/langgraph-api"
+        ),
     )
     return args, stdin
 
@@ -585,9 +682,11 @@ def prepare(
             subp_exec(
                 "docker",
                 "pull",
-                f"langchain/langgraphjs-api:{config['node_version']}"
-                if config.get("node_version")
-                else f"langchain/langgraph-api:{config['python_version']}",
+                (
+                    f"langchain/langgraphjs-api:{config['node_version']}"
+                    if config.get("node_version")
+                    else f"langchain/langgraph-api:{config['python_version']}"
+                ),
                 verbose=verbose,
             )
         )

libs/cli/langgraph_cli/templates.py

@@ -85,7 +85,7 @@ def _choose_template() -> str:
         fg="green",
     )
     version_choice: int = click.prompt(
-        "Choose version (1 for Python 🐍, 2 for JS/TS 🌐)", type=int
+        "Choose language (1 for Python 🐍, 2 for JS/TS 🌐)", type=int
     )
 
     if version_choice == 1:

libs/cli/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-cli"
-version = "0.1.53"
+version = "0.1.55"
 description = "CLI for interacting with LangGraph API"
 authors = []
 license = "MIT"
@@ -14,6 +14,7 @@ langgraph = "langgraph_cli.cli:cli"
 [tool.poetry.dependencies]
 python = "^3.9.0,<4.0"
 click = "^8.1.7"
+langgraph-api-inmem = { version = ">=0.0.5,<0.1.0", optional = true, python = ">=3.11" }
 
 [tool.poetry.group.dev.dependencies]
 ruff = "^0.6.2"
@@ -24,6 +25,9 @@ pytest-mock = "^3.11.1"
 pytest-watch = "^4.2.0"
 mypy = "^1.10.0"
 
+[tool.poetry.extras]
+inmem = ["langgraph-api-inmem"]
+
 [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

libs/langgraph/langgraph/pregel/__init__.py

@@ -65,6 +65,7 @@ from langgraph.constants import (
     CONFIG_KEY_STREAM,
     CONFIG_KEY_STREAM_WRITER,
     CONFIG_KEY_TASK_ID,
+    END,
     ERROR,
     INPUT,
     INTERRUPT,
@@ -901,8 +902,8 @@ class Pregel(PregelProtocol):
             checkpoint,
             LoopProtocol(config=config, step=step + 1, stop=step + 2),
         ) as (channels, managed):
-            # no values, just clear all tasks
-            if values is None and as_node is None:
+            # no values as END, just clear all tasks
+            if values is None and as_node == END:
                 if saved is not None:
                     # tasks for this checkpoint
                     next_tasks = prepare_next_tasks(
@@ -955,6 +956,42 @@ class Pregel(PregelProtocol):
                 return patch_checkpoint_map(
                     next_config, saved.metadata if saved else None
                 )
+            # no values, copy checkpoint
+            if values is None and as_node is None:
+                next_checkpoint = create_checkpoint(checkpoint, None, step)
+                # copy checkpoint
+                next_config = checkpointer.put(
+                    checkpoint_config,
+                    next_checkpoint,
+                    {
+                        **checkpoint_metadata,
+                        "source": "update",
+                        "step": step + 1,
+                        "writes": {},
+                        "parents": saved.metadata.get("parents", {}) if saved else {},
+                    },
+                    {},
+                )
+                return patch_checkpoint_map(
+                    next_config, saved.metadata if saved else None
+                )
+            if values is None and as_node == "__copy__":
+                next_checkpoint = create_checkpoint(checkpoint, None, step)
+                # copy checkpoint
+                next_config = checkpointer.put(
+                    saved.parent_config or saved.config if saved else checkpoint_config,
+                    next_checkpoint,
+                    {
+                        **checkpoint_metadata,
+                        "source": "fork",
+                        "step": step + 1,
+                        "parents": saved.metadata.get("parents", {}) if saved else {},
+                    },
+                    {},
+                )
+                return patch_checkpoint_map(
+                    next_config, saved.metadata if saved else None
+                )
             # apply pending writes, if not on specific checkpoint
             if (
                 CONFIG_KEY_CHECKPOINT_ID not in config[CONF]
@@ -1144,7 +1181,7 @@ class Pregel(PregelProtocol):
             managed,
         ):
             # no values, just clear all tasks
-            if values is None and as_node is None:
+            if values is None and as_node == END:
                 if saved is not None:
                     # tasks for this checkpoint
                     next_tasks = prepare_next_tasks(
@@ -1197,6 +1234,42 @@ class Pregel(PregelProtocol):
                 return patch_checkpoint_map(
                     next_config, saved.metadata if saved else None
                 )
+            # no values, copy checkpoint
+            if values is None and as_node is None:
+                next_checkpoint = create_checkpoint(checkpoint, None, step)
+                # copy checkpoint
+                next_config = await checkpointer.aput(
+                    checkpoint_config,
+                    next_checkpoint,
+                    {
+                        **checkpoint_metadata,
+                        "source": "update",
+                        "step": step + 1,
+                        "writes": {},
+                        "parents": saved.metadata.get("parents", {}) if saved else {},
+                    },
+                    {},
+                )
+                return patch_checkpoint_map(
+                    next_config, saved.metadata if saved else None
+                )
+            if values is None and as_node == "__copy__":
+                next_checkpoint = create_checkpoint(checkpoint, None, step)
+                # copy checkpoint
+                next_config = await checkpointer.aput(
+                    saved.parent_config or saved.config if saved else checkpoint_config,
+                    next_checkpoint,
+                    {
+                        **checkpoint_metadata,
+                        "source": "fork",
+                        "step": step + 1,
+                        "parents": saved.metadata.get("parents", {}) if saved else {},
+                    },
+                    {},
+                )
+                return patch_checkpoint_map(
+                    next_config, saved.metadata if saved else None
+                )
             # apply pending writes, if not on specific checkpoint
             if (
                 CONFIG_KEY_CHECKPOINT_ID not in config[CONF]
@@ -1753,6 +1826,7 @@ class Pregel(PregelProtocol):
                 interrupt_before=interrupt_before_,
                 interrupt_after=interrupt_after_,
                 manager=run_manager,
+                debug=debug,
             ) as loop:
                 # create runner
                 runner = PregelRunner(

libs/langgraph/langgraph/pregel/debug.py

@@ -191,6 +191,14 @@ def map_debug_checkpoint(
                     "state": t.state,
                 }
                 if t.error
+                else {
+                    "id": t.id,
+                    "name": t.name,
+                    "result": t.result,
+                    "interrupts": tuple(asdict(i) for i in t.interrupts),
+                    "state": t.state,
+                }
+                if t.result
                 else {
                     "id": t.id,
                     "name": t.name,

libs/langgraph/langgraph/pregel/utils.py

@@ -48,7 +48,7 @@ def find_subgraph_pregel(candidate: Runnable) -> Optional[Runnable]:
                     nl.__self__ if hasattr(nl, "__self__") else nl
                     for nl in get_function_nonlocals(c.func)
                 )
-            if c.afunc is not None:
+            elif c.afunc is not None:
                 candidates.extend(
                     nl.__self__ if hasattr(nl, "__self__") else nl
                     for nl in get_function_nonlocals(c.afunc)

libs/langgraph/langgraph/utils/config.py

@@ -280,7 +280,10 @@ def ensure_config(*configs: Optional[RunnableConfig]) -> RunnableConfig:
             continue
         for k, v in config.items():
             if v is not None and k in CONFIG_KEYS:
-                empty[k] = v  # type: ignore[literal-required]
+                if k == CONF:
+                    empty[k] = v.copy()  # type: ignore[literal-required]
+                else:
+                    empty[k] = v  # type: ignore[literal-required]
         for k, v in config.items():
             if v is not None and k not in CONFIG_KEYS:
                 empty[CONF][k] = v

libs/langgraph/poetry.lock

@@ -1359,7 +1359,7 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.2"
+version = "2.0.4"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -1429,7 +1429,7 @@ url = "../checkpoint-sqlite"
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.35"
+version = "0.1.36"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -3425,4 +3425,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9.0,<4.0"
-content-hash = "f0b7800f90041227ba4d7b72482c70fabdbacc95e8a3a784fdebc88721728a91"
+content-hash = "9bf5668d3f70f3b77457906732404a6401583a5966f70a72ef10a68f2a5b27ad"

libs/langgraph/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.2.47"
+version = "0.2.52"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"
@@ -10,7 +10,7 @@ repository = "https://www.github.com/langchain-ai/langgraph"
 [tool.poetry.dependencies]
 python = ">=3.9.0,<4.0"
 langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14"
-langgraph-checkpoint = "^2.0.0"
+langgraph-checkpoint = "^2.0.4"
 langgraph-sdk = "^0.1.32"
 
 [tool.poetry.group.dev.dependencies]

libs/langgraph/tests/memory_assert.py

@@ -1,5 +1,8 @@
 import asyncio
+import os
+import tempfile
 from collections import defaultdict
+from functools import partial
 from typing import Any, Optional
 
 from langchain_core.runnables import RunnableConfig
@@ -12,7 +15,7 @@ from langgraph.checkpoint.base import (
     SerializerProtocol,
     copy_checkpoint,
 )
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import MemorySaver, PersistentDict
 
 
 class NoopSerializer(SerializerProtocol):
@@ -32,9 +35,13 @@ class MemorySaverAssertImmutable(MemorySaver):
         serde: Optional[SerializerProtocol] = None,
         put_sleep: Optional[float] = None,
     ) -> None:
-        super().__init__(serde=serde)
+        _, filename = tempfile.mkstemp()
+        super().__init__(
+            serde=serde, factory=partial(PersistentDict, filename=filename)
+        )
         self.storage_for_copies = defaultdict(lambda: defaultdict(dict))
         self.put_sleep = put_sleep
+        self.stack.callback(os.remove, filename)
 
     def put(
         self,

libs/langgraph/tests/test_pregel.py

@@ -8556,8 +8556,8 @@ def test_dynamic_interrupt(
         parent_config=[*tool_two.checkpointer.list(thread1, limit=2)][-1].config,
     )
     # clear the interrupt and next tasks
-    tool_two.update_state(thread1, None)
-    # interrupt is cleared, task will still run next
+    tool_two.update_state(thread1, None, as_node=END)
+    # interrupt and next tasks are cleared
     assert tool_two.get_state(thread1) == StateSnapshot(
         values={"my_key": "value ⛰️", "market": "DE"},
         next=(),
@@ -8575,6 +8575,167 @@ def test_dynamic_interrupt(
     )
 
 
+@pytest.mark.skipif(not FF_SEND_V2, reason="send v2 is not enabled")
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
+def test_copy_checkpoint(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
+
+    class State(TypedDict):
+        my_key: Annotated[str, operator.add]
+        market: str
+
+    def tool_one(s: State) -> State:
+        return {"my_key": " one"}
+
+    tool_two_node_count = 0
+
+    def tool_two_node(s: State) -> State:
+        nonlocal tool_two_node_count
+        tool_two_node_count += 1
+        if s["market"] == "DE":
+            answer = interrupt("Just because...")
+        else:
+            answer = " all good"
+        return {"my_key": answer}
+
+    def start(state: State) -> list[Union[Send, str]]:
+        return ["tool_two", Send("tool_one", state)]
+
+    tool_two_graph = StateGraph(State)
+    tool_two_graph.add_node("tool_two", tool_two_node, retry=RetryPolicy())
+    tool_two_graph.add_node("tool_one", tool_one)
+    tool_two_graph.set_conditional_entry_point(start)
+    tool_two = tool_two_graph.compile()
+
+    tracer = FakeTracer()
+    assert tool_two.invoke(
+        {"my_key": "value", "market": "DE"}, {"callbacks": [tracer]}
+    ) == {
+        "my_key": "value one",
+        "market": "DE",
+    }
+    assert tool_two_node_count == 1, "interrupts aren't retried"
+    assert len(tracer.runs) == 1
+    run = tracer.runs[0]
+    assert run.end_time is not None
+    assert run.error is None
+    assert run.outputs == {"market": "DE", "my_key": "value one"}
+
+    assert tool_two.invoke({"my_key": "value", "market": "US"}) == {
+        "my_key": "value one all good",
+        "market": "US",
+    }
+
+    tool_two = tool_two_graph.compile(checkpointer=checkpointer)
+
+    # missing thread_id
+    with pytest.raises(ValueError, match="thread_id"):
+        tool_two.invoke({"my_key": "value", "market": "DE"})
+
+    # flow: interrupt -> resume with answer
+    thread2 = {"configurable": {"thread_id": "2"}}
+    # stop when about to enter node
+    assert [
+        c for c in tool_two.stream({"my_key": "value ⛰️", "market": "DE"}, thread2)
+    ] == [
+        {
+            "tool_one": {"my_key": " one"},
+        },
+        {
+            "__interrupt__": (
+                Interrupt(
+                    value="Just because...",
+                    resumable=True,
+                    ns=[AnyStr("tool_two:")],
+                ),
+            )
+        },
+    ]
+    # resume with answer
+    assert [c for c in tool_two.stream(Command(resume=" my answer"), thread2)] == [
+        {"tool_two": {"my_key": " my answer"}},
+    ]
+
+    # flow: interrupt -> clear tasks
+    thread1 = {"configurable": {"thread_id": "1"}}
+    # stop when about to enter node
+    assert tool_two.invoke({"my_key": "value ⛰️", "market": "DE"}, thread1) == {
+        "my_key": "value ⛰️ one",
+        "market": "DE",
+    }
+    assert [c.metadata for c in tool_two.checkpointer.list(thread1)] == [
+        {
+            "parents": {},
+            "source": "loop",
+            "step": 0,
+            "writes": {"tool_one": {"my_key": " one"}},
+            "thread_id": "1",
+        },
+        {
+            "parents": {},
+            "source": "input",
+            "step": -1,
+            "writes": {"__start__": {"my_key": "value ⛰️", "market": "DE"}},
+            "thread_id": "1",
+        },
+    ]
+    assert tool_two.get_state(thread1) == StateSnapshot(
+        values={"my_key": "value ⛰️ one", "market": "DE"},
+        next=("tool_two",),
+        tasks=(
+            PregelTask(
+                AnyStr(),
+                "tool_two",
+                (PULL, "tool_two"),
+                interrupts=(
+                    Interrupt(
+                        value="Just because...",
+                        resumable=True,
+                        ns=[AnyStr("tool_two:")],
+                    ),
+                ),
+            ),
+        ),
+        config=tool_two.checkpointer.get_tuple(thread1).config,
+        created_at=tool_two.checkpointer.get_tuple(thread1).checkpoint["ts"],
+        metadata={
+            "parents": {},
+            "source": "loop",
+            "step": 0,
+            "writes": {"tool_one": {"my_key": " one"}},
+            "thread_id": "1",
+        },
+        parent_config=[*tool_two.checkpointer.list(thread1, limit=2)][-1].config,
+    )
+    # clear the interrupt and next tasks
+    tool_two.update_state(thread1, None)
+    # interrupt is cleared, next task is kept
+    assert tool_two.get_state(thread1) == StateSnapshot(
+        values={"my_key": "value ⛰️ one", "market": "DE"},
+        next=("tool_two",),
+        tasks=(
+            PregelTask(
+                AnyStr(),
+                "tool_two",
+                (PULL, "tool_two"),
+                interrupts=(),
+            ),
+        ),
+        config=tool_two.checkpointer.get_tuple(thread1).config,
+        created_at=tool_two.checkpointer.get_tuple(thread1).checkpoint["ts"],
+        metadata={
+            "parents": {},
+            "source": "update",
+            "step": 1,
+            "writes": {},
+            "thread_id": "1",
+        },
+        parent_config=[*tool_two.checkpointer.list(thread1, limit=2)][-1].config,
+    )
+
+
 @pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
 def test_start_branch_then(
     snapshot: SnapshotAssertion, request: pytest.FixtureRequest, checkpointer_name: str

libs/langgraph/tests/test_pregel_async.py

@@ -404,7 +404,7 @@ async def test_dynamic_interrupt(checkpointer_name: str) -> None:
         )
 
         # clear the interrupt and next tasks
-        await tool_two.aupdate_state(thread1, None)
+        await tool_two.aupdate_state(thread1, None, as_node=END)
         # interrupt is cleared, as well as the next tasks
         tup = await tool_two.checkpointer.aget_tuple(thread1)
         assert await tool_two.aget_state(thread1) == StateSnapshot(
@@ -426,6 +426,181 @@ async def test_dynamic_interrupt(checkpointer_name: str) -> None:
         )
 
 
+@pytest.mark.skipif(not FF_SEND_V2, reason="send v2 is not enabled")
+@pytest.mark.skipif(
+    sys.version_info < (3, 11),
+    reason="Python 3.11+ is required for async contextvars support",
+)
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
+async def test_copy_checkpoint(checkpointer_name: str) -> None:
+    class State(TypedDict):
+        my_key: Annotated[str, operator.add]
+        market: str
+
+    def tool_one(s: State) -> State:
+        return {"my_key": " one"}
+
+    tool_two_node_count = 0
+
+    def tool_two_node(s: State) -> State:
+        nonlocal tool_two_node_count
+        tool_two_node_count += 1
+        if s["market"] == "DE":
+            answer = interrupt("Just because...")
+        else:
+            answer = " all good"
+        return {"my_key": answer}
+
+    def start(state: State) -> list[Union[Send, str]]:
+        return ["tool_two", Send("tool_one", state)]
+
+    tool_two_graph = StateGraph(State)
+    tool_two_graph.add_node("tool_two", tool_two_node, retry=RetryPolicy())
+    tool_two_graph.add_node("tool_one", tool_one)
+    tool_two_graph.set_conditional_entry_point(start)
+    tool_two = tool_two_graph.compile()
+
+    tracer = FakeTracer()
+    assert await tool_two.ainvoke(
+        {"my_key": "value", "market": "DE"}, {"callbacks": [tracer]}
+    ) == {
+        "my_key": "value one",
+        "market": "DE",
+    }
+    assert tool_two_node_count == 1, "interrupts aren't retried"
+    assert len(tracer.runs) == 1
+    run = tracer.runs[0]
+    assert run.end_time is not None
+    assert run.error is None
+    assert run.outputs == {"market": "DE", "my_key": "value one"}
+
+    assert await tool_two.ainvoke({"my_key": "value", "market": "US"}) == {
+        "my_key": "value one all good",
+        "market": "US",
+    }
+
+    async with awith_checkpointer(checkpointer_name) as checkpointer:
+        tool_two = tool_two_graph.compile(checkpointer=checkpointer)
+
+        # missing thread_id
+        with pytest.raises(ValueError, match="thread_id"):
+            await tool_two.ainvoke({"my_key": "value", "market": "DE"})
+
+        # flow: interrupt -> resume with answer
+        thread2 = {"configurable": {"thread_id": "2"}}
+        # stop when about to enter node
+        assert [
+            c
+            async for c in tool_two.astream(
+                {"my_key": "value ⛰️", "market": "DE"}, thread2
+            )
+        ] == [
+            {
+                "tool_one": {"my_key": " one"},
+            },
+            {
+                "__interrupt__": (
+                    Interrupt(
+                        value="Just because...",
+                        resumable=True,
+                        ns=[AnyStr("tool_two:")],
+                    ),
+                )
+            },
+        ]
+        # resume with answer
+        assert [
+            c async for c in tool_two.astream(Command(resume=" my answer"), thread2)
+        ] == [
+            {"tool_two": {"my_key": " my answer"}},
+        ]
+
+        # flow: interrupt -> clear tasks
+        thread1 = {"configurable": {"thread_id": "1"}}
+        # stop when about to enter node
+        assert await tool_two.ainvoke(
+            {"my_key": "value ⛰️", "market": "DE"}, thread1
+        ) == {
+            "my_key": "value ⛰️ one",
+            "market": "DE",
+        }
+        assert [c.metadata async for c in tool_two.checkpointer.alist(thread1)] == [
+            {
+                "parents": {},
+                "source": "loop",
+                "step": 0,
+                "writes": {"tool_one": {"my_key": " one"}},
+                "thread_id": "1",
+            },
+            {
+                "parents": {},
+                "source": "input",
+                "step": -1,
+                "writes": {"__start__": {"my_key": "value ⛰️", "market": "DE"}},
+                "thread_id": "1",
+            },
+        ]
+        tup = await tool_two.checkpointer.aget_tuple(thread1)
+        assert await tool_two.aget_state(thread1) == StateSnapshot(
+            values={"my_key": "value ⛰️ one", "market": "DE"},
+            next=("tool_two",),
+            tasks=(
+                PregelTask(
+                    AnyStr(),
+                    "tool_two",
+                    (PULL, "tool_two"),
+                    interrupts=(
+                        Interrupt(
+                            value="Just because...",
+                            resumable=True,
+                            ns=[AnyStr("tool_two:")],
+                        ),
+                    ),
+                ),
+            ),
+            config=tup.config,
+            created_at=tup.checkpoint["ts"],
+            metadata={
+                "parents": {},
+                "source": "loop",
+                "step": 0,
+                "writes": {"tool_one": {"my_key": " one"}},
+                "thread_id": "1",
+            },
+            parent_config=[
+                c async for c in tool_two.checkpointer.alist(thread1, limit=2)
+            ][-1].config,
+        )
+        # clear the interrupt and next tasks
+        await tool_two.aupdate_state(thread1, None)
+        # interrupt is cleared, next task is kept
+        tup = await tool_two.checkpointer.aget_tuple(thread1)
+        assert await tool_two.aget_state(thread1) == StateSnapshot(
+            values={"my_key": "value ⛰️ one", "market": "DE"},
+            next=("tool_two",),
+            tasks=(
+                PregelTask(
+                    AnyStr(),
+                    "tool_two",
+                    (PULL, "tool_two"),
+                    interrupts=(),
+                ),
+            ),
+            config=tup.config,
+            created_at=tup.checkpoint["ts"],
+            metadata={
+                "parents": {},
+                "source": "update",
+                "step": 1,
+                "writes": {},
+                "thread_id": "1",
+            },
+            parent_config=[
+                c async for c in tool_two.checkpointer.alist(thread1, limit=2)
+            ][-1].config,
+        )
+
+
 @pytest.mark.skipif(
     sys.version_info < (3, 11),
     reason="Python 3.11+ is required for async contextvars support",

libs/sdk-js/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/langgraph-sdk",
-  "version": "0.0.23",
+  "version": "0.0.26",
   "description": "Client library for interacting with the LangGraph API",
   "type": "module",
   "packageManager": "yarn@1.22.19",

libs/sdk-js/src/client.ts

@@ -34,6 +34,35 @@ import {
 } from "./types.js";
 import { mergeSignals } from "./utils/signals.js";
 
+/**
+ * Get the API key from the environment.
+ * Precedence:
+ *   1. explicit argument
+ *   2. LANGGRAPH_API_KEY
+ *   3. LANGSMITH_API_KEY
+ *   4. LANGCHAIN_API_KEY
+ *
+ * @param apiKey - Optional API key provided as an argument
+ * @returns The API key if found, otherwise undefined
+ */
+export function getApiKey(apiKey?: string): string | undefined {
+  if (apiKey) {
+    return apiKey;
+  }
+
+  const prefixes = ["LANGGRAPH", "LANGSMITH", "LANGCHAIN"];
+
+  for (const prefix of prefixes) {
+    const envKey = process.env[`${prefix}_API_KEY`];
+    if (envKey) {
+      // Remove surrounding quotes
+      return envKey.trim().replace(/^["']|["']$/g, "");
+    }
+  }
+
+  return undefined;
+}
+
 interface ClientConfig {
   apiUrl?: string;
   apiKey?: string;
@@ -62,10 +91,12 @@ class BaseClient {
 
     // default limit being capped by Chrome
     // https://github.com/nodejs/undici/issues/1373
-    this.apiUrl = config?.apiUrl || "http://localhost:8123";
+    // Regex to remove trailing slash, if present
+    this.apiUrl = config?.apiUrl?.replace(/\/$/, "") || "http://localhost:8123";
     this.defaultHeaders = config?.defaultHeaders || {};
-    if (config?.apiKey != null) {
-      this.defaultHeaders["X-Api-Key"] = config.apiKey;
+    const apiKey = getApiKey(config?.apiKey);
+    if (apiKey) {
+      this.defaultHeaders["X-Api-Key"] = apiKey;
     }
   }
 
@@ -693,6 +724,7 @@ export class RunsClient extends BaseClient {
   }> {
     const json: Record<string, any> = {
       input: payload?.input,
+      command: payload?.command,
       config: payload?.config,
       metadata: payload?.metadata,
       stream_mode: payload?.streamMode,
@@ -780,6 +812,7 @@ export class RunsClient extends BaseClient {
   ): Promise<Run> {
     const json: Record<string, any> = {
       input: payload?.input,
+      command: payload?.command,
       config: payload?.config,
       metadata: payload?.metadata,
       assistant_id: assistantId,
@@ -849,6 +882,7 @@ export class RunsClient extends BaseClient {
   ): Promise<ThreadState["values"]> {
     const json: Record<string, any> = {
       input: payload?.input,
+      command: payload?.command,
       config: payload?.config,
       metadata: payload?.metadata,
       assistant_id: assistantId,

libs/sdk-js/src/index.ts

@@ -10,10 +10,11 @@ export type {
   Metadata,
   Run,
   Thread,
+  ThreadTask,
   ThreadState,
   ThreadStatus,
   Cron,
   Checkpoint,
 } from "./schema.js";
 
-export type { OnConflictBehavior } from "./types.js";
+export type { OnConflictBehavior, Command } from "./types.js";

libs/sdk-js/src/schema.ts

@@ -208,8 +208,14 @@ export interface ThreadState<ValuesType = DefaultValues> {
 export interface ThreadTask {
   id: string;
   name: string;
+  result?: unknown;
   error: Optional<string>;
-  interrupts: Array<{ value: unknown; when: "during" }>;
+  interrupts: Array<{
+    value: unknown;
+    when: "during";
+    resumable: boolean;
+    ns?: string[];
+  }>;
   checkpoint: Optional<Checkpoint>;
   state: Optional<ThreadState>;
 }

libs/sdk-js/src/types.ts

@@ -23,6 +23,28 @@ export type StreamEvent =
   | "messages/complete"
   | (string & {});
 
+export interface Send {
+  node: string;
+  input: Record<string, unknown> | null;
+}
+
+export interface Command {
+  /**
+   * An object to update the thread state with.
+   */
+  update?: Record<string, unknown>;
+
+  /**
+   * The value to return from an `interrupt` function call.
+   */
+  resume?: unknown;
+
+  /**
+   * A single, or array of `Send` commands to trigger nodes.
+   */
+  send?: Send | Send[];
+}
+
 interface RunsInvokePayload {
   /**
    * Input to the run. Pass `null` to resume from the current state of the thread.
@@ -107,6 +129,11 @@ interface RunsInvokePayload {
    * Behavior if the specified run doesn't exist. Defaults to "reject".
    */
   ifNotExists?: "create" | "reject";
+
+  /**
+   * One or more commands to invoke the graph with.
+   */
+  command?: Command;
 }
 
 export interface RunsStreamPayload extends RunsInvokePayload {

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 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -390,6 +390,59 @@ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphi
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
+[[package]]
+name = "autogen"
+version = "0.3.2"
+description = "A programming framework for agentic AI"
+optional = false
+python-versions = "<3.13,>=3.8"
+files = [
+    {file = "autogen-0.3.2-py3-none-any.whl", hash = "sha256:e37a9df0ad84cde3429ec63298b8e9eb4e6306a28eec2627171e14b9a61ea64d"},
+    {file = "autogen-0.3.2.tar.gz", hash = "sha256:9f8a1170ac2e5a1fc9efc3cfa6e23261dd014db97b17c8c416f97ee14951bc7b"},
+]
+
+[package.dependencies]
+diskcache = "*"
+docker = "*"
+flaml = "*"
+numpy = ">=1.17.0,<2"
+openai = ">=1.3"
+packaging = "*"
+pydantic = ">=1.10,<2.6.0 || >2.6.0,<3"
+python-dotenv = "*"
+termcolor = "*"
+tiktoken = "*"
+
+[package.extras]
+anthropic = ["anthropic (>=0.23.1)"]
+autobuild = ["chromadb", "huggingface-hub", "pysqlite3", "sentence-transformers"]
+bedrock = ["boto3 (>=1.34.149)"]
+blendsearch = ["flaml[blendsearch]"]
+cerebras = ["cerebras-cloud-sdk (>=1.0.0)"]
+cohere = ["cohere (>=5.5.8)"]
+cosmosdb = ["azure-cosmos (>=4.2.0)"]
+gemini = ["google-auth", "google-cloud-aiplatform", "google-generativeai (>=0.5,<1)", "pillow", "pydantic"]
+graph = ["matplotlib", "networkx"]
+graph-rag-falkor-db = ["graphrag-sdk"]
+groq = ["groq (>=0.9.0)"]
+jupyter-executor = ["ipykernel (>=6.29.0)", "jupyter-client (>=8.6.0)", "jupyter-kernel-gateway", "requests", "websocket-client"]
+lmm = ["pillow", "replicate"]
+long-context = ["llmlingua (<0.3)"]
+mathchat = ["pydantic (==1.10.9)", "sympy", "wolframalpha"]
+mistral = ["mistralai (>=1.0.1)"]
+ollama = ["fix-busted-json (>=0.0.18)", "ollama (>=0.3.3)"]
+redis = ["redis"]
+retrievechat = ["beautifulsoup4", "chromadb (==0.5.3)", "ipython", "markdownify", "protobuf (==4.25.3)", "pypdf", "sentence-transformers"]
+retrievechat-mongodb = ["beautifulsoup4", "chromadb (==0.5.3)", "ipython", "markdownify", "protobuf (==4.25.3)", "pymongo (>=4.0.0)", "pypdf", "sentence-transformers"]
+retrievechat-pgvector = ["beautifulsoup4", "chromadb (==0.5.3)", "ipython", "markdownify", "pgvector (>=0.2.5)", "protobuf (==4.25.3)", "psycopg (>=3.1.18)", "pypdf", "sentence-transformers"]
+retrievechat-qdrant = ["beautifulsoup4", "chromadb (==0.5.3)", "fastembed (>=0.3.1)", "ipython", "markdownify", "protobuf (==4.25.3)", "pypdf", "qdrant-client", "sentence-transformers"]
+teachable = ["chromadb"]
+test = ["ipykernel", "nbconvert", "nbformat", "pandas", "pre-commit", "pytest (>=6.1.1,<8)", "pytest-asyncio", "pytest-cov (>=5)"]
+together = ["together (>=1.2)"]
+types = ["ipykernel (>=6.29.0)", "jupyter-client (>=8.6.0)", "jupyter-kernel-gateway", "mypy (==1.9.0)", "pytest (>=6.1.1,<8)", "requests", "websocket-client"]
+websockets = ["websockets (>=12.0,<13)"]
+websurfer = ["beautifulsoup4", "markdownify", "pathvalidate", "pdfminer.six"]
+
 [[package]]
 name = "babel"
 version = "2.16.0"
@@ -1135,6 +1188,17 @@ wrapt = ">=1.10,<2"
 [package.extras]
 dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
 
+[[package]]
+name = "diskcache"
+version = "5.6.3"
+description = "Disk Cache -- Disk and file backed persistent cache."
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "diskcache-5.6.3-py3-none-any.whl", hash = "sha256:5e31b2d5fbad117cc363ebaf6b689474db18a1f6438bc82358b024abd4c2ca19"},
+    {file = "diskcache-5.6.3.tar.gz", hash = "sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc"},
+]
+
 [[package]]
 name = "distro"
 version = "1.9.0"
@@ -1166,6 +1230,28 @@ idna = ["idna (>=3.6)"]
 trio = ["trio (>=0.23)"]
 wmi = ["wmi (>=1.5.1)"]
 
+[[package]]
+name = "docker"
+version = "7.1.0"
+description = "A Python library for the Docker Engine API."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "docker-7.1.0-py3-none-any.whl", hash = "sha256:c96b93b7f0a746f9e77d325bcfb87422a3d8bd4f03136ae8a85b37f1898d5fc0"},
+    {file = "docker-7.1.0.tar.gz", hash = "sha256:ad8c70e6e3f8926cb8a92619b832b4ea5299e2831c14284663184e200546fa6c"},
+]
+
+[package.dependencies]
+pywin32 = {version = ">=304", markers = "sys_platform == \"win32\""}
+requests = ">=2.26.0"
+urllib3 = ">=1.26.0"
+
+[package.extras]
+dev = ["coverage (==7.2.7)", "pytest (==7.4.2)", "pytest-cov (==4.1.0)", "pytest-timeout (==2.1.0)", "ruff (==0.1.8)"]
+docs = ["myst-parser (==0.18.0)", "sphinx (==5.1.1)"]
+ssh = ["paramiko (>=2.4.3)"]
+websockets = ["websocket-client (>=1.3.0)"]
+
 [[package]]
 name = "durationpy"
 version = "0.7"
@@ -1271,6 +1357,43 @@ httpx-sse = "*"
 Pillow = "*"
 pydantic = "*"
 
+[[package]]
+name = "flaml"
+version = "2.3.2"
+description = "A fast library for automated machine learning and tuning"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "FLAML-2.3.2-py3-none-any.whl", hash = "sha256:1ee6e8e76bf1d741b4da41e2a2a8c0638b36d90b0f60aac323b5568f54dcb9e7"},
+    {file = "flaml-2.3.2.tar.gz", hash = "sha256:4a1ec289ddaec36850cfc66f6fb335b8521df49ea31f6adb54ea63a5cebb6865"},
+]
+
+[package.dependencies]
+NumPy = ">=1.17"
+
+[package.extras]
+autogen = ["diskcache", "openai (==0.27.8)", "termcolor"]
+automl = ["lightgbm (>=2.3.1)", "pandas (>=1.1.4)", "scikit-learn (>=1.0.0)", "scipy (>=1.4.1)", "xgboost (>=0.90,<3.0.0)"]
+autozero = ["packaging", "pandas", "scikit-learn"]
+azureml = ["azureml-mlflow"]
+benchmark = ["catboost (>=0.26)", "pandas (==1.1.4)", "psutil (==5.8.0)", "xgboost (==1.3.3)"]
+blendsearch = ["optuna (>=2.8.0,<=3.6.1)", "packaging"]
+catboost = ["catboost (>=0.26,<1.2)", "catboost (>=0.26,<=1.2.5)"]
+forecast = ["hcrystalball (==0.1.10)", "holidays (<0.14)", "prophet (>=1.0.1)", "pytorch-forecasting (>=0.9.0)", "pytorch-lightning (==1.9.0)", "statsmodels (>=0.12.2)", "tensorboardX (==2.6)"]
+hf = ["datasets", "nltk (<=3.8.1)", "rouge-score", "seqeval", "transformers[torch] (==4.26)"]
+mathchat = ["diskcache", "openai (==0.27.8)", "pydantic (==1.10.9)", "sympy", "termcolor", "wolframalpha"]
+nlp = ["datasets", "nltk (<=3.8.1)", "rouge-score", "seqeval", "transformers[torch] (==4.26)"]
+nni = ["nni"]
+notebook = ["jupyter"]
+openai = ["diskcache", "openai (==0.27.8)"]
+ray = ["ray[tune] (>=1.13,<2.0)"]
+retrievechat = ["chromadb", "diskcache", "openai (==0.27.8)", "sentence-transformers", "termcolor", "tiktoken"]
+spark = ["joblib (<=1.3.2)", "joblibspark (>=0.5.0)", "pyspark (>=3.2.0)"]
+synapse = ["joblibspark (>=0.5.0)", "optuna (>=2.8.0,<=3.6.1)", "pyspark (>=3.2.0)"]
+test = ["catboost (>=0.26)", "catboost (>=0.26,<1.2)", "coverage (>=5.3)", "dataclasses", "datasets", "dill", "hcrystalball (==0.1.10)", "ipykernel", "joblib (<=1.3.2)", "joblibspark (>=0.5.0)", "jupyter", "lightgbm (>=2.3.1)", "mlflow (==2.15.1)", "nbconvert", "nbformat", "nltk (<=3.8.1)", "openml", "optuna (>=2.8.0,<=3.6.1)", "packaging", "pandas (>=1.1.4)", "pandas (>=1.1.4,<2.0.0)", "pre-commit", "psutil (==5.8.0)", "pydantic (==1.10.9)", "pytest (>=6.1.1)", "pytorch-forecasting (>=0.9.0,<=0.10.1)", "pytorch-lightning (<1.9.1)", "requests (<2.29.0)", "rgf-python", "rouge-score", "scikit-learn (>=1.0.0)", "scipy (>=1.4.1)", "seqeval", "statsmodels (>=0.12.2)", "sympy", "tensorboardX (==2.6)", "thop", "torch", "torchvision", "transformers[torch] (==4.26)", "wolframalpha", "xgboost (>=0.90,<2.0.0)"]
+ts-forecast = ["hcrystalball (==0.1.10)", "holidays (<0.14)", "prophet (>=1.0.1)", "statsmodels (>=0.12.2)"]
+vw = ["scikit-learn", "vowpalwabbit (>=8.10.0,<9.0.0)"]
+
 [[package]]
 name = "flatbuffers"
 version = "24.3.25"
@@ -2912,7 +3035,7 @@ langchain-core = ">=0.3.0,<0.4.0"
 
 [[package]]
 name = "langgraph"
-version = "0.2.34"
+version = "0.2.52"
 description = "Building stateful, multi-actor applications with LLMs"
 optional = false
 python-versions = ">=3.9.0,<4.0"
@@ -2920,8 +3043,9 @@ files = []
 develop = true
 
 [package.dependencies]
-langchain-core = ">=0.2.39,<0.4"
-langgraph-checkpoint = "^2.0.0"
+langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14"
+langgraph-checkpoint = "^2.0.4"
+langgraph-sdk = "^0.1.32"
 
 [package.source]
 type = "directory"
@@ -2929,7 +3053,7 @@ url = "libs/langgraph"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.1"
+version = "2.0.5"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -2946,7 +3070,7 @@ url = "libs/checkpoint"
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.1"
+version = "2.0.3"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -2954,7 +3078,7 @@ files = []
 develop = true
 
 [package.dependencies]
-langgraph-checkpoint = "^2.0.0"
+langgraph-checkpoint = "^2.0.2"
 orjson = ">=3.10.1"
 psycopg = "^3.0.0"
 psycopg-pool = "^3.0.0"
@@ -2965,7 +3089,7 @@ url = "libs/checkpoint-postgres"
 
 [[package]]
 name = "langgraph-checkpoint-sqlite"
-version = "2.0.0"
+version = "2.0.1"
 description = "Library with a SQLite implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0"
@@ -2974,7 +3098,7 @@ develop = true
 
 [package.dependencies]
 aiosqlite = "^0.20.0"
-langgraph-checkpoint = "^2.0.0"
+langgraph-checkpoint = "^2.0.2"
 
 [package.source]
 type = "directory"
@@ -2982,7 +3106,7 @@ url = "libs/checkpoint-sqlite"
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.32"
+version = "0.1.36"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -4974,6 +5098,7 @@ description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs
 optional = false
 python-versions = ">=3.8"
 files = [
+    {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
     {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
 ]
 
@@ -4984,6 +5109,7 @@ description = "A collection of ASN.1-based protocols modules"
 optional = false
 python-versions = ">=3.8"
 files = [
+    {file = "pyasn1_modules-0.4.1-py3-none-any.whl", hash = "sha256:49bfa96b45a292b711e986f222502c1c9a5e1f4e568fc30e2574a6c7d07838fd"},
     {file = "pyasn1_modules-0.4.1.tar.gz", hash = "sha256:c28e2dbf9c06ad61c71a075c7e0f9fd0f1b0bb2d2ad4377f240d33ac2ab60a7c"},
 ]
 
@@ -6043,6 +6169,11 @@ files = [
     {file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f60021ec1574e56632be2a36b946f8143bf4e5e6af4a06d85281adc22938e0dd"},
     {file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:394397841449853c2290a32050382edaec3da89e35b3e03d6cc966aebc6a8ae6"},
     {file = "scikit_learn-1.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:57cc1786cfd6bd118220a92ede80270132aa353647684efa385a74244a41e3b1"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e9a702e2de732bbb20d3bad29ebd77fc05a6b427dc49964300340e4c9328b3f5"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:b0768ad641981f5d3a198430a1d31c3e044ed2e8a6f22166b4d546a5116d7908"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:178ddd0a5cb0044464fc1bfc4cca5b1833bfc7bb022d70b05db8530da4bb3dd3"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f7284ade780084d94505632241bf78c44ab3b6f1e8ccab3d2af58e0e950f9c12"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-win_amd64.whl", hash = "sha256:b7b0f9a0b1040830d38c39b91b3a44e1b643f4b36e36567b80b7c6bd2202a27f"},
     {file = "scikit_learn-1.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:757c7d514ddb00ae249832fe87100d9c73c6ea91423802872d9e74970a0e40b9"},
     {file = "scikit_learn-1.5.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:52788f48b5d8bca5c0736c175fa6bdaab2ef00a8f536cda698db61bd89c551c1"},
     {file = "scikit_learn-1.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:643964678f4b5fbdc95cbf8aec638acc7aa70f5f79ee2cdad1eec3df4ba6ead8"},
@@ -6362,6 +6493,20 @@ files = [
 doc = ["reno", "sphinx"]
 test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
+[[package]]
+name = "termcolor"
+version = "2.5.0"
+description = "ANSI color formatting for output in terminal"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "termcolor-2.5.0-py3-none-any.whl", hash = "sha256:37b17b5fc1e604945c2642c872a3764b5d547a48009871aea3edd3afa180afb8"},
+    {file = "termcolor-2.5.0.tar.gz", hash = "sha256:998d8d27da6d48442e8e1f016119076b690d962507531df4890fcd2db2ef8a6f"},
+]
+
+[package.extras]
+tests = ["pytest", "pytest-cov"]
+
 [[package]]
 name = "terminado"
 version = "0.18.1"
@@ -7331,4 +7476,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "738e69cf406b140217cc8c3c0f2ccb5c2027d4701bcf2a347ba4b107f700ab2a"
+content-hash = "776ee42630769f08e3896338f18ec81830166695d32d2208dc31dedb22d3b22d"

pyproject.toml

@@ -54,6 +54,7 @@ motor = "^3.5.1"
 grandalf = "^0.8"
 pyppeteer = "^2.0.0"
 networkx = "^3.3"
+autogen = { version = "^0.3.0", python = "<3.13,>=3.8" }
 
 [tool.poetry.group.test]
 optional = true