Fix broken code

- Make sure that we're calling `getType` instead of deprecated `_getType()`
- Make sure that we're referencing the proper camelCase symbols instead of snake_case symbols
- Make sure that we're using proper links to the API reference
- Make sure to chain calls of StateGraph methods
- Make sure to use Zod state in all snippets

.github/ISSUE_TEMPLATE/privileged.yml

@@ -23,3 +23,7 @@ body:
     attributes:
       label: Issue Content
       description: Add the content of the issue here.
+  - type: markdown
+    attributes:
+      value: |
+        Community members should **NOT** work on Privileged issues unless these issues have been explicitly marked with a "help-wanted" tag.

.github/workflows/ci.yml

@@ -3,7 +3,7 @@ name: CI
 
 on:
   push:
-    branches: [main]
+    branches: [main, v1]
   pull_request:
 
 permissions:

.github/workflows/codespell.yml

@@ -34,7 +34,7 @@
             id: extract_ignore_words
     
           - name: Codespell
-            uses: codespell-project/actions-codespell@v2.0
+            uses: codespell-project/actions-codespell@v2.1
             with:
               skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map'
               ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}

.github/workflows/deploy_docs.yml

@@ -35,16 +35,7 @@ jobs:
         with:
           filter: "docs/docs/**"
 
-  # TODO: Uncomment this to run on PRs
-  # run-changed-notebooks:
-  #   needs: get-changed-files
-  #   uses: ./.github/workflows/run_notebooks.yml
-  #   secrets: inherit
-  #   with:
-  #     changed-files: ${{ needs.get-changed-files.outputs.changed-files }}
-
   deploy:
-    # needs: run-changed-notebooks
     runs-on: ubuntu-latest
     timeout-minutes: 10 # Job will be cancelled if it runs for more than 10 minutes
     env:

.github/workflows/pr_lint.yml

@@ -39,6 +39,7 @@ jobs:
             scheduler-kafka
             sdk-py
             docs
+            ci
           requireScope: false
           ignoreLabels: |
             ignore-lint-pr-title

.github/workflows/release.yml

@@ -137,7 +137,9 @@ jobs:
     needs:
       - build
       - release-notes
-    permissions: write-all
+    permissions:
+      contents: read
+      id-token: write
     uses: ./.github/workflows/_test_release.yml
     with:
       working-directory: ${{ inputs.working-directory }}

docs/Makefile

@@ -15,8 +15,8 @@ build-prebuilt:
 	fi
 	uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python
 
-build-docs: build-prebuilt
-	uv run python -m mkdocs build --clean -f mkdocs.yml --strict
+build-docs: build-typedoc build-prebuilt
+	TARGET_LANGUAGE=python uv run python -m mkdocs build --clean -f mkdocs.yml --strict
 
 llms-text:
 	uv run python -m _scripts.generate_llms_text docs/llms-full.txt

docs/_scripts/notebook_hooks.py

@@ -310,6 +310,12 @@ def _highlight_code_blocks(markdown: str) -> str:
     return markdown
 
 
+TARGET_LANGUAGE = os.environ.get("TARGET_LANGUAGE", "python")
+
+if TARGET_LANGUAGE not in {"python", "js"}:
+    raise ValueError(f"TARGET_LANGUAGE must be 'python' or 'js', got {TARGET_LANGUAGE}")
+
+
 def _on_page_markdown_with_config(
     markdown: str,
     page: Page,
@@ -332,16 +338,15 @@ def _on_page_markdown_with_config(
     markdown = _highlight_code_blocks(markdown)
 
     # Apply conditional rendering for code blocks
-    target_language = kwargs.get("target_language", "python")
-    markdown = _apply_conditional_rendering(markdown, target_language)
-    if target_language == "js":
+    markdown = _apply_conditional_rendering(markdown, TARGET_LANGUAGE)
+    if TARGET_LANGUAGE == "js":
         markdown = _resolve_cross_references(markdown, JS_LINK_MAP)
-    elif target_language == "python":
+    elif TARGET_LANGUAGE == "python":
         # Via a dedicated plugin
         pass
     else:
         raise ValueError(
-            f"Unsupported target language: {target_language}. "
+            f"Unsupported target language: {TARGET_LANGUAGE}. "
             "Supported languages are 'python' and 'js'."
         )
 

docs/docs/agents/context.md

@@ -8,60 +8,75 @@ Context includes *any* data outside the message list that can shape behavior. Th
 - Internal state updated during a multi-step reasoning process.
 - Persistent memory or facts from previous interactions.
 
-LangGraph provides **three** primary ways to supply context:
+LangGraph provides **three** primary ways to manage context:
 
 | Type                                                                         | Description                                   | Mutable? | Lifetime                |
 |------------------------------------------------------------------------------|-----------------------------------------------|----------|-------------------------|
-| [**Config**](#config-static-context)                                         | data passed at the start of a run             | ❌        | per run                 |
+| [**Runtime Context**](#runtime-context)                                      | data passed at the start of a run             | ❌        | per run                 |
 | [**Short-term memory (State)**](#short-term-memory-mutable-context)          | dynamic data that can change during execution | ✅        | per run or conversation |
 | [**Long-term memory (Store)**](#long-term-memory-cross-conversation-context) | data that can be shared between conversations | ✅        | across conversations    |
 
-## Provide runtime context
+### Runtime Context
 
-### Config (static context)
+Runtime context is for immutable data like user metadata, tools, db connections, etc. Use this when you have values that don't change mid-run.
 
-Config is for immutable data like user metadata or API keys. Use
-when you have values that don't change mid-run.
+!!! version-added "New in LangGraph v0.6: `Runtime.context` replaces `config['configurable']`"
 
-Specify configuration using a key called **"configurable"** which is reserved
-for this purpose:
+    The `Runtime` object is recommended to access static context and runtime-specific information like the store and stream writer.
+
+!!! note 
+
+    Runtime context refers to local context: data and dependencies your code needs to run. It does not refer to:
+
+    * The LLM context, which is the data passed into the LLM's prompt.
+    * The "context window", which is the maximum number of tokens that can be passed to the LLM.
+
+    You likely want to use the local context to optimize the LLM's context window. For example, you
+    could use a user id to fetch a user's name and information from a database to populate the context window with relevant memories.
+
+Specify static context via the `context` argument to `invoke` / `stream`, which is reserved for this purpose:
 
 ```python
+@dataclass
+class ContextSchema:
+    user_name: str
+
 graph.invoke( # (1)!
     {"messages": [{"role": "user", "content": "hi!"}]}, # (2)!
     # highlight-next-line
-    config={"configurable": {"user_id": "user_123"}} # (3)!
+    context={"user_name": "John Smith"} # (3)!
 )
 ```
 
 1. This is the invocation of the agent or graph. The `invoke` method runs the underlying graph with the provided input.
 2. This example uses messages as an input, which is common, but your application may use different input structures.
-3. This is where you pass the configuration data. The `config` parameter allows you to provide additional context that the agent can use during its execution.
+3. This is where you pass the runtime data. The `context` parameter allows you to provide additional dependencies that the agent can use during its execution.
 
 === "Agent prompt"
 
     ```python
     from langchain_core.messages import AnyMessage
-    from langchain_core.runnables import RunnableConfig
+    from langgraph.runtime import get_runtime
     from langgraph.prebuilt.chat_agent_executor import AgentState
     from langgraph.prebuilt import create_react_agent
 
     # highlight-next-line
-    def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:
-        user_name = config["configurable"].get("user_name")
-        system_msg = f"You are a helpful assistant. Address the user as {user_name}."
+    def prompt(state: AgentState) -> list[AnyMessage]:
+        runtime = get_runtime(ContextSchema)
+        system_msg = f"You are a helpful assistant. Address the user as {runtime.context.user_name}."
         return [{"role": "system", "content": system_msg}] + state["messages"]
 
     agent = create_react_agent(
         model="anthropic:claude-3-7-sonnet-latest",
         tools=[get_weather],
-        prompt=prompt
+        prompt=prompt,
+        context_schema=ContextSchema
     )
 
     agent.invoke(
         {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
         # highlight-next-line
-        config={"configurable": {"user_name": "John Smith"}}
+        context={"user_name": "John Smith"}
     )
     ```
 
@@ -70,11 +85,11 @@ graph.invoke( # (1)!
 === "Workflow node"
 
     ```python
-    from langchain_core.runnables import RunnableConfig
+    from langgraph.runtime import Runtime
 
     # highlight-next-line
-    def node(state: State, config: RunnableConfig):
-        user_name = config["configurable"].get("user_name")
+    def node(state: State, config: Runtime[ContextSchema]):
+        user_name = runtime.context.user_name
         ...
     ```
 
@@ -83,14 +98,16 @@ graph.invoke( # (1)!
 === "In a tool"
 
     ```python
-    from langchain_core.runnables import RunnableConfig
+    from langgraph.runtime import get_runtime
 
     @tool
     # highlight-next-line
-    def get_user_info(config: RunnableConfig) -> str:
+    def get_user_email() -> str:
         """Retrieve user information based on user ID."""
-        user_id = config["configurable"].get("user_id")
-        return "User is John Smith" if user_id == "user_123" else "Unknown user"
+        # simulate fetching user info from a database
+        runtime = get_runtime(ContextSchema)
+        email = get_user_email_from_db(runtime.context.user_name)
+        return email
     ```
 
     See the [tool calling guide](../how-tos/tool-calling.md#configuration) for details.

docs/docs/cloud/deployment/egress.md

@@ -0,0 +1,119 @@
+# Egress for Subscription Metrics and Operational Metadata
+
+> **Important: Self Hosted Only** 
+> This section only applies to customers who are not running in offline mode and assumes you are using a self-hosted LangGraph Platform instance.
+> This does not apply to SaaS or Hybrid deployments.
+
+Self-Hosted LangGraph Platform instances store all information locally and will never send sensitive information outside of your network. We currently only track platform usage for billing purposes according to the entitlements in your order. In order to better remotely support our customers, we do require egress to `https://beacon.langchain.com`.
+
+In the future, we will be introducing support diagnostics to help us ensure that the LangGraph Platform is running at an optimal level within your environment.
+
+> **Warning**  
+> **This will require egress to `https://beacon.langchain.com` from your network.**
+> **If using an API key, you will also need to allow egress to `https://api.smith.langchain.com` or `https://eu.api.smith.langchain.com` for API key verification.**
+
+Generally, data that we send to Beacon can be categorized as follows:
+
+- **Subscription Metrics**
+  - Subscription metrics are used to determine level of access and utilization of LangSmith. This includes, but are not limited to:
+    - Nodes Executed
+    - Runs Executed
+    - License Key Verification
+- **Operational Metadata**
+  - This metadata will contain and collect the above subscription metrics to assist with remote support, allowing the LangChain team to diagnose and troubleshoot performance issues more effectively and proactively.
+
+## Example Payloads
+
+In an effort to maximize transparency, we provide sample payloads here:
+
+### License Verification (If using an Enterprise License)
+
+**Endpoint:**
+
+`POST beacon.langchain.com/v1/beacon/verify`
+
+**Request:**
+
+```json
+{
+  "license": "<YOUR_LICENSE_KEY>"
+}
+```
+
+**Response:**
+
+```json
+{
+  "token": "Valid JWT" // Short-lived JWT token to avoid repeated license checks
+}
+```
+
+### Api Key Verification (If using a LangSmith API Key)
+
+**Endpoint:**
+`POST api.smith.langchain.com/auth`
+
+**Request:**
+
+```json
+"Headers": {
+  X-Api-Key: <YOUR_API_KEY>
+}
+```
+
+**Response:**
+
+```json
+{
+  "org_config": {
+    "org_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
+    ... // Additional organization details
+  }
+}
+```
+
+### Usage Reporting
+
+**Endpoint:**
+
+`POST beacon.langchain.com/v1/metadata/submit`
+
+**Request:**
+
+```json
+{
+  "license": "<YOUR_LICENSE_KEY>",
+  "from_timestamp": "2025-01-06T09:00:00Z",
+  "to_timestamp": "2025-01-06T10:00:00Z",
+  "tags": {
+    "langgraph.python.version": "0.1.0",
+    "langgraph_api.version": "0.2.0",
+    "langgraph.platform.revision": "abc123",
+    "langgraph.platform.variant": "standard",
+    "langgraph.platform.host": "host-1",
+    "langgraph.platform.tenant_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
+    "langgraph.platform.project_id": "c5b5f53a-4716-4326-8967-d4f7f7799735",
+    "langgraph.platform.plan": "enterprise",
+    "user_app.uses_indexing": "true",
+    "user_app.uses_custom_app": "false",
+    "user_app.uses_custom_auth": "true",
+    "user_app.uses_thread_ttl": "true",
+    "user_app.uses_store_ttl": "false"
+  },
+  "measures": {
+    "langgraph.platform.runs": 150,
+    "langgraph.platform.nodes": 450
+  },
+  "logs": []
+}
+```
+
+**Response:**
+
+```json
+"204 No Content"
+```
+
+## Our Commitment
+
+LangChain will not store any sensitive information in the Subscription Metrics or Operational Metadata. Any data collected will not be shared with a third party. If you have any concerns about the data being sent, please reach out to your account team.

docs/docs/cloud/deployment/self_hosted_control_plane.md

@@ -23,6 +23,8 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
 
         kubectl get storageclass
 
+1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
+
 ## Setup
 
 1. As part of configuring your Self-Hosted LangSmith instance, you enable the `langgraphPlatform` option. This will provision a few key resources.

docs/docs/cloud/deployment/setup.md

@@ -108,11 +108,11 @@ from langgraph.graph import StateGraph, END, START
 from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
 from my_agent.utils.state import AgentState # import state
 
-# Define the config
-class GraphConfig(TypedDict):
+# Define the runtime context
+class GraphContext(TypedDict):
     model_name: Literal["anthropic", "openai"]
 
-workflow = StateGraph(AgentState, config_schema=GraphConfig)
+workflow = StateGraph(AgentState, context_schema=GraphContext)
 workflow.add_node("agent", call_model)
 workflow.add_node("action", tool_node)
 workflow.add_edge(START, "agent")

docs/docs/cloud/deployment/setup_pyproject.md

@@ -121,11 +121,11 @@ from langgraph.graph import StateGraph, END, START
 from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
 from my_agent.utils.state import AgentState # import state
 
-# Define the config
-class GraphConfig(TypedDict):
+# Define the runtime context
+class GraphContext(TypedDict):
     model_name: Literal["anthropic", "openai"]
 
-workflow = StateGraph(AgentState, config_schema=GraphConfig)
+workflow = StateGraph(AgentState, context_schema=GraphContext)
 workflow.add_node("agent", call_model)
 workflow.add_node("action", tool_node)
 workflow.add_edge(START, "agent")

docs/docs/cloud/deployment/standalone_container.md

@@ -24,6 +24,7 @@ Before deploying, review the [conceptual guide for the Standalone Container](../
     1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_server.md#server-versions)) LangSmith API key. This will be used to authenticate ONCE at server start up.
     1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#licensing)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
     1. `LANGSMITH_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGSMITH_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
+1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
 
 ## Kubernetes (Helm)
 

docs/docs/cloud/how-tos/add-human-in-the-loop.md

@@ -30,9 +30,7 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
     # > [
     # >     {
     # >         'value': {'text_to_revise': 'original text'},
-    # >         'resumable': True,
-    # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
-    # >         'when': 'during'
+    # >         'id': '...',
     # >     }
     # > ]
 
@@ -203,9 +201,7 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
         # > [
         # >     {
         # >         'value': {'text_to_revise': 'original text'},
-        # >         'resumable': True,
-        # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
-        # >         'when': 'during'
+        # >         'id': '...',
         # >     }
         # > ]
 

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

@@ -2,21 +2,20 @@
 
 In this guide we will show how to create, configure, and manage an [assistant](../../concepts/assistants.md).
 
-First, as a brief refresher on the concept of configurations, consider the following simple `call_model` node and configuration schema. Observe that this node tries to read and use the `model_name` as defined by the `config` object's `configurable`.
+First, as a brief refresher on the concept of runtime context, consider the following simple `call_model` node and context schema. Observe that this node tries to read and use the `model_provider` as defined by the `Runtime` object's `context` property.
 
 === "Python"
 
     ```python
+    @dataclass
+    class ContextSchema:
+        llm_provider: str = "anthropic"
 
-    class ConfigSchema(TypedDict):
-        model_name: str
+    builder = StateGraph(AgentState, context_schema=ContextSchema)
 
-    builder = StateGraph(AgentState, config_schema=ConfigSchema)
-
-    def call_model(state, config):
+    def call_model(state, runtime: Runtime[ContextSchema]):
         messages = state["messages"]
-        model_name = config.get('configurable', {}).get("model_name", "anthropic")
-        model = _get_model(model_name)
+        model = _get_model(runtime.context.llm_provider)
         response = model.invoke(messages)
         # We return a list, because this will get added to the existing list
         return {"messages": [response]}
@@ -44,7 +43,7 @@ First, as a brief refresher on the concept of configurations, consider the follo
     }
     ```
 
-For more information on configurations, [see here](../../concepts/low_level.md#configuration).
+For more information on runtime context, [see here](../../concepts/low_level.md#runtime-context).
 
 ## Create an assistant
 

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

@@ -30,17 +30,33 @@ export default {
 
 Next, define your UI components in your `langgraph.json` configuration:
 
-```json
-{
-  "node_version": "20",
-  "graphs": {
-    "agent": "./src/agent/index.ts:graph"
-  },
-  "ui": {
-    "agent": "./src/agent/ui.tsx"
-  }
-}
-```
+=== "Python agent"
+
+    ```json title="langgraph.json"
+    {
+      "node_version": "20",
+      "graphs": {
+        "agent": "./src/agent.py:graph"
+      },
+      "ui": {
+        "agent": "./src/agent/ui.tsx"
+      }
+    }
+    ```
+
+=== "JS agent"
+
+    ```json title="langgraph.json"
+    {
+      "node_version": "20",
+      "graphs": {
+        "agent": "./src/agent/index.ts:graph"
+      },
+      "ui": {
+        "agent": "./src/agent/ui.tsx"
+      }
+    }
+    ```
 
 The `ui` section points to the UI components that will be used by graphs. By default, we recommend using the same key as the graph name, but you can split out the components however you like, see [Customise the namespace of UI components](#customise-the-namespace-of-ui-components) for more details.
 

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

@@ -140,6 +140,22 @@ https://my-server.app/my-webhook-endpoint?token=YOUR_SECRET_TOKEN
 
 Your server should extract and validate this token before processing requests.
 
+## Disable webhooks
+
+As of `langgraph-api>=0.2.78`, developers can disable webhooks in the `langgraph.json` file:
+
+```json
+{
+  "http": {
+    "disable_webhooks": true
+  }
+}
+```
+
+This feature is primarily intended for self-hosted deployments, where platform administrators or developers may prefer to disable webhooks to simplify their security posture—especially if they are not configuring firewall rules or other network controls. Disabling webhooks helps prevent untrusted payloads from being sent to internal endpoints.
+
+For full configuration details, refer to the [configuration file reference](https://langchain-ai.github.io/langgraph/cloud/reference/cli/?h=disable_webhooks#configuration-file).
+
 ## Test webhooks
 
 You can test your webhook using online services like:

docs/docs/cloud/reference/cli.md

@@ -409,8 +409,8 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | Option                       | Default                   | Description                                                                                                             |
     | ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
     | `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
-| `--base-image TEXT`          | `langchain/langgraph-api`  | Base image to use for the LangGraph API server. Pin to specific versions using version tags.                            |
-| `--image TEXT`               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly.           |
+    | `--base-image TEXT`          | `langchain/langgraph-api`  | Base image to use for the LangGraph API server. Pin to specific versions using version tags.                            |
+    | `--image TEXT`               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly.           |
     | `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
     | `--watch`                    |                           | Restart on file changes                                                                                                 |
     | `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
@@ -438,8 +438,8 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | Option                                                                 | Default                   | Description                                                                                                             |
     | ---------------------------------------------------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
     | <span style="white-space: nowrap;">`--wait`</span>                     |                           | Wait for services to start before returning. Implies --detach                                                           |
-| <span style="white-space: nowrap;">`--base-image TEXT`</span>          | <span style="white-space: nowrap;">`langchain/langgraph-api`</span> | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
-| <span style="white-space: nowrap;">`--image TEXT`</span>               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
+    | <span style="white-space: nowrap;">`--base-image TEXT`</span>          | <span style="white-space: nowrap;">`langchain/langgraph-api`</span> | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
+    | <span style="white-space: nowrap;">`--image TEXT`</span>               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
     | <span style="white-space: nowrap;">`--postgres-uri TEXT`</span>        | Local database            | Postgres URI to use for the database.                                                                                   |
     | <span style="white-space: nowrap;">`--watch`</span>                    |                           | Restart on file changes                                                                                                 |
     | <span style="white-space: nowrap;">`-c, --config FILE`</span>          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |

docs/docs/cloud/reference/langgraph_server_changelog.md

@@ -4,6 +4,54 @@
 
 ---
 
+## v0.2.109 (2025-07-28)
+- Fixed an issue where missing config schema occurred when `config_type` was not set.
+
+## v0.2.108 (2025-07-28)
+- Added compatibility for langgraph v0.6, including new context API support and a migration to enhance context handling in assistant operations.
+
+## v0.2.107 (2025-07-27)
+- Implemented caching for authentication processes to improve performance.
+- Merged count and select queries to improve database query efficiency.
+
+## v0.2.106 (2025-07-27)
+- Log whether run uses resumable streams.
+
+## v0.2.105 (2025-07-27)
+- Added a `/heapdump` endpoint to capture and save JS process heap data.
+
+## v0.2.103 (2025-07-25)
+- Corrected the metadata endpoint to ensure accurate data retrieval.
+
+## v0.2.102 (2025-07-24)
+- Captured interrupt events in the wait method to preserve legacy behavior and stream updates by default.
+- Added support for SDK structlog in the JavaScript environment, enhancing logging capabilities.
+
+## v0.2.101 (2025-07-24)
+- Used the correct metadata endpoint for self-hosted environments, resolving an access issue.
+
+## v0.2.99 (2025-07-22)
+- Improved license validation by adding an in-memory cache and handling Redis connection errors more effectively.
+- Automatically remove agents from memory that are removed from `langgraph.json` to prevent persistence issues.
+- Ensured the UI namespace for generated UI is a valid JavaScript property name to prevent errors.
+- Raised a 422 error for improved request validation feedback.
+
+## v0.2.98 (2025-07-19)
+- Added langgraph node context for improved log filtering and trace visibility.
+
+## v0.2.97 (2025-07-19)
+- Fixed scheduling issue with ckpt ingestion worker that occurred on isolated background loops.
+- Ensured queue worker starts only after all migrations have completed.
+- Added more detailed error messages for thread state issues and improved response handling when state updates fail.
+- Exposed interrupt ID while retrieving thread state for enhanced API response details.
+
+## v0.2.96 (2025-07-17)
+- Added a fallback mechanism for configurable header patterns to handle exclude/include settings more effectively.
+
+## v0.2.95 (2025-07-17)
+- Avoided setting the future if it is already done to prevent redundant operations.
+- Resolved compatibility errors in CI by switching from `typing.TypedDict` to `typing_extensions.TypedDict` for Python versions below 3.12.
+
 ## v0.2.94 (2025-07-16)
 - Improved performance by omitting pending sends for langgraph versions 0.5 and above.
 - Improved server startup logs to provide clearer warnings when the DD_API_KEY environment variable is set.

docs/docs/concepts/assistants.md

@@ -1,6 +1,6 @@
 # Assistants
 
-**Assistants** allow you to manage configurations (like prompts, LLM selection, tools) separately from your graph's core logic, enabling rapid changes that don't alter the graph architecture. It is a way to create multiple specialized versions of the same graph architecture, each optimized for different use cases through configuration variations rather than structural changes.
+**Assistants** allow you to manage configurations (like prompts, LLM selection, tools) separately from your graph's core logic, enabling rapid changes that don't alter the graph architecture. It is a way to create multiple specialized versions of the same graph architecture, each optimized for different use cases through context/configuration variations rather than structural changes.
 
 For example, imagine a general-purpose writing agent built on a common graph architecture. While the structure remains the same, different writing styles—such as blog posts and tweets—require tailored configurations to optimize performance. To support these variations, you can create multiple assistants (e.g., one for blogs and another for tweets) that share the underlying graph but differ in model selection and system prompt.
 
@@ -14,8 +14,8 @@ The LangGraph Cloud API provides several endpoints for creating and managing ass
 
 ## Configuration
 
-Assistants build on the LangGraph open source concept of [configuration](low_level.md#configuration).
-While configuration is available in the open source LangGraph library, assistants are only present in [LangGraph Platform](langgraph_platform.md). This is due to the fact that assistants are tightly coupled to your deployed graph. Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default configuration settings.
+Assistants build on the LangGraph open source concepts of configuration and [runtime context](low_level.md#runtime-context).
+While these features are available in the open source LangGraph library, assistants are only present in [LangGraph Platform](langgraph_platform.md). This is due to the fact that assistants are tightly coupled to your deployed graph. Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default context and configuration settings.
 
 In practice, an assistant is just an _instance_ of a graph with a specific configuration. Therefore, multiple assistants can reference the same graph but can contain different configurations (e.g. prompts, models, tools). The LangGraph Server API provides several endpoints for creating and managing assistants. See the [API reference](../cloud/reference/api/api_ref.html) and [this how-to](../cloud/how-tos/configuration_cloud.md) for more details on how to create assistants.
 
@@ -26,6 +26,6 @@ Once you've created an assistant, subsequent edits to that assistant will create
 
 ## Execution
 
-A **run** is an invocation of an assistant. Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](./persistence.md#threads).
+A **run** is an invocation of an assistant. Each run may have its own input, configuration, context, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](./persistence.md#threads).
 
 The LangGraph Platform API provides several endpoints for creating and managing runs. See the [API reference](../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.

docs/docs/concepts/deployment_options.md

@@ -10,7 +10,7 @@ search:
 There are two free options for deploying LangGraph applications via the LangGraph Server:
 
 1. [Local](../tutorials/langgraph-platform/local-server.md): Deploy for local testing and development. 
-1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more that 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.
+1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more than 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.
 
 ## Production deployment
 

docs/docs/concepts/durable_execution.md

@@ -48,7 +48,7 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
     from typing_extensions import TypedDict
     import uuid
 
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
     from langgraph.graph import StateGraph, START, END
     import requests
 
@@ -74,7 +74,7 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
     builder.add_edge("call_api", END)
 
     # Specify a checkpointer
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
 
     # Compile the graph with the checkpointer
     graph = builder.compile(checkpointer=checkpointer)
@@ -94,7 +94,7 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
     from typing_extensions import TypedDict
     import uuid
 
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
     from langgraph.func import task
     from langgraph.graph import StateGraph, START, END
     import requests
@@ -129,7 +129,7 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
     builder.add_edge("call_api", END)
 
     # Specify a checkpointer
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
 
     # Compile the graph with the checkpointer
     graph = builder.compile(checkpointer=checkpointer)

docs/docs/concepts/functional_api.md

@@ -39,7 +39,7 @@ Here are some key differences:
 Below we demonstrate a simple application that writes an essay and [interrupts](human_in_the_loop.md) to request human review.
 
 ```python
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.func import entrypoint, task
 from langgraph.types import interrupt
 
@@ -50,7 +50,7 @@ def write_essay(topic: str) -> str:
     time.sleep(1) # A placeholder for a long-running task.
     return f"An essay about topic: {topic}"
 
-@entrypoint(checkpointer=MemorySaver())
+@entrypoint(checkpointer=InMemorySaver())
 def workflow(topic: str) -> dict:
     """A simple workflow that writes an essay and asks for a review."""
     essay = write_essay("cat").result()
@@ -79,51 +79,54 @@ def workflow(topic: str) -> dict:
     ```python
     import time
     import uuid
-
     from langgraph.func import entrypoint, task
     from langgraph.types import interrupt
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
+
 
     @task
     def write_essay(topic: str) -> str:
         """Write an essay about the given topic."""
-        time.sleep(1) # This is a placeholder for a long-running task.
+        time.sleep(1)  # This is a placeholder for a long-running task.
         return f"An essay about topic: {topic}"
 
-    @entrypoint(checkpointer=MemorySaver())
+    @entrypoint(checkpointer=InMemorySaver())
     def workflow(topic: str) -> dict:
         """A simple workflow that writes an essay and asks for a review."""
         essay = write_essay("cat").result()
-        is_approved = interrupt({
-            # Any json-serializable payload provided to interrupt as argument.
-            # It will be surfaced on the client side as an Interrupt when streaming data
-            # from the workflow.
-            "essay": essay, # The essay we want reviewed.
-            # We can add any additional information that we need.
-            # For example, introduce a key called "action" with some instructions.
-            "action": "Please approve/reject the essay",
-        })
-
+        is_approved = interrupt(
+            {
+                # Any json-serializable payload provided to interrupt as argument.
+                # It will be surfaced on the client side as an Interrupt when streaming data
+                # from the workflow.
+                "essay": essay,  # The essay we want reviewed.
+                # We can add any additional information that we need.
+                # For example, introduce a key called "action" with some instructions.
+                "action": "Please approve/reject the essay",
+            }
+        )
         return {
-            "essay": essay, # The essay that was generated
-            "is_approved": is_approved, # Response from HIL
+            "essay": essay,  # The essay that was generated
+            "is_approved": is_approved,  # Response from HIL
         }
 
+
     thread_id = str(uuid.uuid4())
-
-    config = {
-        "configurable": {
-            "thread_id": thread_id
-        }
-    }
-
+    config = {"configurable": {"thread_id": thread_id}}
     for item in workflow.stream("cat", config):
         print(item)
-    ```
-
-    ```pycon
-    {'write_essay': 'An essay about topic: cat'}
-    {'__interrupt__': (Interrupt(value={'essay': 'An essay about topic: cat', 'action': 'Please approve/reject the essay'}, resumable=True, ns=['workflow:f7b8508b-21c0-8b4c-5958-4e8de74d2684'], when='during'),)}
+    # > {'write_essay': 'An essay about topic: cat'}
+    # > {
+    # >     '__interrupt__': (
+    # >        Interrupt(
+    # >            value={
+    # >                'essay': 'An essay about topic: cat',
+    # >                'action': 'Please approve/reject the essay'
+    # >            },
+    # >            id='b9b2b9d788f482663ced6dc755c9e981'
+    # >        ),
+    # >    )
+    # > }
     ```
 
     An essay has been written and is ready for review. Once the review is provided, we can resume the workflow:

docs/docs/concepts/human_in_the_loop.md

@@ -23,12 +23,12 @@ To review, edit, and approve tool calls in an agent or workflow, [use LangGraph'
 
 ## Key capabilities
 
-* **Persistent execution state**: Interrupts use LangGraph's [persistence](../../concepts/persistence.md) layer, which saves the graph state, to indefinitely pause graph execution until you resume. This is possible because LangGraph checkpoints the graph state after each step, which allows the system to persist execution context and later resume the workflow, continuing from where it left off. This supports asynchronous human review or input without time constraints.
+* **Persistent execution state**: Interrupts use LangGraph's [persistence](./persistence.md) layer, which saves the graph state, to indefinitely pause graph execution until you resume. This is possible because LangGraph checkpoints the graph state after each step, which allows the system to persist execution context and later resume the workflow, continuing from where it left off. This supports asynchronous human review or input without time constraints.
 
     There are two ways to pause a graph:
 
     - [Dynamic interrupts](../how-tos/human_in_the_loop/add-human-in-the-loop.md#pause-using-interrupt): Use `interrupt` to pause a graph from inside a specific node, based on the current state of the graph.
-    - [Static interrupts](../how-tos/human_in_the_loop/add-human-in-the-loop.md#debug-with-interrupts): Use `interrupt_before` and `interrupt_after` to pause the graph at defined points, either before or after a node executes.
+    - [Static interrupts](../how-tos/human_in_the_loop/add-human-in-the-loop.md#debug-with-interrupts): Use `interrupt_before` and `interrupt_after` to pause the graph at pre-defined points, either before or after a node executes.
 
     <figure markdown="1">
     ![image](./img/breakpoints.png){: style="max-height:400px"}

docs/docs/concepts/langgraph_control_plane.md

@@ -119,6 +119,11 @@ These metrics are displayed as charts in the Control Plane UI.
 
 ### LangSmith Integration
 
-A [LangSmith](https://docs.smith.langchain.com/) tracing project is automatically created for each deployment. The tracing project has the same name as the deployment. When creating a deployment, the `LANGCHAIN_TRACING` and `LANGSMITH_API_KEY`/`LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set automatically by the control plane.
+A [LangSmith](https://docs.smith.langchain.com/) tracing project and LangSmith API key are automatically created for each deployment. The deployment uses the API key to automatically send traces to LangSmith.
 
-When a deployment is deleted, the traces and the tracing project are not deleted.
+- The tracing project has the same name as the deployment.
+- The API key has the description `LangGraph Platform: <deployment_name>`.
+- The API key is never revealed and cannot be deleted manually.
+- When creating a deployment, the `LANGCHAIN_TRACING` and `LANGSMITH_API_KEY`/`LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set automatically by the control plane.
+
+When a deployment is deleted, the traces and the tracing project are not deleted. However, the API will be deleted when the deployment is deleted.

docs/docs/concepts/low_level.md

@@ -192,35 +192,48 @@ class State(MessagesState):
 
 ## Nodes
 
-In LangGraph, nodes are typically python functions (sync or async) where the **first** positional argument is the [state](#state), and (optionally), the **second** positional argument is a "config", containing optional [configurable parameters](#configuration) (such as a `thread_id`).
+In LangGraph, nodes are Python functions (either synchronous or asynchronous) that accept the following arguments:
+
+1. `state`: The [state](#state) of the graph
+2. `config`: A `RunnableConfig` object that contains configuration information like `thread_id` and tracing information like `tags`
+3. `runtime`: A `Runtime` object that contains [runtime `context`](#runtime-context) and other information like `store` and `stream_writer`
+    
 
 Similar to `NetworkX`, you add these nodes to a graph using the [add_node][langgraph.graph.StateGraph.add_node] method:
 
 ```python
+from dataclasses import dataclass
 from typing_extensions import TypedDict
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.graph import StateGraph
+from langgraph.runtime import Runtime
 
 class State(TypedDict):
     input: str
     results: str
 
+@dataclass
+class Context:
+    user_id: str
+
 builder = StateGraph(State)
 
+def plain_node(state: State):
+    return state
 
-def my_node(state: State, config: RunnableConfig):
-    print("In node: ", config["configurable"]["user_id"])
+def node_with_runtime(state: State, runtime: Runtime[Context]):
+    print("In node: ", runtime.context.user_id)
+    return {"results": f"Hello, {state['input']}!"}
+
+def node_with_config(state: State, config: RunnableConfig):
+    print("In node with thread_id: ", config["configurable"]["thread_id"])
     return {"results": f"Hello, {state['input']}!"}
 
 
-# The second argument is optional
-def my_other_node(state: State):
-    return state
-
-
-builder.add_node("my_node", my_node)
-builder.add_node("other_node", my_other_node)
+builder.add_node("plain_node", plain_node)
+builder.add_node("node_with_runtime", node_with_runtime)
+builder.add_node("node_with_config", node_with_config)
 ...
 ```
 
@@ -459,33 +472,32 @@ LangGraph can easily handle migrations of graph definitions (nodes, edges, and s
 - State keys that are renamed lose their saved state in existing threads
 - State keys whose types change in incompatible ways could currently cause issues in threads with state from before the change -- if this is a blocker please reach out and we can prioritize a solution.
 
-## Configuration
+## Runtime Context
 
-When creating a graph, you can also mark that certain parts of the graph are configurable. This is commonly done to enable easily switching between models or system prompts. This allows you to create a single "cognitive architecture" (the graph) but have multiple different instance of it.
-
-You can optionally specify a `config_schema` when creating a graph.
+When creating a graph, you can specify a `context_schema` for runtime context passed to nodes. This is useful for passing
+information to nodes that is not part of the graph state. For example, you might want to pass dependencies such as model name or a database connection.
 
 ```python
-class ConfigSchema(TypedDict):
-    llm: str
+@dataclass
+class ContextSchema:
+    llm_provider: str = "openai"
 
-graph = StateGraph(State, config_schema=ConfigSchema)
+graph = StateGraph(State, context_schema=ContextSchema)
 ```
 
-You can then pass this configuration into the graph using the `configurable` config field.
+You can then pass this context into the graph using the `context` parameter of the `invoke` method.
 
 ```python
-config = {"configurable": {"llm": "anthropic"}}
-
-graph.invoke(inputs, config=config)
+graph.invoke(inputs, context={"llm_provider": "anthropic"})
 ```
 
-You can then access and use this configuration inside a node or conditional edge:
+You can then access and use this context inside a node or conditional edge:
 
 ```python
-def node_a(state, config):
-    llm_type = config.get("configurable", {}).get("llm", "openai")
-    llm = get_llm(llm_type)
+from langgraph.runtime import Runtime
+
+def node_a(state: State, runtime: Runtime[ContextSchema]):
+    llm = get_llm(runtime.context.llm_provider)
     ...
 ```
 
@@ -496,7 +508,7 @@ See [this guide](../how-tos/graph-api.md#add-runtime-configuration) for a full b
 The recursion limit sets the maximum number of [super-steps](#graphs) the graph can execute during a single execution. Once the limit is reached, LangGraph will raise `GraphRecursionError`. By default this value is set to 25 steps. The recursion limit can be set on any graph at runtime, and is passed to `.invoke`/`.stream` via the config dictionary. Importantly, `recursion_limit` is a standalone `config` key and should not be passed inside the `configurable` key as all other user-defined configuration. See the example below:
 
 ```python
-graph.invoke(inputs, config={"recursion_limit": 5, "configurable":{"llm": "anthropic"}})
+graph.invoke(inputs, config={"recursion_limit": 5}, context={"llm": "anthropic"})
 ```
 
 Read [this how-to](https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/) to learn more about how the recursion limit works.

docs/docs/concepts/persistence.md

@@ -487,12 +487,12 @@ If you want to fallback to pickle for objects not currently supported by our msg
 you can use the `pickle_fallback` argument of the `JsonPlusSerializer`:
 
 ```python
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
 
 # ... Define the graph ...
 graph.compile(
-    checkpointer=MemorySaver(serde=JsonPlusSerializer(pickle_fallback=True))
+    checkpointer=InMemorySaver(serde=JsonPlusSerializer(pickle_fallback=True))
 )
 ```
 

docs/docs/guides/index.md

@@ -2,6 +2,11 @@
 
 The pages in this section provide a conceptual overview and how-tos for the following topics:
 
+## Agent development
+
+- [Overview](../agents/overview.md): Use prebuilt components to build an agent.
+- [Run an agent](../agents/run_agents.md): Run an agent by providing input, interpreting output, enabling streaming, and controlling execution limits.
+
 ## LangGraph APIs
 
 - [Graph API](../concepts/low_level.md): Use the Graph API to define workflows using a graph paradigm.

docs/docs/how-tos/autogen-integration-functional.ipynb

@@ -77,7 +77,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "OPENAI_API_KEY:  ········\n"
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "d129e4e1-3766-429a-b806-cde3d8bc0469",
    "metadata": {},
    "outputs": [],
@@ -173,7 +173,7 @@
     "from langchain_core.messages import convert_to_openai_messages, BaseMessage\n",
     "from langgraph.func import entrypoint, task\n",
     "from langgraph.graph import add_messages\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "\n",
     "\n",
     "@task\n",
@@ -192,7 +192,7 @@
     "\n",
     "\n",
     "# add short-term memory for storing conversation history\n",
-    "checkpointer = MemorySaver()\n",
+    "checkpointer = InMemorySaver()\n",
     "\n",
     "\n",
     "@entrypoint(checkpointer=checkpointer)\n",
@@ -222,12 +222,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[33muser_proxy\u001B[0m (to assistant):\n",
+      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
       "\n",
       "Find numbers between 10 and 30 in fibonacci sequence\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001B[33massistant\u001B[0m (to user_proxy):\n",
+      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
       "\n",
       "To find numbers between 10 and 30 in the Fibonacci sequence, we can generate the Fibonacci sequence and check which numbers fall within this range. Here's a plan:\n",
       "\n",
@@ -253,9 +253,9 @@
       "This script will print the Fibonacci numbers between 10 and 30. Please execute the code to see the result.\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001B[31m\n",
-      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001B[0m\n",
-      "\u001B[33muser_proxy\u001B[0m (to assistant):\n",
+      "\u001b[31m\n",
+      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
+      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
       "\n",
       "exitcode: 0 (execution succeeded)\n",
       "Code output: \n",
@@ -264,7 +264,7 @@
       "\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001B[33massistant\u001B[0m (to user_proxy):\n",
+      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
       "\n",
       "The Fibonacci numbers between 10 and 30 are 13 and 21. \n",
       "\n",
@@ -318,7 +318,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[33muser_proxy\u001B[0m (to assistant):\n",
+      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
       "\n",
       "Multiply the last number by 3\n",
       "Context: \n",
@@ -334,7 +334,7 @@
       "TERMINATE\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001B[33massistant\u001B[0m (to user_proxy):\n",
+      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
       "\n",
       "The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives:\n",
       "\n",

docs/docs/how-tos/autogen-integration.md

@@ -75,7 +75,7 @@ We will now create a LangGraph chatbot graph that calls AutoGen agent.
 ```python
 from langchain_core.messages import convert_to_openai_messages
 from langgraph.graph import StateGraph, MessagesState, START
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 
 def call_autogen_agent(state: MessagesState):
     # Convert LangGraph messages to OpenAI format for AutoGen
@@ -101,7 +101,7 @@ def call_autogen_agent(state: MessagesState):
     return {"messages": {"role": "assistant", "content": final_content}}
 
 # Create the graph with memory for persistence
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 
 # Build the graph
 builder = StateGraph(MessagesState)
@@ -228,7 +228,7 @@ my-autogen-agent/
     import autogen
     from langchain_core.messages import convert_to_openai_messages
     from langgraph.graph import StateGraph, MessagesState, START
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
     # AutoGen configuration
     config_list = [{"model": "gpt-4o", "api_key": os.environ["OPENAI_API_KEY"]}]
@@ -276,7 +276,7 @@ my-autogen-agent/
 
     # Create and compile the graph
     def create_graph():
-        checkpointer = MemorySaver()
+        checkpointer = InMemorySaver()
         builder = StateGraph(MessagesState)
         builder.add_node("autogen", call_autogen_agent)
         builder.add_edge(START, "autogen")
@@ -290,7 +290,7 @@ my-autogen-agent/
 
     ```
     langgraph>=0.1.0
-    pyautogen>=0.2.0
+    ag2>=0.2.0
     langchain-core>=0.1.0
     langchain-openai>=0.0.5
     ```

docs/docs/how-tos/cross-thread-persistence-functional.ipynb

@@ -167,7 +167,7 @@
     "from langchain_core.messages import BaseMessage\n",
     "from langgraph.func import entrypoint, task\n",
     "from langgraph.graph import add_messages\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "from langgraph.store.base import BaseStore\n",
     "\n",
     "\n",
@@ -192,7 +192,7 @@
     "\n",
     "\n",
     "# NOTE: we're passing the store object here when creating a workflow via entrypoint()\n",
-    "@entrypoint(checkpointer=MemorySaver(), store=in_memory_store)\n",
+    "@entrypoint(checkpointer=InMemorySaver(), store=in_memory_store)\n",
     "def workflow(\n",
     "    inputs: list[BaseMessage],\n",
     "    *,\n",

docs/docs/how-tos/graph-api.md

@@ -514,12 +514,12 @@ To add runtime configuration:
 See below for a simple example:
 
 ```python
-from langchain_core.runnables import RunnableConfig
 from langgraph.graph import END, StateGraph, START
+from langgraph.runtime import Runtime
 from typing_extensions import TypedDict
 
 # 1. Specify config schema
-class ConfigSchema(TypedDict):
+class ContextSchema(TypedDict):
     my_runtime_value: str
 
 # 2. Define a graph that accesses the config in a node
@@ -527,18 +527,18 @@ class State(TypedDict):
     my_state_value: str
 
 # highlight-next-line
-def node(state: State, config: RunnableConfig):
+def node(state: State, runtime: Runtime[ContextSchema]):
     # highlight-next-line
-    if config["configurable"]["my_runtime_value"] == "a":
+    if runtime.context["my_runtime_value"] == "a":
         return {"my_state_value": 1}
         # highlight-next-line
-    elif config["configurable"]["my_runtime_value"] == "b":
+    elif runtime.context["my_runtime_value"] == "b":
         return {"my_state_value": 2}
     else:
         raise ValueError("Unknown values.")
 
 # highlight-next-line
-builder = StateGraph(State, config_schema=ConfigSchema)
+builder = StateGraph(State, context_schema=ContextSchema)
 builder.add_node(node)
 builder.add_edge(START, "node")
 builder.add_edge("node", END)
@@ -547,9 +547,9 @@ graph = builder.compile()
 
 # 3. Pass in configuration at runtime:
 # highlight-next-line
-print(graph.invoke({}, {"configurable": {"my_runtime_value": "a"}}))
+print(graph.invoke({}, context={"my_runtime_value": "a"}))
 # highlight-next-line
-print(graph.invoke({}, {"configurable": {"my_runtime_value": "b"}}))
+print(graph.invoke({}, context={"my_runtime_value": "b"}))
 ```
 ```
 {'my_state_value': 1}
@@ -560,27 +560,28 @@ print(graph.invoke({}, {"configurable": {"my_runtime_value": "b"}}))
     Below we demonstrate a practical example in which we configure what LLM to use at runtime. We will use both OpenAI and Anthropic models.
 
     ```python
+    from dataclasses import dataclass
+
     from langchain.chat_models import init_chat_model
-    from langchain_core.runnables import RunnableConfig
-    from langgraph.graph import MessagesState
-    from langgraph.graph import END, StateGraph, START
+    from langgraph.graph import MessagesState, END, StateGraph, START
+    from langgraph.runtime import Runtime
     from typing_extensions import TypedDict
 
-    class ConfigSchema(TypedDict):
-        model: str
+    @dataclass
+    class ContextSchema:
+        model_provider: str = "anthropic"
 
     MODELS = {
         "anthropic": init_chat_model("anthropic:claude-3-5-haiku-latest"),
         "openai": init_chat_model("openai:gpt-4.1-mini"),
     }
 
-    def call_model(state: MessagesState, config: RunnableConfig):
-        model = config["configurable"].get("model", "anthropic")
-        model = MODELS[model]
+    def call_model(state: MessagesState, runtime: Runtime[ContextSchema]):
+        model = MODELS[runtime.context.model_provider]
         response = model.invoke(state["messages"])
         return {"messages": [response]}
 
-    builder = StateGraph(MessagesState, config_schema=ConfigSchema)
+    builder = StateGraph(MessagesState, context_schema=ContextSchema)
     builder.add_node("model", call_model)
     builder.add_edge(START, "model")
     builder.add_edge("model", END)
@@ -592,8 +593,7 @@ print(graph.invoke({}, {"configurable": {"my_runtime_value": "b"}}))
     # With no configuration, uses default (Anthropic)
     response_1 = graph.invoke({"messages": [input_message]})["messages"][-1]
     # Or, can set OpenAI
-    config = {"configurable": {"model": "openai"}}
-    response_2 = graph.invoke({"messages": [input_message]}, config=config)["messages"][-1]
+    response_2 = graph.invoke({"messages": [input_message]}, context={"model_provider": "openai"})["messages"][-1]
 
     print(response_1.response_metadata["model_name"])
     print(response_2.response_metadata["model_name"])
@@ -607,32 +607,33 @@ print(graph.invoke({}, {"configurable": {"my_runtime_value": "b"}}))
     Below we demonstrate a practical example in which we configure two parameters: the LLM and system message to use at runtime.
 
     ```python
+    from dataclasses import dataclass
     from typing import Optional
     from langchain.chat_models import init_chat_model
     from langchain_core.messages import SystemMessage
-    from langchain_core.runnables import RunnableConfig
     from langgraph.graph import END, MessagesState, StateGraph, START
+    from langgraph.runtime import Runtime
     from typing_extensions import TypedDict
 
-    class ConfigSchema(TypedDict):
-        model: Optional[str]
-        system_message: Optional[str]
+    @dataclass
+    class ContextSchema:
+        model_provider: str = "anthropic"
+        system_message: str | None = None
 
     MODELS = {
         "anthropic": init_chat_model("anthropic:claude-3-5-haiku-latest"),
         "openai": init_chat_model("openai:gpt-4.1-mini"),
     }
 
-    def call_model(state: MessagesState, config: RunnableConfig):
-        model = config["configurable"].get("model", "anthropic")
-        model = MODELS[model]
+    def call_model(state: MessagesState, runtime: Runtime[ContextSchema]):
+        model = MODELS[runtime.context.model_provider]
         messages = state["messages"]
-        if system_message := config["configurable"].get("system_message"):
+        if (system_message := runtime.context.system_message):
             messages = [SystemMessage(system_message)] + messages
         response = model.invoke(messages)
         return {"messages": [response]}
 
-    builder = StateGraph(MessagesState, config_schema=ConfigSchema)
+    builder = StateGraph(MessagesState, context_schema=ContextSchema)
     builder.add_node("model", call_model)
     builder.add_edge(START, "model")
     builder.add_edge("model", END)
@@ -641,8 +642,7 @@ print(graph.invoke({}, {"configurable": {"my_runtime_value": "b"}}))
 
     # Usage
     input_message = {"role": "user", "content": "hi"}
-    config = {"configurable": {"model": "openai", "system_message": "Respond in Italian."}}
-    response = graph.invoke({"messages": [input_message]}, config)
+    response = graph.invoke({"messages": [input_message]}, context={"model_provider": "openai", "system_message": "Respond in Italian."})
     for message in response["messages"]:
         message.pretty_print()
     ```
@@ -1152,12 +1152,13 @@ LangGraph supports map-reduce and other advanced branching patterns using the Se
 ```python
 from langgraph.graph import StateGraph, START, END
 from langgraph.types import Send
-from typing_extensions import TypedDict
+from typing_extensions import TypedDict, Annotated
+import operator
 
 class OverallState(TypedDict):
     topic: str
     subjects: list[str]
-    jokes: list[str]
+    jokes: Annotated[list[str], operator.add]
     best_selected_joke: str
 
 def generate_topics(state: OverallState):
@@ -1566,9 +1567,9 @@ class State(TypedDict):
 
 def node_a(state: State) -> Command[Literal["node_b", "node_c"]]:
     print("Called A")
-    value = random.choice(["a", "b"])
+    value = random.choice(["b", "c"])
     # this is a replacement for a conditional edge function
-    if value == "a":
+    if value == "b":
         goto = "node_b"
     else:
         goto = "node_c"

docs/docs/how-tos/human_in_the_loop/add-human-in-the-loop.md

@@ -54,13 +54,7 @@ graph = graph_builder.compile(checkpointer=checkpointer) # (4)!
 config = {"configurable": {"thread_id": "some_id"}}
 result = graph.invoke({"some_text": "original text"}, config=config) # (5)!
 print(result['__interrupt__']) # (6)!
-# > [
-# >    Interrupt(
-# >       value={'text_to_revise': 'original text'}, 
-# >       resumable=True,
-# >       ns=['human_node:6ce9e64f-edef-fe5d-f7dc-511fa9526960']
-# >    )
-# > ] 
+# > [Interrupt(value={'text_to_revise': 'original text'}, id='a0d9dd40440ac7be2720dc5c20858627')]
 
 # highlight-next-line
 print(graph.invoke(Command(resume="Edited text"), config=config)) # (7)!
@@ -80,25 +74,27 @@ print(graph.invoke(Command(resume="Edited text"), config=config)) # (7)!
     ```python
     from typing import TypedDict
     import uuid
-
     from langgraph.checkpoint.memory import InMemorySaver
     from langgraph.constants import START
     from langgraph.graph import StateGraph
+
     # highlight-next-line
     from langgraph.types import interrupt, Command
 
+
     class State(TypedDict):
         some_text: str
 
+
     def human_node(state: State):
         # highlight-next-line
-        value = interrupt( # (1)!
+        value = interrupt(  # (1)!
             {
-                "text_to_revise": state["some_text"] # (2)!
+                "text_to_revise": state["some_text"]  # (2)!
             }
         )
         return {
-            "some_text": value # (3)!
+            "some_text": value  # (3)!
         }
 
 
@@ -106,25 +102,15 @@ print(graph.invoke(Command(resume="Edited text"), config=config)) # (7)!
     graph_builder = StateGraph(State)
     graph_builder.add_node("human_node", human_node)
     graph_builder.add_edge(START, "human_node")
-
-    checkpointer = InMemorySaver() # (4)!
-
+    checkpointer = InMemorySaver()  # (4)!
     graph = graph_builder.compile(checkpointer=checkpointer)
-
     # Pass a thread ID to the graph to run it.
     config = {"configurable": {"thread_id": uuid.uuid4()}}
-
     # Run the graph until the interrupt is hit.
-    result = graph.invoke({"some_text": "original text"}, config=config) # (5)!
+    result = graph.invoke({"some_text": "original text"}, config=config)  # (5)!
 
-    print(result['__interrupt__']) # (6)!
-    # > [
-    # >    Interrupt(
-    # >       value={'text_to_revise': 'original text'}, 
-    # >       resumable=True,
-    # >       ns=['human_node:6ce9e64f-edef-fe5d-f7dc-511fa9526960']
-    # >    )
-    # > ] 
+    print(result["__interrupt__"])  # (6)!
+    # > [Interrupt(value={'text_to_revise': 'original text'}, id='6d7c4048049254c83195429a3659661d')]
 
     # highlight-next-line
     print(graph.invoke(Command(resume="Edited text"), config=config)) # (7)!
@@ -142,7 +128,7 @@ print(graph.invoke(Command(resume="Edited text"), config=config)) # (7)!
 
 !!! tip "New in 0.4.0"
 
-      `__interrupt__` is a special key that will be returned when running the graph if the graph is interrupted. Support for `__interrupt__` in `invoke` and `ainvoke` has been added in version 0.4.0. If you're on an older version, you will only see `__interrupt__` in the result if you use `stream` or `astream`. You can also use `graph.get_state(thread_id)` to get the interrupt value.
+      `__interrupt__` is a special key that will be returned when running the graph if the graph is interrupted. Support for `__interrupt__` in `invoke` and `ainvoke` has been added in version 0.4.0. If you're on an older version, you will only see `__interrupt__` in the result if you use `stream` or `astream`. You can also use `graph.get_state(thread_id)` to get the interrupt value(s).
 
 !!! warning
 
@@ -159,19 +145,67 @@ To resume execution, use the [`Command`][langgraph.types.Command] primitive, whi
 graph.invoke(Command(resume={"age": "25"}), thread_config)
 ```
 
-### Resume multiple interrupts with one invocation
+## Resuming Multiple interrupts
 
-If you have multiple interrupts in the task queue, you can use `Command.resume` with a dictionary mapping of interrupt ids to resume with a single `invoke` / `stream` call.
+When nodes with interrupt conditions are run in parallel, it's possible to have multiple interrupts in the task queue.
+For example, the following graph has two nodes run in parallel that require human input:
+
+<figure markdown="1">
+![image](../assets/human_in_loop_parallel.png){: style="max-height:400px"}
+</figure>
+
+Once your graph has been interrupted and is stalled, you can resume all the interrupts at once with `Command.resume`, passing a dictionary mapping of interrupt ids to resume values.
 
-For example, once your graph has been interrupted (multiple times, theoretically) and is stalled:
 
 ```python
-resume_map = {
-    i.interrupt_id: f"human input for prompt {i.value}"
-    for i in parent.get_state(thread_config).interrupts
-}
+from typing import TypedDict
+import uuid
+from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.constants import START
+from langgraph.graph import StateGraph
+from langgraph.types import interrupt, Command
 
-parent_graph.invoke(Command(resume=resume_map), config=thread_config)
+
+class State(TypedDict):
+    text_1: str
+    text_2: str
+
+
+def human_node_1(state: State):
+    value = interrupt({"text_to_revise": state["text_1"]})
+    return {"text_1": value}
+
+
+def human_node_2(state: State):
+    value = interrupt({"text_to_revise": state["text_2"]})
+    return {"text_2": value}
+
+
+graph_builder = StateGraph(State)
+graph_builder.add_node("human_node_1", human_node_1)
+graph_builder.add_node("human_node_2", human_node_2)
+
+# Add both nodes in parallel from START
+graph_builder.add_edge(START, "human_node_1")
+graph_builder.add_edge(START, "human_node_2")
+
+checkpointer = InMemorySaver()
+graph = graph_builder.compile(checkpointer=checkpointer)
+
+thread_id = str(uuid.uuid4())
+config: RunnableConfig = {"configurable": {"thread_id": thread_id}}
+result = graph.invoke(
+    {"text_1": "original text 1", "text_2": "original text 2"}, config=config
+)
+
+# Resume with mapping of interrupt IDs to values
+resume_map = {
+    i.id: f"edited text for {i.value['text_to_revise']}"
+    for i in result["__interrupt__"]
+}
+print(graph.invoke(Command(resume=resume_map), config=config))
+# > {'text_1': 'edited text for original text 1', 'text_2': 'edited text for original text 2'}
 ```
 
 ## Common patterns
@@ -226,7 +260,7 @@ graph.invoke(Command(resume=True), config=thread_config)
     from langgraph.constants import START, END
     from langgraph.graph import StateGraph
     from langgraph.types import interrupt, Command
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
     # Define the shared graph state
     class State(TypedDict):
@@ -271,7 +305,7 @@ graph.invoke(Command(resume=True), config=thread_config)
     builder.add_edge("approved_path", END)
     builder.add_edge("rejected_path", END)
 
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
     graph = builder.compile(checkpointer=checkpointer)
 
     # Run until interrupt
@@ -339,7 +373,7 @@ graph.invoke(
     from langgraph.constants import START, END
     from langgraph.graph import StateGraph
     from langgraph.types import interrupt, Command
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
     # Define the graph state
     class State(TypedDict):
@@ -378,7 +412,7 @@ graph.invoke(
     builder.add_edge("downstream_use", END)
 
     # Set up in-memory checkpointing for interrupt support
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
     graph = builder.compile(checkpointer=checkpointer)
 
     # Invoke the graph until it hits the interrupt
@@ -388,14 +422,15 @@ graph.invoke(
     # Output interrupt payload
     print(result["__interrupt__"])
     # Example output:
-    # Interrupt(
-    #   value={
-    #     'task': 'Please review and edit the generated summary if necessary.',
-    #     'generated_summary': 'The cat sat on the mat and looked at the stars.'
-    #   },
-    #   resumable=True,
-    #   ...
-    # )
+    # > [
+    # >     Interrupt(
+    # >         value={
+    # >             'task': 'Please review and edit the generated summary if necessary.',
+    # >             'generated_summary': 'The cat sat on the mat and looked at the stars.'
+    # >         }, 
+    # >         id='...'
+    # >     )
+    # > ]
 
     # Resume the graph with human-edited input
     edited_summary = "The cat lay on the rug, gazing peacefully at the night sky."
@@ -655,7 +690,7 @@ def human_node(state: State):
     from langgraph.constants import START, END
     from langgraph.graph import StateGraph
     from langgraph.types import interrupt, Command
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
     # Define graph state
     class State(TypedDict):
@@ -694,7 +729,7 @@ def human_node(state: State):
     builder.add_edge("report_age", END)
 
     # Create the graph with a memory checkpointer
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
     graph = builder.compile(checkpointer=checkpointer)
 
     # Run the graph until the first interrupt
@@ -951,7 +986,7 @@ def node_in_parent_graph(state: State):
     from langgraph.graph import StateGraph
     from langgraph.constants import START
     from langgraph.types import interrupt, Command
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
 
     class State(TypedDict):
@@ -977,7 +1012,7 @@ def node_in_parent_graph(state: State):
         print(f"Got an answer of {answer}")
 
 
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
 
     subgraph_builder = StateGraph(State)
     subgraph_builder.add_node("some_node", node_in_subgraph)
@@ -1008,7 +1043,7 @@ def node_in_parent_graph(state: State):
     builder.add_edge(START, "parent_node")
 
     # A checkpointer must be enabled for interrupts to work!
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
     graph = builder.compile(checkpointer=checkpointer)
 
     config = {
@@ -1032,7 +1067,7 @@ def node_in_parent_graph(state: State):
     Entered `parent_node` a total of 1 times
     Entered `node_in_subgraph` a total of 1 times
     Entered human_node in sub-graph a total of 1 times
-    {'__interrupt__': (Interrupt(value='what is your name?', resumable=True, ns=['parent_node:4c3a0248-21f0-1287-eacf-3002bc304db4', 'human_node:2fe86d52-6f70-2a3f-6b2f-b1eededd6348'], when='during'),)}
+    {'__interrupt__': (Interrupt(value='what is your name?', id='...'),)}
     --- Resuming ---
     Entered `parent_node` a total of 2 times
     Entered human_node in sub-graph a total of 2 times
@@ -1040,7 +1075,7 @@ def node_in_parent_graph(state: State):
     {'parent_node': {'state_counter': 1}}
     ```
 
-### Using multiple interrupts
+### Using multiple interrupts in a single node
 
 Using multiple interrupts within a **single** node can be helpful for patterns like [validating human input](#validate-human-input). However, using multiple interrupts in the same node can lead to unexpected behavior if not handled carefully.
 
@@ -1057,7 +1092,7 @@ To avoid issues, refrain from dynamically changing the node's structure between
     from langgraph.graph import StateGraph
     from langgraph.constants import START 
     from langgraph.types import interrupt, Command
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
 
     class State(TypedDict):
@@ -1091,7 +1126,7 @@ To avoid issues, refrain from dynamically changing the node's structure between
     builder.add_edge(START, "human_node")
 
     # A checkpointer must be enabled for interrupts to work!
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
     graph = builder.compile(checkpointer=checkpointer)
 
     config = {
@@ -1108,7 +1143,7 @@ To avoid issues, refrain from dynamically changing the node's structure between
     ```
 
     ```pycon
-    {'__interrupt__': (Interrupt(value='what is your name?', resumable=True, ns=['human_node:3a007ef9-c30d-c357-1ec1-86a1a70d8fba'], when='during'),)}
+    {'__interrupt__': (Interrupt(value='what is your name?', id='...'),)}
     Name: N/A. Age: John
     {'human_node': {'age': 'John', 'name': 'N/A'}}
     ```

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

@@ -121,7 +121,7 @@
     "\n",
     "# highlight-next-line\n",
     "from langgraph.types import Command, interrupt\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "from IPython.display import Image, display\n",
     "\n",
     "\n",
@@ -157,7 +157,7 @@
     "builder.add_edge(\"step_3\", END)\n",
     "\n",
     "# Set up memory\n",
-    "memory = MemorySaver()\n",
+    "memory = InMemorySaver()\n",
     "\n",
     "# Add\n",
     "graph = builder.compile(checkpointer=memory)\n",
@@ -435,9 +435,9 @@
     "workflow.add_edge(\"ask_human\", \"agent\")\n",
     "\n",
     "# Set up memory\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "\n",
-    "memory = MemorySaver()\n",
+    "memory = InMemorySaver()\n",
     "\n",
     "# Finally, we compile it!\n",
     "# This compiles it into a LangChain Runnable,\n",

docs/docs/how-tos/multi-agent-multi-turn-convo-functional.ipynb

@@ -224,7 +224,7 @@
     "from langgraph.prebuilt import create_react_agent\n",
     "from langgraph.graph import add_messages\n",
     "from langgraph.func import entrypoint, task\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "from langgraph.types import interrupt, Command\n",
     "\n",
     "model = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n",
@@ -272,7 +272,7 @@
     "    return response[\"messages\"]\n",
     "\n",
     "\n",
-    "checkpointer = MemorySaver()\n",
+    "checkpointer = InMemorySaver()\n",
     "\n",
     "\n",
     "def string_to_uuid(input_string):\n",

docs/docs/how-tos/multi_agent.md

@@ -375,7 +375,7 @@ def agent(state) -> Command[Literal["agent", "another_agent", "human"]]:
     from langgraph.graph import MessagesState, StateGraph, START
     from langgraph.prebuilt import create_react_agent, InjectedState
     from langgraph.types import Command, interrupt
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
     
     
     model = ChatAnthropic(model="claude-3-5-sonnet-latest")
@@ -467,7 +467,7 @@ def agent(state) -> Command[Literal["agent", "another_agent", "human"]]:
     builder.add_edge(START, "travel_advisor")
     
     
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
     graph = builder.compile(checkpointer=checkpointer)
     ```
     

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

@@ -28,9 +28,9 @@
     "1. Create an instance of a checkpointer:\n",
     "\n",
     "    ```python\n",
-    "    from langgraph.checkpoint.memory import MemorySaver\n",
+    "    from langgraph.checkpoint.memory import InMemorySaver\n",
     "    \n",
-    "    checkpointer = MemorySaver()       \n",
+    "    checkpointer = InMemorySaver()       \n",
     "    ```\n",
     "\n",
     "2. Pass `checkpointer` instance to the `entrypoint()` decorator:\n",
@@ -184,7 +184,7 @@
     "from langchain_core.messages import BaseMessage\n",
     "from langgraph.graph import add_messages\n",
     "from langgraph.func import entrypoint, task\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "\n",
     "\n",
     "@task\n",
@@ -193,7 +193,7 @@
     "    return response\n",
     "\n",
     "\n",
-    "checkpointer = MemorySaver()\n",
+    "checkpointer = InMemorySaver()\n",
     "\n",
     "\n",
     "@entrypoint(checkpointer=checkpointer)\n",

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

@@ -261,7 +261,7 @@
     "\n",
     "To add thread-level persistence to our agent:\n",
     "\n",
-    "1. Select a [checkpointer](../../concepts/persistence#checkpointer-libraries): here we will use [MemorySaver](../../reference/checkpoints/#langgraph.checkpoint.memory.MemorySaver), a simple in-memory checkpointer.\n",
+    "1. Select a [checkpointer](../../concepts/persistence#checkpointer-libraries): here we will use [InMemorySaver](../../reference/checkpoints/#langgraph.checkpoint.memory.InMemorySaver), a simple in-memory checkpointer.\n",
     "2. Update our entrypoint to accept the previous messages state as a second argument. Here, we simply append the message updates to the previous sequence of messages.\n",
     "3. Choose which values will be returned from the workflow and which will be saved by the checkpointer as `previous` using `entrypoint.final` (optional)"
    ]
@@ -272,10 +272,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "\n",
     "# highlight-next-line\n",
-    "checkpointer = MemorySaver()\n",
+    "checkpointer = InMemorySaver()\n",
     "\n",
     "\n",
     "# highlight-next-line\n",

docs/docs/how-tos/use-functional-api.md

@@ -26,7 +26,7 @@ my_workflow.invoke({"value": 1, "another_value": 2})
     ```python
     import uuid
     from langgraph.func import entrypoint, task
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
     # Task that checks if a number is even
     @task
@@ -39,7 +39,7 @@ my_workflow.invoke({"value": 1, "another_value": 2})
         return "The number is even." if is_even else "The number is odd."
 
     # Create a checkpointer for persistence
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
 
     @entrypoint(checkpointer=checkpointer)
     def workflow(inputs: dict) -> str:
@@ -63,7 +63,7 @@ my_workflow.invoke({"value": 1, "another_value": 2})
     import uuid
     from langchain.chat_models import init_chat_model
     from langgraph.func import entrypoint, task
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
     llm = init_chat_model('openai:gpt-3.5-turbo')
 
@@ -77,7 +77,7 @@ my_workflow.invoke({"value": 1, "another_value": 2})
         ]).content
 
     # Create a checkpointer for persistence
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
 
     @entrypoint(checkpointer=checkpointer)
     def workflow(topic: str) -> str:
@@ -114,7 +114,7 @@ def graph(numbers: list[int]) -> list[str]:
     import uuid
     from langchain.chat_models import init_chat_model
     from langgraph.func import entrypoint, task
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
     # Initialize the LLM model
     llm = init_chat_model("openai:gpt-3.5-turbo")
@@ -129,7 +129,7 @@ def graph(numbers: list[int]) -> list[str]:
         return response.content
 
     # Create a checkpointer for persistence
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
 
     @entrypoint(checkpointer=checkpointer)
     def workflow(topics: list[str]) -> str:
@@ -176,7 +176,7 @@ def some_workflow(some_input: dict) -> int:
     import uuid
     from typing import TypedDict
     from langgraph.func import entrypoint
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
     from langgraph.graph import StateGraph
 
     # Define the shared state type
@@ -194,7 +194,7 @@ def some_workflow(some_input: dict) -> int:
     graph = builder.compile()
 
     # Define the functional API workflow
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
 
     @entrypoint(checkpointer=checkpointer)
     def workflow(x: int) -> dict:
@@ -227,10 +227,10 @@ def my_workflow(inputs: dict) -> int:
     ```python
     import uuid
     from langgraph.func import entrypoint
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
     # Initialize a checkpointer
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
 
     # A reusable sub-workflow that multiplies a number
     @entrypoint()
@@ -258,10 +258,10 @@ Example of using the streaming API to stream both updates and custom data.
 
 ```python
 from langgraph.func import entrypoint
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.config import get_stream_writer # (1)!
 
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 
 @entrypoint(checkpointer=checkpointer)
 def main(inputs: dict) -> int:
@@ -316,7 +316,7 @@ for mode, chunk in main.stream( # (5)!
 ## Retry policy
 
 ```python
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.func import entrypoint, task
 from langgraph.types import RetryPolicy
 
@@ -337,7 +337,7 @@ def get_info():
         raise ValueError('Failure')
     return "OK"
 
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 
 @entrypoint(checkpointer=checkpointer)
 def main(inputs, writer):
@@ -392,7 +392,7 @@ for chunk in main.stream({"x": 5}, stream_mode="updates"):
 
 ```python
 import time
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.func import entrypoint, task
 from langgraph.types import StreamWriter
 
@@ -414,7 +414,7 @@ def get_info():
     return "OK"
 
 # Initialize an in-memory checkpointer for persistence
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 
 @task
 def slow_task():
@@ -504,9 +504,9 @@ def step_3(input_query):
 We can now compose these tasks in an [entrypoint](../concepts/functional_api.md#entrypoint):
 
 ```python
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 
 
 @entrypoint(checkpointer=checkpointer)
@@ -577,12 +577,12 @@ def review_tool_call(tool_call: ToolCall) -> Union[ToolCall, ToolMessage]:
 We can now update our [entrypoint](../concepts/functional_api.md#entrypoint) to review the generated tool calls. If a tool call is accepted or revised, we execute in the same way as before. Otherwise, we just append the `ToolMessage` supplied by the human. The results of prior tasks — in this case the initial model call — are persisted, so that they are not run again following the `interrupt`.
 
 ```python
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph.message import add_messages
 from langgraph.types import Command, interrupt
 
 
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 
 
 @entrypoint(checkpointer=checkpointer)
@@ -757,9 +757,9 @@ Use `entrypoint.final` to decouple what is returned to the caller from what is p
 ```python
 from typing import Optional
 from langgraph.func import entrypoint
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 
 @entrypoint(checkpointer=checkpointer)
 def accumulate(n: int, *, previous: Optional[int]) -> entrypoint.final[int, int]:
@@ -777,14 +777,14 @@ print(accumulate.invoke(3, config=config))  # 3
 
 ### Chatbot example
 
-An example of a simple chatbot using the functional API and the `MemorySaver` checkpointer.
+An example of a simple chatbot using the functional API and the `InMemorySaver` checkpointer.
 The bot is able to remember the previous conversation and continue from where it left off.
 
 ```python
 from langchain_core.messages import BaseMessage
 from langgraph.graph import add_messages
 from langgraph.func import entrypoint, task
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langchain_anthropic import ChatAnthropic
 
 model = ChatAnthropic(model="claude-3-5-sonnet-latest")
@@ -794,7 +794,7 @@ def call_model(messages: list[BaseMessage]):
     response = model.invoke(messages)
     return response
 
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 
 @entrypoint(checkpointer=checkpointer)
 def workflow(inputs: list[BaseMessage], *, previous: list[BaseMessage]):

docs/docs/reference/constants.md

@@ -2,5 +2,6 @@
     options:
       members:
         - TAG_HIDDEN
+        - TAG_NOSTREAM
         - START
-        - END
+        - END

docs/docs/reference/runtime.md

@@ -0,0 +1,18 @@
+# Runtime
+
+::: langgraph.runtime.Runtime
+    options:
+      show_root_heading: true
+      show_root_full_path: false
+      members:
+        - context
+        - store
+        - stream_writer
+        - previous
+
+::: langgraph.runtime
+    options:
+      members:
+        - get_runtime
+
+

docs/docs/tutorials/chatbots/information-gather-prompting.ipynb

@@ -256,7 +256,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "from langgraph.graph import StateGraph, START\n",
     "from langgraph.graph.message import add_messages\n",
     "from typing import Annotated\n",
@@ -267,7 +267,7 @@
     "    messages: Annotated[list, add_messages]\n",
     "\n",
     "\n",
-    "memory = MemorySaver()\n",
+    "memory = InMemorySaver()\n",
     "workflow = StateGraph(State)\n",
     "workflow.add_node(\"info\", info_chain)\n",
     "workflow.add_node(\"prompt\", prompt_gen_chain)\n",

docs/docs/tutorials/customer-support/customer-support.ipynb

@@ -1124,7 +1124,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "from langgraph.graph import END, StateGraph, START\n",
     "from langgraph.prebuilt import tools_condition\n",
     "\n",
@@ -1144,7 +1144,7 @@
     "\n",
     "# The checkpointer lets the graph persist its state\n",
     "# this is a complete memory for the entire graph.\n",
-    "memory = MemorySaver()\n",
+    "memory = InMemorySaver()\n",
     "part_1_graph = builder.compile(checkpointer=memory)"
    ]
   },
@@ -1943,7 +1943,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "from langgraph.graph import StateGraph\n",
     "from langgraph.prebuilt import tools_condition\n",
     "\n",
@@ -1967,7 +1967,7 @@
     ")\n",
     "builder.add_edge(\"tools\", \"assistant\")\n",
     "\n",
-    "memory = MemorySaver()\n",
+    "memory = InMemorySaver()\n",
     "part_2_graph = builder.compile(\n",
     "    checkpointer=memory,\n",
     "    # NEW: The graph will always halt before executing the \"tools\" node.\n",
@@ -2532,7 +2532,7 @@
    "source": [
     "from typing import Literal\n",
     "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "from langgraph.graph import StateGraph\n",
     "from langgraph.prebuilt import tools_condition\n",
     "\n",
@@ -2576,7 +2576,7 @@
     "builder.add_edge(\"safe_tools\", \"assistant\")\n",
     "builder.add_edge(\"sensitive_tools\", \"assistant\")\n",
     "\n",
-    "memory = MemorySaver()\n",
+    "memory = InMemorySaver()\n",
     "part_3_graph = builder.compile(\n",
     "    checkpointer=memory,\n",
     "    # NEW: The graph will always halt before executing the \"tools\" node.\n",
@@ -3477,7 +3477,7 @@
    "source": [
     "from typing import Literal\n",
     "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "from langgraph.graph import StateGraph\n",
     "from langgraph.prebuilt import tools_condition\n",
     "\n",
@@ -3841,7 +3841,7 @@
     "builder.add_conditional_edges(\"fetch_user_info\", route_to_workflow)\n",
     "\n",
     "# Compile graph\n",
-    "memory = MemorySaver()\n",
+    "memory = InMemorySaver()\n",
     "part_4_graph = builder.compile(\n",
     "    checkpointer=memory,\n",
     "    # Let the user approve or deny the use of sensitive tools\n",

docs/docs/tutorials/get-started/1-build-basic-chatbot.md

@@ -1,6 +1,6 @@
 # Build a basic chatbot
 
-In this tutorial, you will build a basic chatbot. This chatbot is the basis for the following series of tutorials where you will progressively add more sophisticated capabilities, and be introduced to key LangGraph concepts along the way. Let’s dive in! 🌟
+In this tutorial, you will build a basic chatbot. This chatbot is the basis for the following series of tutorials where you will progressively add more sophisticated capabilities, and be introduced to key LangGraph concepts along the way. Let's dive in! 🌟
 
 ## Prerequisites
 
@@ -13,13 +13,45 @@ tool-calling features, such as [OpenAI](https://platform.openai.com/api-keys),
 
 Install the required packages:
 
+:::python
+
 ```bash
 pip install -U langgraph langsmith
 ```
 
+:::
+
+:::js
+=== "npm"
+
+    ```bash
+    npm install @langchain/langgraph @langchain/core zod
+    ```
+
+=== "yarn"
+
+    ```bash
+    yarn add @langchain/langgraph @langchain/core zod
+    ```
+
+=== "pnpm"
+
+    ```bash
+    pnpm add @langchain/langgraph @langchain/core zod
+    ```
+
+=== "bun"
+
+    ```bash
+    bun add @langchain/langgraph @langchain/core zod
+    ```
+
+:::
+
 !!! tip
 
-    Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph. For more information on how to get started, see [LangSmith docs](https://docs.smith.langchain.com). 
+    Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph. For more information on how to get started, see [LangSmith docs](https://docs.smith.langchain.com).
+    Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph. For more information on how to get started, see [LangSmith docs](https://docs.smith.langchain.com).
 
 ## 2. Create a `StateGraph`
 
@@ -27,6 +59,8 @@ Now you can create a basic chatbot using LangGraph. This chatbot will respond di
 
 Start by creating a `StateGraph`. A `StateGraph` object defines the structure of our chatbot as a "state machine". We'll add `nodes` to represent the llm and functions our chatbot can call and `edges` to specify how the bot should transition between these functions.
 
+:::python
+
 ```python
 from typing import Annotated
 
@@ -46,24 +80,43 @@ class State(TypedDict):
 graph_builder = StateGraph(State)
 ```
 
+:::
+
+:::js
+
+```typescript
+import { StateGraph, MessagesZodState, START } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const graph = new StateGraph(State).compile();
+```
+
+:::
+
 Our graph can now handle two key tasks:
 
 1. Each `node` can receive the current `State` as input and output an update to the state.
-2. Updates to `messages` will be appended to the existing list rather than overwriting it, thanks to the prebuilt [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages) function used with the `Annotated` syntax.
+2. Updates to `messages` will be appended to the existing list rather than overwriting it, thanks to the prebuilt reducer function.
 
-------
+---
+
+---
 
 !!! tip "Concept"
 
-    When defining a graph, the first step is to define its `State`. The `State` includes the graph's schema and [reducer functions](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) that handle state updates. In our example, `State` is a `TypedDict` with one key: `messages`. The [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages) reducer function is used to append new messages to the list instead of overwriting it. Keys without a reducer annotation will overwrite previous values. To learn more about state, reducers, and related concepts, see [LangGraph reference docs](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages).
+    When defining a graph, the first step is to define its `State`. The `State` includes the graph's schema and [reducer functions](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) that handle state updates. In our example, `State` is a schema with one key: `messages`. The reducer function is used to append new messages to the list instead of overwriting it. Keys without a reducer annotation will overwrite previous values.
+
+    To learn more about state, reducers, and related concepts, see [LangGraph reference docs](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages).
 
 ## 3. Add a node
 
-Next, add a "`chatbot`" node. **Nodes** represent units of work and are typically regular Python functions.
+Next, add a "`chatbot`" node. **Nodes** represent units of work and are typically regular functions.
 
 Let's first select a chat model:
 
-{% include-markdown "../../../snippets/chat_model_tabs.md" %}
+{!snippets/chat_model_tabs.md!}
 
 <!---
 ```python
@@ -73,9 +126,26 @@ llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
 ```
 -->
 
+:::
+
+:::js
+
+```typescript
+import { ChatOpenAI } from "@langchain/openai";
+// or import { ChatAnthropic } from "@langchain/anthropic";
+
+const llm = new ChatOpenAI({
+  model: "gpt-4o",
+  temperature: 0,
+});
+```
+
+:::
 
 We can now incorporate the chat model into a simple node:
 
+:::python
+
 ```python
 
 def chatbot(state: State):
@@ -88,38 +158,133 @@ def chatbot(state: State):
 graph_builder.add_node("chatbot", chatbot)
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="7-9"
+import { StateGraph, MessagesZodState, START } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state: z.infer<typeof State>) => {
+    return { messages: [await llm.invoke(state.messages)] };
+  })
+  .compile();
+```
+
+:::
+
 **Notice** how the `chatbot` node function takes the current `State` as input and returns a dictionary containing an updated `messages` list under the key "messages". This is the basic pattern for all LangGraph node functions.
 
+:::python
 The `add_messages` function in our `State` will append the LLM's response messages to whatever messages are already in the state.
+:::
+
+:::js
+The `addMessages` function used within `MessagesZodState` will append the LLM's response messages to whatever messages are already in the state.
+:::
 
 ## 4. Add an `entry` point
 
 Add an `entry` point to tell the graph **where to start its work** each time it is run:
 
+:::python
+
 ```python
 graph_builder.add_edge(START, "chatbot")
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="10"
+import { StateGraph, MessagesZodState, START } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state: z.infer<typeof State>) => {
+    return { messages: [await llm.invoke(state.messages)] };
+  })
+  .addEdge(START, "chatbot")
+  .compile();
+```
+
+:::
+
 ## 5. Add an `exit` point
 
 Add an `exit` point to indicate **where the graph should finish execution**. This is helpful for more complex flows, but even in a simple graph like this, adding an end node improves clarity.
 
+:::python
+
 ```python
 graph_builder.add_edge("chatbot", END)
 ```
+
+:::
+
+:::js
+
+```typescript hl_lines="11"
+import { StateGraph, MessagesZodState, START, END } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state: z.infer<typeof State>) => {
+    return { messages: [await llm.invoke(state.messages)] };
+  })
+  .addEdge(START, "chatbot")
+  .addEdge("chatbot", END)
+  .compile();
+```
+
+:::
+
 This tells the graph to terminate after running the chatbot node.
 
 ## 6. Compile the graph
 
 Before running the graph, we'll need to compile it. We can do so by calling `compile()`
-on the graph builder. This creates a `CompiledStateGraph` we can invoke on our state.
+on the graph builder. This creates a `CompiledGraph` we can invoke on our state.
+
+:::python
 
 ```python
 graph = graph_builder.compile()
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="12"
+import { StateGraph, MessagesZodState, START, END } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state: z.infer<typeof State>) => {
+    return { messages: [await llm.invoke(state.messages)] };
+  })
+  .addEdge(START, "chatbot")
+  .addEdge("chatbot", END)
+  .compile();
+```
+
+:::
+
 ## 7. Visualize the graph (optional)
 
+:::python
 You can visualize the graph using the `get_graph` method and one of the "draw" methods, like `draw_ascii` or `draw_png`. The `draw` methods each require additional dependencies.
 
 ```python
@@ -132,17 +297,35 @@ except Exception:
     pass
 ```
 
-![basic chatbot diagram](basic-chatbot.png)
+:::
 
+:::js
+You can visualize the graph using the `getGraph` method and render the graph with the `drawMermaidPng` method.
+
+```typescript
+import * as fs from "node:fs/promises";
+
+const drawableGraph = await graph.getGraphAsync();
+const image = await drawableGraph.drawMermaidPng();
+const imageBuffer = new Uint8Array(await image.arrayBuffer());
+
+await fs.writeFile("basic-chatbot.png", imageBuffer);
+```
+
+:::
+
+![basic chatbot diagram](basic-chatbot.png)
 
 ## 8. Run the chatbot
 
-Now run the chatbot! 
+Now run the chatbot!
 
 !!! tip
 
     You can exit the chat loop at any time by typing `quit`, `exit`, or `q`.
 
+:::python
+
 ```python
 def stream_graph_updates(user_input: str):
     for event in graph.stream({"messages": [{"role": "user", "content": user_input}]}):
@@ -165,15 +348,90 @@ while True:
         break
 ```
 
+:::
+
+:::js
+
+```typescript
+import { HumanMessage } from "@langchain/core/messages";
+
+async function streamGraphUpdates(userInput: string) {
+  const stream = await graph.stream({
+    messages: [new HumanMessage(userInput)],
+  });
+
+import * as readline from "node:readline/promises";
+import { StateGraph, MessagesZodState, START, END } from "@langchain/langgraph";
+import { ChatOpenAI } from "@langchain/openai";
+import { z } from "zod";
+
+const llm = new ChatOpenAI({ model: "gpt-4o-mini" });
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state: z.infer<typeof State>) => {
+    return { messages: [await llm.invoke(state.messages)] };
+  })
+  .addEdge(START, "chatbot")
+  .addEdge("chatbot", END)
+  .compile();
+
+async function generateText(content: string) {
+  const stream = await graph.stream(
+    { messages: [{ type: "human", content }] },
+    { streamMode: "values" }
+  );
+
+  for await (const event of stream) {
+    for (const value of Object.values(event)) {
+      console.log(
+        "Assistant:",
+        value.messages[value.messages.length - 1].content
+      );
+    const lastMessage = event.messages.at(-1);
+    if (lastMessage?.getType() === "ai") {
+      console.log(`Assistant: ${lastMessage.text}`);
+    }
+  }
+}
+
+const prompt = readline.createInterface({
+  input: process.stdin,
+  output: process.stdout,
+});
+
+while (true) {
+  const human = await prompt.question("User: ");
+  if (["quit", "exit", "q"].includes(human.trim())) break;
+  await generateText(human || "What do you know about LangGraph?");
+}
+
+prompt.close();
+```
+
+:::
+
 ```
 Assistant: LangGraph is a library designed to help build stateful multi-agent applications using language models. It provides tools for creating workflows and state machines to coordinate multiple AI agents or language model interactions. LangGraph is built on top of LangChain, leveraging its components while adding graph-based coordination capabilities. It's particularly useful for developing more complex, stateful AI applications that go beyond simple query-response interactions.
+```
+
+:::python
+
+```
 Goodbye!
 ```
 
+:::
+
 **Congratulations!** You've built your first chatbot using LangGraph. This bot can engage in basic conversation by taking user input and generating responses using an LLM. You can inspect a [LangSmith Trace](https://smith.langchain.com/public/7527e308-9502-4894-b347-f34385740d5a/r) for the call above.
 
+:::python
+
 Below is the full code for this tutorial:
 
+:::python
+
 ```python
 from typing import Annotated
 
@@ -207,8 +465,44 @@ graph_builder.add_edge("chatbot", END)
 graph = graph_builder.compile()
 ```
 
+:::
+
+:::js
+
+```typescript
+import { Annotation } from "@langchain/langgraph";
+import { StateGraph, START, END } from "@langchain/langgraph";
+import { BaseMessage, HumanMessage } from "@langchain/core/messages";
+import { ChatOpenAI } from "@langchain/openai";
+
+const State = Annotation.Root({
+  messages: Annotation<BaseMessage[]>({
+    reducer: (x, y) => x.concat(y),
+  }),
+});
+
+const graphBuilder = new StateGraph(State);
+
+const llm = new ChatOpenAI({
+  model: "gpt-4o",
+  temperature: 0,
+});
+
+const chatbot = async (state: typeof State.State) => {
+  return { messages: [await llm.invoke(state.messages)] };
+};
+
+// The first argument is the unique node name
+// The second argument is the function or object that will be called whenever
+// the node is used.
+graphBuilder.addNode("chatbot", chatbot);
+graphBuilder.addEdge(START, "chatbot");
+graphBuilder.addEdge("chatbot", END);
+const graph = graphBuilder.compile();
+```
+
+:::
+
 ## Next steps
 
 You may have noticed that the bot's knowledge is limited to what's in its training data. In the next part, we'll [add a web search tool](./2-add-tools.md) to expand the bot's knowledge and make it more capable.
-
-

docs/docs/tutorials/get-started/2-add-tools.md

@@ -10,24 +10,65 @@ To handle queries that your chatbot can't answer "from memory", integrate a web
 
 Before you start this tutorial, ensure you have the following:
 
+:::python
+
 - An API key for the [Tavily Search Engine](https://python.langchain.com/docs/integrations/tools/tavily_search/).
 
+:::
+
+:::js
+
+- An API key for the [Tavily Search Engine](https://js.langchain.com/docs/integrations/tools/tavily_search/).
+
+:::
+
 ## 1. Install the search engine
 
+:::python
 Install the requirements to use the [Tavily Search Engine](https://python.langchain.com/docs/integrations/tools/tavily_search/):
 
 ```bash
 pip install -U langchain-tavily
 ```
+
+:::
+
+:::js
+Install the requirements to use the [Tavily Search Engine](https://docs.tavily.com/):
+
+=== "npm"
+
+    ```bash
+    npm install @langchain/tavily
+    ```
+
+=== "yarn"
+
+    ```bash
+    yarn add @langchain/tavily
+    ```
+
+=== "pnpm"
+
+    ```bash
+    pnpm add @langchain/tavily
+    ```
+
+=== "bun"
+
+    ```bash
+    bun add @langchain/tavily
+    ```
+
+:::
+
 ## 2. Configure your environment
 
 Configure your environment with your search engine API key:
 
-```python
-def _set_env(var: str):
-    if not os.environ.get(var):
-        os.environ[var] = getpass.getpass(f"{var}: ")
+:::python
 
+```bash
 _set_env("TAVILY_API_KEY")
 ```
 
@@ -35,10 +76,22 @@ _set_env("TAVILY_API_KEY")
 os.environ["TAVILY_API_KEY"]:  "········"
 ```
 
+:::
+
+:::js
+
+```typescript
+process.env.TAVILY_API_KEY = "tvly-...";
+```
+
+:::
+
 ## 3. Define the tool
 
 Define the web search tool:
 
+:::python
+
 ```python
 from langchain_tavily import TavilySearch
 
@@ -47,8 +100,25 @@ tools = [tool]
 tool.invoke("What's a 'node' in LangGraph?")
 ```
 
+:::
+
+:::js
+
+```typescript
+import { TavilySearch } from "@langchain/tavily";
+
+const tool = new TavilySearch({ maxResults: 2 });
+const tools = [tool];
+
+await tool.invoke({ query: "What's a 'node' in LangGraph?" });
+```
+
+:::
+
 The results are page summaries our chat bot can use to answer questions:
 
+:::python
+
 ```
 {'query': "What's a 'node' in LangGraph?",
 'follow_up_questions': None,
@@ -67,13 +137,51 @@ The results are page summaries our chat bot can use to answer questions:
 'response_time': 1.38}
 ```
 
+:::
+
+:::js
+
+```json
+{
+  "query": "What's a 'node' in LangGraph?",
+  "follow_up_questions": null,
+  "answer": null,
+  "images": [],
+  "results": [
+    {
+      "url": "https://blog.langchain.dev/langgraph/",
+      "title": "LangGraph - LangChain Blog",
+      "content": "TL;DR: LangGraph is module built on top of LangChain to better enable creation of cyclical graphs, often needed for agent runtimes. This state is updated by nodes in the graph, which return operations to attributes of this state (in the form of a key-value store). After adding nodes, you can then add edges to create the graph. An example of this may be in the basic agent runtime, where we always want the model to be called after we call a tool. The state of this graph by default contains concepts that should be familiar to you if you've used LangChain agents: `input`, `chat_history`, `intermediate_steps` (and `agent_outcome` to represent the most recent agent outcome)",
+      "score": 0.7407191,
+      "raw_content": null
+    },
+    {
+      "url": "https://medium.com/@cplog/introduction-to-langgraph-a-beginners-guide-14f9be027141",
+      "title": "Introduction to LangGraph: A Beginner's Guide - Medium",
+      "content": "*   **Stateful Graph:** LangGraph revolves around the concept of a stateful graph, where each node in the graph represents a step in your computation, and the graph maintains a state that is passed around and updated as the computation progresses. LangGraph supports conditional edges, allowing you to dynamically determine the next node to execute based on the current state of the graph. Image 10: Introduction to AI Agent with LangChain and LangGraph: A Beginner’s Guide Image 18: How to build LLM Agent with LangGraph — StateGraph and Reducer Image 20: Simplest Graphs using LangGraph Framework Image 24: Building a ReAct Agent with Langgraph: A Step-by-Step Guide Image 28: Building an Agentic RAG with LangGraph: A Step-by-Step Guide",
+      "score": 0.65279555,
+      "raw_content": null
+    }
+  ],
+  "response_time": 1.34
+}
+```
+
+:::
+
 ## 4. Define the graph
 
+:::python
 For the `StateGraph` you created in the [first tutorial](./1-build-basic-chatbot.md), add `bind_tools` on the LLM. This lets the LLM know the correct JSON format to use if it wants to use the search engine.
+:::
+
+:::js
+For the `StateGraph` you created in the [first tutorial](./1-build-basic-chatbot.md), add `bindTools` on the LLM. This lets the LLM know the correct JSON format to use if it wants to use the search engine.
+:::
 
 Let's first select our LLM:
 
-{% include-markdown "../../../snippets/chat_model_tabs.md" %}
+{!snippets/chat_model_tabs.md!}
 
 <!---
 ```python
@@ -83,8 +191,22 @@ llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
 ```
 -->
 
+:::
+
+:::js
+
+```typescript
+import { ChatAnthropic } from "@langchain/anthropic";
+
+const llm = new ChatAnthropic({ model: "claude-3-5-sonnet-latest" });
+```
+
+:::
+
 We can now incorporate it into a `StateGraph`:
 
+:::python
+
 ```python hl_lines="15"
 from typing import Annotated
 
@@ -108,9 +230,31 @@ def chatbot(state: State):
 graph_builder.add_node("chatbot", chatbot)
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="7-8"
+import { StateGraph, MessagesZodState } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const chatbot = async (state: z.infer<typeof State>) => {
+  // Modification: tell the LLM which tools it can call
+  const llmWithTools = llm.bindTools(tools);
+
+  return { messages: [await llmWithTools.invoke(state.messages)] };
+};
+```
+
+:::
+
 ## 5. Create a function to run the tools
 
-Now, create a function to run the tools if they are called. Do this by adding the tools to a new node called`BasicToolNode` that checks the most recent message in the state and calls tools if the message contains `tool_calls`. It relies on the LLM's `tool_calling` support, which is available in Anthropic, OpenAI, Google Gemini, and a number of other LLM providers.
+:::python
+
+Now, create a function to run the tools if they are called. Do this by adding the tools to a new node called `BasicToolNode` that checks the most recent message in the state and calls tools if the message contains `tool_calls`. It relies on the LLM's `tool_calling` support, which is available in Anthropic, OpenAI, Google Gemini, and a number of other LLM providers.
 
 ```python
 import json
@@ -152,16 +296,80 @@ graph_builder.add_node("tools", tool_node)
 
     If you do not want to build this yourself in the future, you can use LangGraph's prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/agents/#langgraph.prebuilt.tool_node.ToolNode).
 
+:::
+
+:::js
+
+Now, create a function to run the tools if they are called. Do this by adding the tools to a new node called `"tools"` that checks the most recent message in the state and calls tools if the message contains `tool_calls`. It relies on the LLM's tool calling support, which is available in Anthropic, OpenAI, Google Gemini, and a number of other LLM providers.
+
+```typescript
+import type { StructuredToolInterface } from "@langchain/core/tools";
+import { isAIMessage, ToolMessage } from "@langchain/core/messages";
+
+function createToolNode(tools: StructuredToolInterface[]) {
+  const toolByName: Record<string, StructuredToolInterface> = {};
+  for (const tool of tools) {
+    toolByName[tool.name] = tool;
+  }
+
+  return async (inputs: z.infer<typeof State>) => {
+    const { messages } = inputs;
+    if (!messages || messages.length === 0) {
+      throw new Error("No message found in input");
+    }
+
+    const message = messages.at(-1);
+    if (!message || !isAIMessage(message) || !message.tool_calls) {
+      throw new Error("Last message is not an AI message with tool calls");
+    }
+
+    const outputs: ToolMessage[] = [];
+    for (const toolCall of message.tool_calls) {
+      if (!toolCall.id) throw new Error("Tool call ID is required");
+
+      const tool = toolByName[toolCall.name];
+      if (!tool) throw new Error(`Tool ${toolCall.name} not found`);
+
+      const result = await tool.invoke(toolCall.args);
+
+      outputs.push(
+        new ToolMessage({
+          content: JSON.stringify(result),
+          name: toolCall.name,
+          tool_call_id: toolCall.id,
+        })
+      );
+    }
+
+    return { messages: outputs };
+  };
+}
+```
+
+!!! note
+
+    If you do not want to build this yourself in the future, you can use LangGraph's prebuilt [ToolNode](https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph_prebuilt.ToolNode.html).
+
+:::
+
 ## 6. Define the `conditional_edges`
 
-With the tool node added, now you can define the `conditional_edges`. 
+With the tool node added, now you can define the `conditional_edges`.
 
 **Edges** route the control flow from one node to the next. **Conditional edges** start from a single node and usually contain "if" statements to route to different nodes depending on the current graph state. These functions receive the current graph `state` and return a string or list of strings indicating which node(s) to call next.
 
-Next, define a router function called `route_tools` that checks for `tool_calls` in the chatbot's output. Provide this function to the graph by calling `add_conditional_edges`, which tells the graph that whenever the `chatbot` node completes to check this function to see where to go next. 
+:::python
+Next, define a router function called `route_tools` that checks for `tool_calls` in the chatbot's output. Provide this function to the graph by calling `add_conditional_edges`, which tells the graph that whenever the `chatbot` node completes to check this function to see where to go next.
+:::
+
+:::js
+Next, define a router function called `routeTools` that checks for `tool_calls` in the chatbot's output. Provide this function to the graph by calling `addConditionalEdges`, which tells the graph that whenever the `chatbot` node completes to check this function to see where to go next.
+:::
 
 The condition will route to `tools` if tool calls are present and `END` if not. Because the condition can return `END`, you do not need to explicitly set a `finish_point` this time.
 
+:::python
+
 ```python
 def route_tools(
     state: State,
@@ -201,10 +409,61 @@ graph = graph_builder.compile()
 
 !!! note
 
-    You can replace this with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition) to be more concise. 
+    You can replace this with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition) to be more concise.
+
+:::
+
+:::js
+
+```typescript
+import { END, START } from "@langchain/langgraph";
+
+const routeTools = (state: z.infer<typeof State>) => {
+  /**
+   * Use as conditional edge to route to the ToolNode if the last message
+   * has tool calls.
+   */
+  const lastMessage = state.messages.at(-1);
+  if (
+    lastMessage &&
+    isAIMessage(lastMessage) &&
+    lastMessage.tool_calls?.length
+  ) {
+    return "tools";
+  }
+
+  /** Otherwise, route to the end. */
+  return END;
+};
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", chatbot)
+
+  // The `routeTools` function returns "tools" if the chatbot asks to use a tool, and "END" if
+  // it is fine directly responding. This conditional routing defines the main agent loop.
+  .addNode("tools", createToolNode(tools))
+
+  // Start the graph with the chatbot
+  .addEdge(START, "chatbot")
+
+  // The `routeTools` function returns "tools" if the chatbot asks to use a tool, and "END" if
+  // it is fine directly responding.
+  .addConditionalEdges("chatbot", routeTools, ["tools", END])
+
+  // Any time a tool is called, we need to return to the chatbot
+  .addEdge("tools", "chatbot")
+  .compile();
+```
+
+!!! note
+
+    You can replace this with the prebuilt [toolsCondition](https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.toolsCondition.html) to be more concise.
+
+:::
 
 ## 7. Visualize the graph (optional)
 
+:::python
 You can visualize the graph using the `get_graph` method and one of the "draw" methods, like `draw_ascii` or `draw_png`. The `draw` methods each require additional dependencies.
 
 ```python
@@ -217,12 +476,31 @@ except Exception:
     pass
 ```
 
+:::
+
+:::js
+You can visualize the graph using the `getGraph` method and render the graph with the `drawMermaidPng` method.
+
+```typescript
+import * as fs from "node:fs/promises";
+
+const drawableGraph = await graph.getGraphAsync();
+const image = await drawableGraph.drawMermaidPng();
+const imageBuffer = new Uint8Array(await image.arrayBuffer());
+
+await fs.writeFile("chatbot-with-tools.png", imageBuffer);
+```
+
+:::
+
 ![chatbot-with-tools-diagram](chatbot-with-tools.png)
 
 ## 8. Ask the bot questions
 
 Now you can ask the chatbot questions outside its training data:
 
+:::python
+
 ```python
 def stream_graph_updates(user_input: str):
     for event in graph.stream({"messages": [{"role": "user", "content": user_input}]}):
@@ -245,7 +523,7 @@ while True:
         break
 ```
 
-``` 
+```
 Assistant: [{'text': "To provide you with accurate and up-to-date information about LangGraph, I'll need to search for the latest details. Let me do that for you.", 'type': 'text'}, {'id': 'toolu_01Q588CszHaSvvP2MxRq9zRD', 'input': {'query': 'LangGraph AI tool information'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
 Assistant: [{"url": "https://www.langchain.com/langgraph", "content": "LangGraph sets the foundation for how we can build and scale AI workloads \u2014 from conversational agents, complex task automation, to custom LLM-backed experiences that 'just work'. The next chapter in building complex production-ready features with LLMs is agentic, and with LangGraph and LangSmith, LangChain delivers an out-of-the-box solution ..."}, {"url": "https://github.com/langchain-ai/langgraph", "content": "Overview. LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures ..."}]
 Assistant: Based on the search results, I can provide you with information about LangGraph:
@@ -276,18 +554,99 @@ Assistant: Based on the search results, I can provide you with information about
 
 LangGraph appears to be a significant tool in the evolving landscape of LLM-based application development, offering developers new ways to create more complex, stateful, and interactive AI systems.
 Goodbye!
-Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
 ```
 
+:::
+
+:::js
+
+```typescript
+import readline from "node:readline/promises";
+
+const prompt = readline.createInterface({
+  input: process.stdin,
+  output: process.stdout,
+});
+
+async function generateText(content: string) {
+  const stream = await graph.stream(
+    { messages: [{ type: "human", content }] },
+    { streamMode: "values" }
+  );
+
+  for await (const event of stream) {
+    const lastMessage = event.messages.at(-1);
+
+    if (lastMessage?.getType() === "ai" || lastMessage?.getType() === "tool") {
+      console.log(`Assistant: ${lastMessage?.text}`);
+    }
+  }
+}
+
+while (true) {
+  const human = await prompt.question("User: ");
+  if (["quit", "exit", "q"].includes(human.trim())) break;
+  await generateText(human || "What do you know about LangGraph?");
+}
+
+prompt.close();
+```
+
+```
+User: What do you know about LangGraph?
+Assistant: I'll search for the latest information about LangGraph for you.
+Assistant: [{"title":"Introduction to LangGraph: A Beginner's Guide - Medium","url":"https://medium.com/@cplog/introduction-to-langgraph-a-beginners-guide-14f9be027141","content":"..."}]
+Assistant: Based on the search results, I can provide you with information about LangGraph:
+
+LangGraph is a library within the LangChain ecosystem designed for building stateful, multi-actor applications with Large Language Models (LLMs). Here are the key aspects:
+
+**Core Purpose:**
+- LangGraph is specifically designed for creating agent and multi-agent workflows
+- It provides a framework for defining, coordinating, and executing multiple LLM agents in a structured manner
+
+**Key Features:**
+1. **Stateful Graph Architecture**: LangGraph revolves around a stateful graph where each node represents a step in computation, and the graph maintains state that is passed around and updated as the computation progresses
+
+2. **Conditional Edges**: It supports conditional edges, allowing you to dynamically determine the next node to execute based on the current state of the graph
+
+3. **Cycles**: Unlike other LLM frameworks, LangGraph allows you to define flows that involve cycles, which is essential for most agentic architectures
+
+4. **Controllability**: It offers enhanced control over the application flow
+
+5. **Persistence**: The library provides ways to maintain state and persistence in LLM-based applications
+
+**Use Cases:**
+- Conversational agents
+- Complex task automation
+- Custom LLM-backed experiences
+- Multi-agent systems that perform complex tasks
+
+**Benefits:**
+LangGraph allows developers to focus on the high-level logic of their applications rather than the intricacies of agent coordination, making it easier to build complex, production-ready features with LLMs.
+
+This makes LangGraph a significant tool in the evolving landscape of LLM-based application development.
+```
+
+:::
+
 ## 9. Use prebuilts
 
 For ease of use, adjust your code to replace the following with LangGraph prebuilt components. These have built in functionality like parallel API execution.
 
+:::python
+
 - `BasicToolNode` is replaced with the prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)
 - `route_tools` is replaced with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition)
 
 {% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
+<!---
+```python
+from langchain.chat_models import init_chat_model
+
+llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
+```
+-->
 
 ```python hl_lines="25 30"
 from typing import Annotated
@@ -327,7 +686,46 @@ graph_builder.add_edge(START, "chatbot")
 graph = graph_builder.compile()
 ```
 
-**Congratulations!** You've created a conversational agent in LangGraph that can use a search engine to retrieve updated information when needed. Now it can handle a wider range of user queries. To inspect all the steps your agent just took, check out this [LangSmith trace](https://smith.langchain.com/public/4fbd7636-25af-4638-9587-5a02fdbb0172/r).
+:::
+
+:::js
+
+- `createToolNode` is replaced with the prebuilt [ToolNode](https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph_prebuilt.ToolNode.html)
+- `routeTools` is replaced with the prebuilt [toolsCondition](https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.toolsCondition.html)
+
+```typescript
+import { TavilySearch } from "@langchain/tavily";
+import { ChatOpenAI } from "@langchain/openai";
+import { StateGraph, START, MessagesZodState, END } from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const tools = [new TavilySearch({ maxResults: 2 })];
+
+const llm = new ChatOpenAI({ model: "gpt-4o-mini" }).bindTools(tools);
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state) => ({
+    messages: [await llm.invoke(state.messages)],
+  }))
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  .compile();
+```
+
+:::
+
+**Congratulations!** You've created a conversational agent in LangGraph that can use a search engine to retrieve updated information when needed. Now it can handle a wider range of user queries.
+
+:::python
+
+To inspect all the steps your agent just took, check out this [LangSmith trace](https://smith.langchain.com/public/4fbd7636-25af-4638-9587-5a02fdbb0172/r).
+
+:::
 
 ## Next steps
 

docs/docs/tutorials/get-started/3-add-memory.md

@@ -2,7 +2,7 @@
 
 The chatbot can now [use tools](./2-add-tools.md) to answer user questions, but it does not remember the context of previous interactions. This limits its ability to have coherent, multi-turn conversations.
 
-LangGraph solves this problem through **persistent checkpointing**. If you provide a `checkpointer` when compiling the graph and a `thread_id` when calling your graph, LangGraph automatically saves the state after each step. When you invoke the graph again using the same `thread_id`, the graph loads its saved state, allowing the chatbot to pick up where it left off. 
+LangGraph solves this problem through **persistent checkpointing**. If you provide a `checkpointer` when compiling the graph and a `thread_id` when calling your graph, LangGraph automatically saves the state after each step. When you invoke the graph again using the same `thread_id`, the graph loads its saved state, allowing the chatbot to pick up where it left off.
 
 We will see later that **checkpointing** is _much_ more powerful than simple chat memory - it lets you save and resume complex state at any time for error recovery, human-in-the-loop workflows, time travel interactions, and more. But first, let's add checkpointing to enable multi-turn conversations.
 
@@ -10,47 +10,83 @@ We will see later that **checkpointing** is _much_ more powerful than simple cha
 
     This tutorial builds on [Add tools](./2-add-tools.md).
 
-## 1. Create a `MemorySaver` checkpointer
+## 1. Create a `InMemorySaver` checkpointer
 
-Create a `MemorySaver` checkpointer:
+Create a `InMemorySaver` checkpointer:
 
-``` python
-from langgraph.checkpoint.memory import MemorySaver
+:::python
 
-memory = MemorySaver()
+```python
+from langgraph.checkpoint.memory import InMemorySaver
+
+memory = InMemorySaver()
 ```
 
+:::
+
+:::js
+
+```typescript
+import { MemorySaver } from "@langchain/langgraph";
+
+const memory = new MemorySaver();
+```
+
+:::
+
 This is in-memory checkpointer, which is convenient for the tutorial. However, in a production application, you would likely change this to use `SqliteSaver` or `PostgresSaver` and connect a database.
 
 ## 2. Compile the graph
 
 Compile the graph with the provided checkpointer, which will checkpoint the `State` as the graph works through each node:
 
-``` python
+:::python
+
+```python
 graph = graph_builder.compile(checkpointer=memory)
 ```
 
-``` python
-from IPython.display import Image, display
+:::
 
-try:
-    display(Image(graph.get_graph().draw_mermaid_png()))
-except Exception:
-    # This requires some extra dependencies and is optional
-    pass
+:::js
+
+```typescript hl_lines="7"
+const graph = new StateGraph(State)
+  .addNode("chatbot", chatbot)
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  .compile({ checkpointer: memory });
 ```
 
+:::
+
 ## 3. Interact with your chatbot
 
 Now you can interact with your bot!
 
-1. Pick a thread to use as the key for this conversation.
+1.  Pick a thread to use as the key for this conversation.
+
+    :::python
 
     ```python
     config = {"configurable": {"thread_id": "1"}}
     ```
 
-2. Call your chatbot:
+    :::
+
+    :::js
+
+    ```typescript
+    const config = { configurable: { thread_id: "1" } };
+    ```
+
+    :::
+
+2.  Call your chatbot:
+
+    :::python
 
     ```python
     user_input = "Hi there! My name is Will."
@@ -74,14 +110,45 @@ Now you can interact with your bot!
     Hello Will! It's nice to meet you. How can I assist you today? Is there anything specific you'd like to know or discuss?
     ```
 
-    !!! note 
+    !!! note
 
         The config was provided as the **second positional argument** when calling our graph. It importantly is _not_ nested within the graph inputs (`{'messages': []}`).
 
+    :::
+
+    :::js
+
+    ```typescript
+    const userInput = "Hi there! My name is Will.";
+
+    const events = await graph.stream(
+      { messages: [{ type: "human", content: userInput }] },
+      { configurable: { thread_id: "1" }, streamMode: "values" }
+    );
+
+    for await (const event of events) {
+      const lastMessage = event.messages.at(-1);
+      console.log(`${lastMessage?.getType()}: ${lastMessage?.text}`);
+    }
+    ```
+
+    ```
+    human: Hi there! My name is Will.
+    ai: Hello Will! It's nice to meet you. How can I assist you today? Is there anything specific you'd like to know or discuss?
+    ```
+
+    !!! note
+
+        The config was provided as the **second parameter** when calling our graph. It importantly is _not_ nested within the graph inputs (`{"messages": []}`).
+
+    :::
+
 ## 4. Ask a follow up question
 
 Ask a follow up question:
 
+:::python
+
 ```python
 user_input = "Remember my name?"
 
@@ -104,10 +171,37 @@ Remember my name?
 Of course, I remember your name, Will. I always try to pay attention to important details that users share with me. Is there anything else you'd like to talk about or any questions you have? I'm here to help with a wide range of topics or tasks.
 ```
 
+:::
+
+:::js
+
+```typescript
+const userInput2 = "Remember my name?";
+
+const events2 = await graph.stream(
+  { messages: [{ type: "human", content: userInput2 }] },
+  { configurable: { thread_id: "1" }, streamMode: "values" }
+);
+
+for await (const event of events2) {
+  const lastMessage = event.messages.at(-1);
+  console.log(`${lastMessage?.getType()}: ${lastMessage?.text}`);
+}
+```
+
+```
+human: Remember my name?
+ai: Yes, your name is Will. How can I help you today?
+```
+
+:::
+
 **Notice** that we aren't using an external list for memory: it's all handled by the checkpointer! You can inspect the full execution in this [LangSmith trace](https://smith.langchain.com/public/29ba22b5-6d40-4fbe-8d27-b369e3329c84/r) to see what's going on.
 
 Don't believe me? Try this using a different config.
 
+:::python
+
 ```python
 # The only difference is we change the `thread_id` here to "2" instead of "1"
 events = graph.stream(
@@ -129,10 +223,36 @@ Remember my name?
 I apologize, but I don't have any previous context or memory of your name. As an AI assistant, I don't retain information from past conversations. Each interaction starts fresh. Could you please tell me your name so I can address you properly in this conversation?
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="3-4"
+const events3 = await graph.stream(
+  { messages: [{ type: "human", content: userInput2 }] },
+  // The only difference is we change the `thread_id` here to "2" instead of "1"
+  { configurable: { thread_id: "2" }, streamMode: "values" }
+);
+
+for await (const event of events3) {
+  const lastMessage = event.messages.at(-1);
+  console.log(`${lastMessage?.getType()}: ${lastMessage?.text}`);
+}
+```
+
+```
+human: Remember my name?
+ai: I don't have the ability to remember personal information about users between interactions. However, I'm here to help you with any questions or topics you want to discuss!
+```
+
+:::
+
 **Notice** that the **only** change we've made is to modify the `thread_id` in the config. See this call's [LangSmith trace](https://smith.langchain.com/public/51a62351-2f0a-4058-91cc-9996c5561428/r) for comparison.
 
 ## 5. Inspect the state
 
+:::python
+
 By now, we have made a few checkpoints across two different threads. But what goes into a checkpoint? To inspect a graph's `state` for a given config at any time, call `get_state(config)`.
 
 ```python
@@ -148,12 +268,94 @@ StateSnapshot(values={'messages': [HumanMessage(content='Hi there! My name is Wi
 snapshot.next  # (since the graph ended this turn, `next` is empty. If you fetch a state from within a graph invocation, next tells which node will execute next)
 ```
 
+:::
+
+:::js
+
+By now, we have made a few checkpoints across two different threads. But what goes into a checkpoint? To inspect a graph's `state` for a given config at any time, call `getState(config)`.
+
+```typescript
+await graph.getState({ configurable: { thread_id: "1" } });
+```
+
+```typescript
+{
+  values: {
+    messages: [
+      HumanMessage {
+        "id": "32fabcef-b3b8-481f-8bcb-fd83399a5f8d",
+        "content": "Hi there! My name is Will.",
+        "additional_kwargs": {},
+        "response_metadata": {}
+      },
+      AIMessage {
+        "id": "chatcmpl-BrPbTsCJbVqBvXWySlYoTJvM75Kv8",
+        "content": "Hello Will! How can I assist you today?",
+        "additional_kwargs": {},
+        "response_metadata": {},
+        "tool_calls": [],
+        "invalid_tool_calls": []
+      },
+      HumanMessage {
+        "id": "561c3aad-f8fc-4fac-94a6-54269a220856",
+        "content": "Remember my name?",
+        "additional_kwargs": {},
+        "response_metadata": {}
+      },
+      AIMessage {
+        "id": "chatcmpl-BrPbU4BhhsUikGbW37hYuF5vvnnE2",
+        "content": "Yes, I remember your name, Will! How can I help you today?",
+        "additional_kwargs": {},
+        "response_metadata": {},
+        "tool_calls": [],
+        "invalid_tool_calls": []
+      }
+    ]
+  },
+  next: [],
+  tasks: [],
+  metadata: {
+    source: 'loop',
+    step: 4,
+    parents: {},
+    thread_id: '1'
+  },
+  config: {
+    configurable: {
+      thread_id: '1',
+      checkpoint_id: '1f05cccc-9bb6-6270-8004-1d2108bcec77',
+      checkpoint_ns: ''
+    }
+  },
+  createdAt: '2025-07-09T13:58:27.607Z',
+  parentConfig: {
+    configurable: {
+      thread_id: '1',
+      checkpoint_ns: '',
+      checkpoint_id: '1f05cccc-78fa-68d0-8003-ffb01a76b599'
+    }
+  }
+}
+```
+
+```typescript
+import * as assert from "node:assert";
+
+// Since the graph ended this turn, `next` is empty.
+// If you fetch a state from within a graph invocation, next tells which node will execute next)
+assert.deepEqual(snapshot.next, []);
+```
+
+:::
+
 The snapshot above contains the current state values, corresponding config, and the `next` node to process. In our case, the graph has reached an `END` state, so `next` is empty.
 
 **Congratulations!** Your chatbot can now maintain conversation state across sessions thanks to LangGraph's checkpointing system. This opens up exciting possibilities for more natural, contextual interactions. LangGraph's checkpointing even handles **arbitrarily complex graph states**, which is much more expressive and powerful than simple chat memory.
-  
+
 Check out the code snippet below to review the graph from this tutorial:
 
+:::python
+
 {% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
@@ -172,7 +374,7 @@ from langchain_tavily import TavilySearch
 from langchain_core.messages import BaseMessage
 from typing_extensions import TypedDict
 
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph import StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode, tools_condition
@@ -200,10 +402,47 @@ graph_builder.add_conditional_edges(
 )
 graph_builder.add_edge("tools", "chatbot")
 graph_builder.set_entry_point("chatbot")
-memory = MemorySaver()
+memory = InMemorySaver()
 graph = graph_builder.compile(checkpointer=memory)
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="16 26"
+import { END, MessagesZodState, START } from "@langchain/langgraph";
+import { ChatOpenAI } from "@langchain/openai";
+import { TavilySearch } from "@langchain/tavily";
+
+import { MemorySaver } from "@langchain/langgraph";
+import { StateGraph } from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+import { z } from "zod";
+
+const State = z.object({
+  messages: MessagesZodState.shape.messages,
+});
+
+const tools = [new TavilySearch({ maxResults: 2 })];
+const llm = new ChatOpenAI({ model: "gpt-4o-mini" }).bindTools(tools);
+// highlight-next-line
+const memory = new MemorySaver();
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state) => ({
+    messages: [await llm.invoke(state.messages)],
+  }))
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  // highlight-next-line
+  .compile({ checkpointer: memory });
+```
+
+:::
+
 ## Next steps
 
 In the next tutorial, you will [add human-in-the-loop to the chatbot](./4-human-in-the-loop.md) to handle situations where it may need guidance or verification before proceeding.

docs/docs/tutorials/get-started/4-human-in-the-loop.md

@@ -2,7 +2,15 @@
 
 Agents can be unreliable and may need human input to successfully accomplish tasks. Similarly, for some actions, you may want to require human approval before running to ensure that everything is running as intended.
 
-LangGraph's [persistence](../../concepts/persistence.md) layer supports **human-in-the-loop** workflows, allowing execution to pause and resume based on user feedback. The primary interface to this functionality is the [`interrupt`](../../how-tos/human_in_the_loop/add-human-in-the-loop.md) function. Calling `interrupt` inside a node will pause execution. Execution can be resumed, together with new input from a human, by passing in a [Command](../../concepts/low_level.md#command). `interrupt` is ergonomically similar to Python's built-in `input()`, [with some caveats](../../how-tos/human_in_the_loop/add-human-in-the-loop.md).
+LangGraph's [persistence](../../concepts/persistence.md) layer supports **human-in-the-loop** workflows, allowing execution to pause and resume based on user feedback. The primary interface to this functionality is the [`interrupt`](../../how-tos/human_in_the_loop/add-human-in-the-loop.md) function. Calling `interrupt` inside a node will pause execution. Execution can be resumed, together with new input from a human, by passing in a [Command](../../concepts/low_level.md#command).
+
+:::python
+`interrupt` is ergonomically similar to Python's built-in `input()`, [with some caveats](../../how-tos/human_in_the_loop/add-human-in-the-loop.md).
+:::
+
+:::js
+`interrupt` is ergonomically similar to Node.js's built-in `readline.question()` function, [with some caveats](../../how-tos/human_in_the_loop/add-human-in-the-loop.md).
+:::
 
 !!! note
 
@@ -14,6 +22,7 @@ Starting with the existing code from the [Add memory to the chatbot](./3-add-mem
 
 Let's first select a chat model:
 
+:::python
 {% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
@@ -24,16 +33,31 @@ llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
 ```
 -->
 
+:::
+
+:::js
+
+```typescript
+// Add your API key here
+process.env.ANTHROPIC_API_KEY = "YOUR_API_KEY";
+```
+
+:::
+
 We can now incorporate it into our `StateGraph` with an additional tool:
 
-``` python hl_lines="12 19 20 21 22 23"
+:::python
+
+````python hl_lines="12 19 20 21 22 23"
+
+```python hl_lines="12 19 20 21 22 23"
 from typing import Annotated
 
 from langchain_tavily import TavilySearch
 from langchain_core.tools import tool
 from typing_extensions import TypedDict
 
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph import StateGraph, START, END
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode, tools_condition
@@ -74,7 +98,103 @@ graph_builder.add_conditional_edges(
 )
 graph_builder.add_edge("tools", "chatbot")
 graph_builder.add_edge(START, "chatbot")
-```
+````
+
+:::
+
+:::js
+
+````typescript hl_lines="12 19 20 21 22 23"
+import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
+
+```typescript hl_lines="1 7-19"
+import { interrupt, MessagesZodState } from "@langchain/langgraph";
+import { ChatAnthropic } from "@langchain/anthropic";
+import { TavilySearch } from "@langchain/tavily";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+
+import { MemorySaver } from "@langchain/langgraph";
+import {
+  StateGraph,
+  START,
+  END,
+  MessagesAnnotation,
+} from "@langchain/langgraph";
+import { ToolNode } from "@langchain/langgraph/prebuilt";
+import { ChatAnthropic } from "@langchain/anthropic";
+
+import { Command, interrupt } from "@langchain/langgraph";
+
+const humanAssistance = tool(
+  async ({ query }) => {
+    const humanResponse = interrupt({ query });
+    return humanResponse.data;
+  },
+  {
+    name: "humanAssistance",
+    description: "Request assistance from a human.",
+    schema: z.object({
+      query: z.string().describe("Human readable question for the human"),
+    }),
+  }
+);
+const humanAssistance = tool(
+  async ({ query }) => {
+    const humanResponse = interrupt({ query });
+    return humanResponse.data;
+  },
+  {
+    name: "humanAssistance",
+    description: "Request assistance from a human.",
+    schema: z.object({
+      query: z.string().describe("Human readable question for the human"),
+    }),
+  }
+);
+
+const searchTool = new TavilySearch({ maxResults: 2 });
+const tools = [searchTool, humanAssistance];
+
+const llmWithTools = new ChatAnthropic({
+  model: "claude-3-5-sonnet-latest",
+}).bindTools(tools);
+
+async function chatbot(state: z.infer<typeof MessagesZodState>) {
+  const message = await llmWithTools.invoke(state.messages);
+
+  // Because we will be interrupting during tool execution,
+  // we disable parallel tool calling to avoid repeating any
+  // tool invocations when we resume.
+  if (message.tool_calls && message.tool_calls.length > 1) {
+    throw new Error("Multiple tool calls not supported with interrupts");
+  }
+  return { messages: [message] };
+}
+
+const graphBuilder = new StateGraph(MessagesAnnotation).addNode(
+  "chatbot",
+  chatbot
+);
+
+const toolNode = new ToolNode(tools);
+graphBuilder.addNode("tools", toolNode);
+
+const shouldContinue = (state: typeof MessagesAnnotation.State) => {
+  const messages = state.messages;
+  const lastMessage = messages[messages.length - 1];
+  if ("tool_calls" in lastMessage && lastMessage.tool_calls?.length) {
+    return "tools";
+  }
+  return END;
+};
+
+graphBuilder.addConditionalEdges("chatbot", shouldContinue);
+graphBuilder.addEdge("tools", "chatbot");
+graphBuilder.addEdge(START, "chatbot");
+````
+
+:::
 
 !!! tip
 
@@ -84,17 +204,39 @@ graph_builder.add_edge(START, "chatbot")
 
 We compile the graph with a checkpointer, as before:
 
+:::python
+
 ```python
-memory = MemorySaver()
+memory = InMemorySaver()
 
 graph = graph_builder.compile(checkpointer=memory)
 ```
 
+:::
+
+:::js
+
+```typescript
+const memory = new MemorySaver();
+
+const graph = new StateGraph(MessagesZodState)
+  .addNode("chatbot", chatbot)
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  .compile({ checkpointer: memory });
+```
+
+:::
+
 ## 3. Visualize the graph (optional)
 
 Visualizing the graph, you get the same layout as before – just with the added tool!
 
-``` python
+:::python
+
+```python
 from IPython.display import Image, display
 
 try:
@@ -104,12 +246,30 @@ except Exception:
     pass
 ```
 
+:::
+
+:::js
+
+```typescript
+import * as fs from "node:fs/promises";
+
+const drawableGraph = await graph.getGraphAsync();
+const image = await drawableGraph.drawMermaidPng();
+const imageBuffer = new Uint8Array(await image.arrayBuffer());
+
+await fs.writeFile("chatbot-with-tools.png", imageBuffer);
+```
+
+:::
+
 ![chatbot-with-tools-diagram](chatbot-with-tools.png)
 
 ## 4. Prompt the chatbot
 
 Now, prompt the chatbot with a question that will engage the new `human_assistance` tool:
 
+:::python
+
 ```python
 user_input = "I need some expert guidance for building an AI agent. Could you request assistance for me?"
 config = {"configurable": {"thread_id": "1"}}
@@ -138,8 +298,60 @@ Tool Calls:
     query: A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?
 ```
 
+:::
+
+:::js
+
+```typescript
+const userInput =
+  "I need some expert guidance for building an AI agent. Could you request assistance for me?";
+const config = {
+  configurable: { thread_id: "1" },
+  streamMode: "values" as const,
+};
+
+const events = await graph.stream(
+  { messages: [{ role: "user", content: userInput }] },
+  { configurable: { thread_id: "1" }, streamMode: "values" }
+);
+
+for await (const event of events) {
+  if ("messages" in event) {
+    const lastMessage = event.messages.at(-1);
+    console.log(`[${lastMessage?.getType()}]: ${lastMessage?.text}`);
+
+    if (
+      lastMessage &&
+      isAIMessage(lastMessage) &&
+      lastMessage.tool_calls?.length
+    ) {
+      console.log("Tool calls:", lastMessage.tool_calls);
+    }
+  }
+}
+```
+
+```
+[human]: I need some expert guidance for building an AI agent. Could you request assistance for me?
+[ai]: I'll help you request human assistance for guidance on building an AI agent.
+Tool calls: [
+  {
+    name: 'humanAssistance',
+    args: {
+      query: 'I would like expert guidance on building an AI agent. Could you please provide assistance with this topic?'
+    },
+    id: 'toolu_01Bpxc8rFVMhSaRosS6b85Ts',
+    type: 'tool_call'
+  }
+]
+```
+
+:::
+
 The chatbot generated a tool call, but then execution has been interrupted. If you inspect the graph state, you see that it stopped at the tools node:
 
+:::python
+
 ```python
 snapshot = graph.get_state(config)
 snapshot.next
@@ -149,8 +361,28 @@ snapshot.next
 ('tools',)
 ```
 
+:::
+
+:::js
+
+```typescript
+const snapshot = await graph.getState({ configurable: { thread_id: "1" } });
+snapshot.next;
+```
+
+```json
+["tools"]
+```
+
+['tools']
+
+````
+:::
+
 !!! info Additional information
 
+    :::python
+
     Take a closer look at the `human_assistance` tool:
 
     ```python
@@ -162,12 +394,40 @@ snapshot.next
     ```
 
     Similar to Python's built-in `input()` function, calling `interrupt` inside the tool will pause execution. Progress is persisted based on the [checkpointer](../../concepts/persistence.md#checkpointer-libraries); so if it is persisting with Postgres, it can resume at any time as long as the database is alive. In this example, it is persisting with the in-memory checkpointer and can resume any time if the Python kernel is running.
+    :::
+
+    :::js
+
+    Take a closer look at the `humanAssistance` tool:
+
+    ```typescript hl_lines="3"
+    const humanAssistance = tool(
+      async ({ query }) => {
+        const humanResponse = interrupt({ query });
+        return humanResponse.data;
+      },
+      {
+        name: "humanAssistance",
+        description: "Request assistance from a human.",
+        schema: z.object({
+          query: z.string().describe("Human readable question for the human"),
+        }),
+      },
+    );
+    ```
+
+    Calling `interrupt` inside the tool will pause execution. Progress is persisted based on the [checkpointer](../../concepts/persistence.md#checkpointer-libraries); so if it is persisting with Postgres, it can resume at any time as long as the database is alive. In this example, it is persisting with the in-memory checkpointer and can resume any time if the JavaScript runtime is running.
+    :::
 
 ## 5. Resume execution
 
-To resume execution, pass a [`Command`](../../concepts/low_level.md#command) object containing data expected by the tool. The format of this data can be customized based on needs. For this example, use a dict with a key `"data"`:
+To resume execution, pass a [`Command`](../../concepts/low_level.md#command) object containing data expected by the tool. The format of this data can be customized based on needs.
 
-``` python
+:::python
+
+For this example, use a dict with a key `"data"`:
+
+```python
 human_response = (
     "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent."
     " It's much more reliable and extensible than simple autonomous agents."
@@ -179,7 +439,7 @@ events = graph.stream(human_command, config, stream_mode="values")
 for event in events:
     if "messages" in event:
         event["messages"][-1].pretty_print()
-```
+````
 
 ```
 ================================== Ai Message ==================================
@@ -215,13 +475,58 @@ If you'd like more specific information about LangGraph or have any questions ab
 Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
 ```
 
+:::
+
+:::js
+For this example, use an object with a key `"data"`:
+
+```typescript
+const humanResponse = (
+  "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent." +
+  " It's much more reliable and extensible than simple autonomous agents.";
+
+const humanCommand = new Command({ resume: { data: humanResponse } });
+
+const resumeEvents = await graph.stream(humanCommand, {
+  configurable: { thread_id: "1" },
+  streamMode: "values",
+});
+
+for await (const event of resumeEvents) {
+  if ("messages" in event) {
+    const lastMessage = event.messages.at(-1);
+    console.log(`[${lastMessage?.getType()}]: ${lastMessage?.text}`);
+  }
+}
+```
+
+```
+[tool]: We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.
+[ai]: Thank you for your patience. I've received some expert advice regarding your request for guidance on building an AI agent. Here's what the experts have suggested:
+
+The experts recommend that you look into LangGraph for building your AI agent. They mention that LangGraph is a more reliable and extensible option compared to simple autonomous agents.
+
+LangGraph is likely a framework or library designed specifically for creating AI agents with advanced capabilities. Here are a few points to consider based on this recommendation:
+
+1. Reliability: The experts emphasize that LangGraph is more reliable than simpler autonomous agent approaches. This could mean it has better stability, error handling, or consistent performance.
+
+2. Extensibility: LangGraph is described as more extensible, which suggests that it probably offers a flexible architecture that allows you to easily add new features or modify existing ones as your agent's requirements evolve.
+
+3. Advanced capabilities: Given that it's recommended over "simple autonomous agents," LangGraph likely provides more sophisticated tools and techniques for building complex AI agents.
+
+...
+```
+
+:::
+
 The input has been received and processed as a tool message. Review this call's [LangSmith trace](https://smith.langchain.com/public/9f0f87e3-56a7-4dde-9c76-b71675624e91/r) to see the exact work that was done in the above call. Notice that the state is loaded in the first step so that our chatbot can continue where it left off.
 
 **Congratulations!** You've used an `interrupt` to add human-in-the-loop execution to your chatbot, allowing for human oversight and intervention when needed. This opens up the potential UIs you can create with your AI systems. Since you have already added a **checkpointer**, as long as the underlying persistence layer is running, the graph can be paused **indefinitely** and resumed at any time as if nothing had happened.
 
 Check out the code snippet below to review the graph from this tutorial:
 
-{% include-markdown "../../../snippets/chat_model_tabs.md" %}
+:::python
+{!snippets/chat_model_tabs.md!}
 
 ```python
 from typing import Annotated
@@ -230,7 +535,7 @@ from langchain_tavily import TavilySearch
 from langchain_core.tools import tool
 from typing_extensions import TypedDict
 
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph import StateGraph, START, END
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode, tools_condition
@@ -268,10 +573,98 @@ graph_builder.add_conditional_edges(
 graph_builder.add_edge("tools", "chatbot")
 graph_builder.add_edge(START, "chatbot")
 
-memory = MemorySaver()
+memory = InMemorySaver()
 graph = graph_builder.compile(checkpointer=memory)
 ```
 
+:::
+
+:::js
+
+```typescript
+import {
+  interrupt,
+  MessagesZodState,
+  StateGraph,
+  MemorySaver,
+  START,
+  END,
+} from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+import { isAIMessage } from "@langchain/core/messages";
+import { ChatAnthropic } from "@langchain/anthropic";
+import { TavilySearch } from "@langchain/tavily";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+
+const humanAssistance = tool(
+  async ({ query }) => {
+    const humanResponse = interrupt({ query });
+    return humanResponse.data;
+  },
+  {
+    name: "humanAssistance",
+    description: "Request assistance from a human.",
+    schema: z.object({
+      query: z.string().describe("Human readable question for the human"),
+    }),
+  }
+);
+
+const searchTool = new TavilySearch({ maxResults: 2 });
+const tools = [searchTool, humanAssistance];
+
+const llmWithTools = new ChatAnthropic({
+  model: "claude-3-5-sonnet-latest",
+}).bindTools(tools);
+
+const chatbot = async (state: z.infer<typeof MessagesZodState>) => {
+  const message = await llmWithTools.invoke(state.messages);
+
+  // Because we will be interrupting during tool execution,
+  // we disable parallel tool calling to avoid repeating any
+  // tool invocations when we resume.
+  if (message.tool_calls && message.tool_calls.length > 1) {
+    throw new Error("Multiple tool calls not supported with interrupts");
+  }
+
+  return { messages: message };
+};
+
+const graphBuilder = new StateGraph(MessagesAnnotation).addNode(
+  "chatbot",
+  chatbot
+);
+
+const toolNode = new ToolNode(tools);
+graphBuilder.addNode("tools", toolNode);
+
+const shouldContinue = (state: typeof MessagesAnnotation.State) => {
+  const messages = state.messages;
+  const lastMessage = messages[messages.length - 1];
+  if ("tool_calls" in lastMessage && lastMessage.tool_calls?.length) {
+    return "tools";
+  }
+  return END;
+};
+
+graphBuilder.addConditionalEdges("chatbot", shouldContinue);
+graphBuilder.addEdge("tools", "chatbot");
+graphBuilder.addEdge(START, "chatbot");
+
+const memory = new MemorySaver();
+
+const graph = new StateGraph(MessagesZodState)
+  .addNode("chatbot", chatbot)
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  .compile({ checkpointer: memory });
+```
+
+:::
+
 ## Next steps
 
-So far, the tutorial examples have relied on a simple state with one entry: a list of messages. You can go far with this simple state, but if you want to define complex behavior without relying on the message list, you can [add additional fields to the state](./5-customize-state.md).
+So far, the tutorial examples have relied on a simple state with one entry: a list of messages. You can go far with this simple state, but if you want to define complex behavior without relying on the message list, you can [add additional fields to the state](./5-customize-state.md).

docs/docs/tutorials/get-started/5-customize-state.md

@@ -10,6 +10,8 @@ In this tutorial, you will add additional fields to the state to define complex
 
 Update the chatbot to research the birthday of an entity by adding `name` and `birthday` keys to the state:
 
+:::python
+
 ```python
 from typing import Annotated
 
@@ -26,13 +28,34 @@ class State(TypedDict):
     birthday: str
 ```
 
+:::
+
+:::js
+
+```typescript
+import { MessagesZodState } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({
+  messages: MessagesZodState.shape.messages,
+  // highlight-next-line
+  name: z.string(),
+  // highlight-next-line
+  birthday: z.string(),
+});
+```
+
+:::
+
 Adding this information to the state makes it easily accessible by other graph nodes (like a downstream node that stores or processes the information), as well as the graph's persistence layer.
 
 ## 2. Update the state inside the tool
 
+:::python
+
 Now, populate the state keys inside of the `human_assistance` tool. This allows a human to review the information before it is stored in the state. Use [`Command`](../../concepts/low_level.md#using-inside-tools) to issue a state update from inside the tool.
 
-``` python
+```python
 from langchain_core.messages import ToolMessage
 from langchain_core.tools import InjectedToolCallId, tool
 
@@ -76,10 +99,78 @@ def human_assistance(
     return Command(update=state_update)
 ```
 
+:::
+
+:::js
+
+Now, populate the state keys inside of the `humanAssistance` tool. This allows a human to review the information before it is stored in the state. Use [`Command`](../../concepts/low_level.md#using-inside-tools) to issue a state update from inside the tool.
+
+```typescript
+import { tool } from "@langchain/core/tools";
+import { ToolMessage } from "@langchain/core/messages";
+import { Command, interrupt } from "@langchain/langgraph";
+
+const humanAssistance = tool(
+  async (input, config) => {
+    // Note that because we are generating a ToolMessage for a state update,
+    // we generally require the ID of the corresponding tool call.
+    // This is available in the tool's config.
+    const toolCallId = config?.toolCall?.id as string | undefined;
+    if (!toolCallId) throw new Error("Tool call ID is required");
+
+    const humanResponse = await interrupt({
+      question: "Is this correct?",
+      name: input.name,
+      birthday: input.birthday,
+    });
+
+    // We explicitly update the state with a ToolMessage inside the tool.
+    const stateUpdate = (() => {
+      // If the information is correct, update the state as-is.
+      if (humanResponse.correct?.toLowerCase().startsWith("y")) {
+        return {
+          name: input.name,
+          birthday: input.birthday,
+          messages: [
+            new ToolMessage({ content: "Correct", tool_call_id: toolCallId }),
+          ],
+        };
+      }
+
+      // Otherwise, receive information from the human reviewer.
+      return {
+        name: humanResponse.name || input.name,
+        birthday: humanResponse.birthday || input.birthday,
+        messages: [
+          new ToolMessage({
+            content: `Made a correction: ${JSON.stringify(humanResponse)}`,
+            tool_call_id: toolCallId,
+          }),
+        ],
+      };
+    })();
+
+    // We return a Command object in the tool to update our state.
+    return new Command({ update: stateUpdate });
+  },
+  {
+    name: "humanAssistance",
+    description: "Request assistance from a human.",
+    schema: z.object({
+      name: z.string().describe("The name of the entity"),
+      birthday: z.string().describe("The birthday/release date of the entity"),
+    }),
+  }
+);
+```
+
+:::
+
 The rest of the graph stays the same.
 
 ## 3. Prompt the chatbot
 
+:::python
 Prompt the chatbot to look up the "birthday" of the LangGraph library and direct the chatbot to reach out to the `human_assistance` tool once it has the required information. By setting `name` and `birthday` in the arguments for the tool, you force the chatbot to generate proposals for these fields.
 
 ```python
@@ -99,6 +190,51 @@ for event in events:
         event["messages"][-1].pretty_print()
 ```
 
+:::
+
+:::js
+Prompt the chatbot to look up the "birthday" of the LangGraph library and direct the chatbot to reach out to the `humanAssistance` tool once it has the required information. By setting `name` and `birthday` in the arguments for the tool, you force the chatbot to generate proposals for these fields.
+
+```typescript
+import { isAIMessage } from "@langchain/core/messages";
+
+const userInput =
+  "Can you look up when LangGraph was released? " +
+  "When you have the answer, use the humanAssistance tool for review.";
+
+const events = await graph.stream(
+  { messages: [{ role: "user", content: userInput }] },
+  { configurable: { thread_id: "1" }, streamMode: "values" }
+);
+
+for await (const event of events) {
+  if ("messages" in event) {
+    const lastMessage = event.messages.at(-1);
+
+    console.log(
+      "=".repeat(32),
+      `${lastMessage?.getType()} Message`,
+      "=".repeat(32)
+    );
+    console.log(lastMessage?.text);
+
+    if (
+      lastMessage &&
+      isAIMessage(lastMessage) &&
+      lastMessage.tool_calls?.length
+    ) {
+      console.log("Tool Calls:");
+      for (const call of lastMessage.tool_calls) {
+        console.log(`  ${call.name} (${call.id})`);
+        console.log(`  Args: ${JSON.stringify(call.args)}`);
+      }
+    }
+  }
+}
+```
+
+:::
+
 ```
 ================================ Human Message =================================
 
@@ -126,12 +262,20 @@ Tool Calls:
     birthday: 2023-01-01
 ```
 
+:::python
 We've hit the `interrupt` in the `human_assistance` tool again.
+:::
+
+:::js
+We've hit the `interrupt` in the `humanAssistance` tool again.
+:::
 
 ## 4. Add human assistance
 
 The chatbot failed to identify the correct date, so supply it with information:
 
+:::python
+
 ```python
 human_command = Command(
     resume={
@@ -146,6 +290,53 @@ for event in events:
         event["messages"][-1].pretty_print()
 ```
 
+:::
+
+:::js
+
+```typescript
+import { Command } from "@langchain/langgraph";
+
+const humanCommand = new Command({
+  resume: {
+    name: "LangGraph",
+    birthday: "Jan 17, 2024",
+  },
+});
+
+const resumeEvents = await graph.stream(humanCommand, {
+  configurable: { thread_id: "1" },
+  streamMode: "values",
+});
+
+for await (const event of resumeEvents) {
+  if ("messages" in event) {
+    const lastMessage = event.messages.at(-1);
+
+    console.log(
+      "=".repeat(32),
+      `${lastMessage?.getType()} Message`,
+      "=".repeat(32)
+    );
+    console.log(lastMessage?.text);
+
+    if (
+      lastMessage &&
+      isAIMessage(lastMessage) &&
+      lastMessage.tool_calls?.length
+    ) {
+      console.log("Tool Calls:");
+      for (const call of lastMessage.tool_calls) {
+        console.log(`  ${call.name} (${call.id})`);
+        console.log(`  Args: ${JSON.stringify(call.args)}`);
+      }
+    }
+  }
+}
+```
+
+:::
+
 ```
 ================================== Ai Message ==================================
 
@@ -175,6 +366,8 @@ It's worth noting that LangGraph had been in development and use for some time b
 
 Note that these fields are now reflected in the state:
 
+:::python
+
 ```python
 snapshot = graph.get_state(config)
 
@@ -185,13 +378,34 @@ snapshot = graph.get_state(config)
 {'name': 'LangGraph', 'birthday': 'Jan 17, 2024'}
 ```
 
+:::
+
+:::js
+
+```typescript
+const snapshot = await graph.getState(config);
+
+const relevantState = Object.fromEntries(
+  Object.entries(snapshot.values).filter(([k]) =>
+    ["name", "birthday"].includes(k)
+  )
+);
+```
+
+```
+{ name: 'LangGraph', birthday: 'Jan 17, 2024' }
+```
+
+:::
+
 This makes them easily accessible to downstream nodes (e.g., a node that further processes or stores the information).
 
 ## 5. Manually update the state
 
+:::python
 LangGraph gives a high degree of control over the application state. For instance, at any point (including when interrupted), you can manually override a key using `graph.update_state`:
 
-``` python
+```python
 graph.update_state(config, {"name": "LangGraph (library)"})
 ```
 
@@ -201,11 +415,36 @@ graph.update_state(config, {"name": "LangGraph (library)"})
   'checkpoint_id': '1efd4ec5-cf69-6352-8006-9278f1730162'}}
 ```
 
+:::
+
+:::js
+LangGraph gives a high degree of control over the application state. For instance, at any point (including when interrupted), you can manually override a key using `graph.updateState`:
+
+```typescript
+await graph.updateState(
+  { configurable: { thread_id: "1" } },
+  { name: "LangGraph (library)" }
+);
+```
+
+```typescript
+{
+  configurable: {
+    thread_id: '1',
+    checkpoint_ns: '',
+    checkpoint_id: '1efd4ec5-cf69-6352-8006-9278f1730162'
+  }
+}
+```
+
+:::
+
 ## 6. View the new value
 
+:::python
 If you call `graph.get_state`, you can see the new value is reflected:
 
-``` python
+```python
 snapshot = graph.get_state(config)
 
 {k: v for k, v in snapshot.values.items() if k in ("name", "birthday")}
@@ -215,12 +454,35 @@ snapshot = graph.get_state(config)
 {'name': 'LangGraph (library)', 'birthday': 'Jan 17, 2024'}
 ```
 
+:::
+
+:::js
+If you call `graph.getState`, you can see the new value is reflected:
+
+```typescript
+const updatedSnapshot = await graph.getState(config);
+
+const updatedRelevantState = Object.fromEntries(
+  Object.entries(updatedSnapshot.values).filter(([k]) =>
+    ["name", "birthday"].includes(k)
+  )
+);
+```
+
+```typescript
+{ name: 'LangGraph (library)', birthday: 'Jan 17, 2024' }
+```
+
+:::
+
 Manual state updates will [generate a trace](https://smith.langchain.com/public/7ebb7827-378d-49fe-9f6c-5df0e90086c8/r) in LangSmith. If desired, they can also be used to [control human-in-the-loop workflows](../../how-tos/human_in_the_loop/add-human-in-the-loop.md). Use of the `interrupt` function is generally recommended instead, as it allows data to be transmitted in a human-in-the-loop interaction independently of state updates.
 
 **Congratulations!** You've added custom keys to the state to facilitate a more complex workflow, and learned how to generate state updates from inside tools.
 
 Check out the code snippet below to review the graph from this tutorial:
 
+:::python
+
 {% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
@@ -239,7 +501,7 @@ from langchain_core.messages import ToolMessage
 from langchain_core.tools import InjectedToolCallId, tool
 from typing_extensions import TypedDict
 
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph import StateGraph, START, END
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode, tools_condition
@@ -301,11 +563,115 @@ graph_builder.add_conditional_edges(
 graph_builder.add_edge("tools", "chatbot")
 graph_builder.add_edge(START, "chatbot")
 
-memory = MemorySaver()
+memory = InMemorySaver()
 graph = graph_builder.compile(checkpointer=memory)
 ```
 
+:::
+
+:::js
+
+```typescript
+import {
+  Command,
+  interrupt,
+  MessagesZodState,
+  MemorySaver,
+  StateGraph,
+  END,
+  START,
+} from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+import { ChatAnthropic } from "@langchain/anthropic";
+import { TavilySearch } from "@langchain/tavily";
+import { ToolMessage } from "@langchain/core/messages";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+
+const State = z.object({
+  messages: MessagesZodState.shape.messages,
+  name: z.string(),
+  birthday: z.string(),
+});
+
+const humanAssistance = tool(
+  async (input, config) => {
+    // Note that because we are generating a ToolMessage for a state update, we
+    // generally require the ID of the corresponding tool call. This is available
+    // in the tool's config.
+    const toolCallId = config?.toolCall?.id as string | undefined;
+    if (!toolCallId) throw new Error("Tool call ID is required");
+
+    const humanResponse = await interrupt({
+      question: "Is this correct?",
+      name: input.name,
+      birthday: input.birthday,
+    });
+
+    // We explicitly update the state with a ToolMessage inside the tool.
+    const stateUpdate = (() => {
+      // If the information is correct, update the state as-is.
+      if (humanResponse.correct?.toLowerCase().startsWith("y")) {
+        return {
+          name: input.name,
+          birthday: input.birthday,
+          messages: [
+            new ToolMessage({ content: "Correct", tool_call_id: toolCallId }),
+          ],
+        };
+      }
+
+      // Otherwise, receive information from the human reviewer.
+      return {
+        name: humanResponse.name || input.name,
+        birthday: humanResponse.birthday || input.birthday,
+        messages: [
+          new ToolMessage({
+            content: `Made a correction: ${JSON.stringify(humanResponse)}`,
+            tool_call_id: toolCallId,
+          }),
+        ],
+      };
+    })();
+
+    // We return a Command object in the tool to update our state.
+    return new Command({ update: stateUpdate });
+  },
+  {
+    name: "humanAssistance",
+    description: "Request assistance from a human.",
+    schema: z.object({
+      name: z.string().describe("The name of the entity"),
+      birthday: z.string().describe("The birthday/release date of the entity"),
+    }),
+  }
+);
+
+const searchTool = new TavilySearch({ maxResults: 2 });
+
+const tools = [searchTool, humanAssistance];
+const llmWithTools = new ChatAnthropic({
+  model: "claude-3-5-sonnet-latest",
+}).bindTools(tools);
+
+const memory = new MemorySaver();
+
+const chatbot = async (state: z.infer<typeof State>) => {
+  const message = await llmWithTools.invoke(state.messages);
+  return { messages: message };
+};
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", chatbot)
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  .compile({ checkpointer: memory });
+```
+
+:::
+
 ## Next steps
 
-There's one more concept to review before finishing the LangGraph basics tutorials: connecting `checkpointing` and `state updates` to [time travel](./6-time-travel.md). 
-
+There's one more concept to review before finishing the LangGraph basics tutorials: connecting `checkpointing` and `state updates` to [time travel](./6-time-travel.md).

docs/docs/tutorials/get-started/6-time-travel.md

@@ -4,7 +4,7 @@ In a typical chatbot workflow, the user interacts with the bot one or more times
 
 What if you want a user to be able to start from a previous response and explore a different outcome? Or what if you want users to be able to rewind your chatbot's work to fix mistakes or try a different strategy, something that is common in applications like autonomous software engineers?
 
-You can create these types of experiences using LangGraph's built-in **time travel** functionality. 
+You can create these types of experiences using LangGraph's built-in **time travel** functionality.
 
 !!! note
 
@@ -12,7 +12,15 @@ You can create these types of experiences using LangGraph's built-in **time trav
 
 ## 1. Rewind your graph
 
+:::python
 Rewind your graph by fetching a checkpoint using the graph's `get_state_history` method. You can then resume execution at this previous point in time.
+:::
+
+:::js
+Rewind your graph by fetching a checkpoint using the graph's `getStateHistory` method. You can then resume execution at this previous point in time.
+:::
+
+:::python
 
 {% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
@@ -31,7 +39,7 @@ from langchain_tavily import TavilySearch
 from langchain_core.messages import BaseMessage
 from typing_extensions import TypedDict
 
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph import StateGraph, START, END
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode, tools_condition
@@ -60,15 +68,53 @@ graph_builder.add_conditional_edges(
 graph_builder.add_edge("tools", "chatbot")
 graph_builder.add_edge(START, "chatbot")
 
-memory = MemorySaver()
+memory = InMemorySaver()
 graph = graph_builder.compile(checkpointer=memory)
 ```
 
+:::
+
+:::js
+
+```typescript
+import {
+  StateGraph,
+  START,
+  END,
+  MessagesZodState,
+  MemorySaver,
+} from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+import { TavilySearch } from "@langchain/tavily";
+import { ChatOpenAI } from "@langchain/openai";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const tools = [new TavilySearch({ maxResults: 2 })];
+const llmWithTools = new ChatOpenAI({ model: "gpt-4o-mini" }).bindTools(tools);
+const memory = new MemorySaver();
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state) => ({
+    messages: [await llmWithTools.invoke(state.messages)],
+  }))
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  .compile({ checkpointer: memory });
+```
+
+:::
+
 ## 2. Add steps
 
 Add steps to your graph. Every step will be checkpointed in its state history:
 
-``` python
+:::python
+
+```python
 config = {"configurable": {"thread_id": "1"}}
 events = graph.stream(
     {
@@ -159,7 +205,7 @@ Tool Calls:
 ================================= Tool Message =================================
 Name: tavily_search_results_json
 
-[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user’s question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
+[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user's question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
 ================================== Ai Message ==================================
 
 Great idea! Building an autonomous agent with LangGraph is definitely an exciting project. Based on the latest information I've found, here are some insights and tips for building autonomous agents with LangGraph:
@@ -177,11 +223,140 @@ Building an autonomous agent is an iterative process, so be prepared to refine a
 Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
 ```
 
+:::
+
+:::js
+
+```typescript
+import { randomUUID } from "node:crypto";
+const threadId = randomUUID();
+
+let iter = 0;
+
+for (const userInput of [
+  "I'm learning LangGraph. Could you do some research on it for me?",
+  "Ya that's helpful. Maybe I'll build an autonomous agent with it!",
+]) {
+  iter += 1;
+
+  console.log(`\n--- Conversation Turn ${iter} ---\n`);
+  const events = await graph.stream(
+    { messages: [{ role: "user", content: userInput }] },
+    { configurable: { thread_id: threadId }, streamMode: "values" }
+  );
+
+  for await (const event of events) {
+    if ("messages" in event) {
+      const lastMessage = event.messages.at(-1);
+
+      console.log(
+        "=".repeat(32),
+        `${lastMessage?.getType()} Message`,
+        "=".repeat(32)
+      );
+      console.log(lastMessage?.text);
+    }
+  }
+}
+```
+
+```
+--- Conversation Turn 1 ---
+
+================================ human Message ================================
+I'm learning LangGraph.js. Could you do some research on it for me?
+================================ ai Message ================================
+I'll search for information about LangGraph.js for you.
+================================ tool Message ================================
+{
+  "query": "LangGraph.js framework TypeScript langchain what is it tutorial guide",
+  "follow_up_questions": null,
+  "answer": null,
+  "images": [],
+  "results": [
+    {
+      "url": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/an-absolute-beginners-guide-to-langgraph-js/4212496",
+      "title": "An Absolute Beginner's Guide to LangGraph.js",
+      "content": "(...)",
+      "score": 0.79369855,
+      "raw_content": null
+    },
+    {
+      "url": "https://langchain-ai.github.io/langgraphjs/",
+      "title": "LangGraph.js",
+      "content": "(...)",
+      "score": 0.78154784,
+      "raw_content": null
+    }
+  ],
+  "response_time": 2.37
+}
+================================ ai Message ================================
+Let me provide you with an overview of LangGraph.js based on the search results:
+
+LangGraph.js is a JavaScript/TypeScript library that's part of the LangChain ecosystem, specifically designed for creating and managing complex LLM (Large Language Model) based workflows. Here are the key points about LangGraph.js:
+
+1. Purpose:
+- It's a low-level orchestration framework for building controllable agents
+- Particularly useful for creating agentic workflows where LLMs decide the course of action based on current state
+- Helps model workflows as graphs with nodes and edges
+
+(...)
+
+--- Conversation Turn 2 ---
+
+================================ human Message ================================
+Ya that's helpful. Maybe I'll build an autonomous agent with it!
+================================ ai Message ================================
+Let me search for specific information about building autonomous agents with LangGraph.js.
+================================ tool Message ================================
+{
+  "query": "how to build autonomous agents with LangGraph.js examples tutorial react agent",
+  "follow_up_questions": null,
+  "answer": null,
+  "images": [],
+  "results": [
+    {
+      "url": "https://ai.google.dev/gemini-api/docs/langgraph-example",
+      "title": "ReAct agent from scratch with Gemini 2.5 and LangGraph",
+      "content": "(...)",
+      "score": 0.7602419,
+      "raw_content": null
+    },
+    {
+      "url": "https://www.youtube.com/watch?v=ZfjaIshGkmk",
+      "title": "Build Autonomous AI Agents with ReAct and LangGraph Tools",
+      "content": "(...)",
+      "score": 0.7471924,
+      "raw_content": null
+    }
+  ],
+  "response_time": 1.98
+}
+================================ ai Message ================================
+Based on the search results, I can provide you with a practical overview of how to build an autonomous agent with LangGraph.js. Here's what you need to know:
+
+1. Basic Structure for Building an Agent:
+- LangGraph.js provides a ReAct (Reason + Act) pattern implementation
+- The basic components include:
+  - State management for conversation history
+  - Nodes for different actions
+  - Edges for decision-making flow
+  - Tools for specific functionalities
+
+(...)
+
+```
+
+:::
+
 ## 3. Replay the full state history
 
 Now that you have added steps to the chatbot, you can `replay` the full state history to see everything that occurred.
 
-``` python
+:::python
+
+```python
 to_replay = None
 for state in graph.get_state_history(config):
     print("Num Messages: ", len(state.values["messages"]), "Next: ", state.next)
@@ -214,10 +389,61 @@ Num Messages:  0 Next:  ('__start__',)
 --------------------------------------------------------------------------------
 ```
 
-Checkpoints are saved for every step of the graph. This __spans invocations__ so you can rewind across a full thread's history.
+:::
+
+:::js
+
+```typescript
+import type { StateSnapshot } from "@langchain/langgraph";
+
+let toReplay: StateSnapshot | undefined;
+for await (const state of graph.getStateHistory({
+  configurable: { thread_id: threadId },
+})) {
+  console.log(
+    `Num Messages: ${state.values.messages.length}, Next: ${JSON.stringify(
+      state.next
+    )}`
+  );
+  console.log("-".repeat(80));
+  if (state.values.messages.length === 6) {
+    // We are somewhat arbitrarily selecting a specific state based on the number of chat messages in the state.
+    toReplay = state;
+  }
+}
+```
+
+```
+Num Messages: 8, Next: []
+--------------------------------------------------------------------------------
+Num Messages: 7, Next: ["chatbot"]
+--------------------------------------------------------------------------------
+Num Messages: 6, Next: ["tools"]
+--------------------------------------------------------------------------------
+Num Messages: 5, Next: ["chatbot"]
+--------------------------------------------------------------------------------
+Num Messages: 4, Next: ["__start__"]
+--------------------------------------------------------------------------------
+Num Messages: 4, Next: []
+--------------------------------------------------------------------------------
+Num Messages: 3, Next: ["chatbot"]
+--------------------------------------------------------------------------------
+Num Messages: 2, Next: ["tools"]
+--------------------------------------------------------------------------------
+Num Messages: 1, Next: ["chatbot"]
+--------------------------------------------------------------------------------
+Num Messages: 0, Next: ["__start__"]
+--------------------------------------------------------------------------------
+```
+
+:::
+
+Checkpoints are saved for every step of the graph. This **spans invocations** so you can rewind across a full thread's history.
 
 ## Resume from a checkpoint
 
+:::python
+
 Resume from the `to_replay` state, which is after the `chatbot` node in the second graph invocation. Resuming from this point will call the **action** node next.
 
 ```python
@@ -230,12 +456,37 @@ print(to_replay.config)
 {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1efd43e3-0c1f-6c4e-8006-891877d65740'}}
 ```
 
+:::
+
+:::js
+
+Resume from the `toReplay` state, which is after the `chatbot` node in one of the graph invocations. Resuming from this point will call the next scheduled node.
+
+```typescript
+console.log(toReplay.next);
+console.log(toReplay.config);
+```
+
+```
+["tools"]
+{
+  configurable: {
+    thread_id: "007708b8-ea9b-4ff7-a7ad-3843364dbf75",
+    checkpoint_ns: "",
+    checkpoint_id: "1efd43e3-0c1f-6c4e-8006-891877d65740"
+  }
+}
+```
+
+:::
+
 ## 4. Load a state from a moment-in-time
 
+:::python
+
 The checkpoint's `to_replay.config` contains a `checkpoint_id` timestamp. Providing this `checkpoint_id` value tells LangGraph's checkpointer to **load** the state from that moment in time.
 
-
-``` python
+```python
 # The `checkpoint_id` in the `to_replay.config` corresponds to a state we've persisted to our checkpointer.
 for event in graph.stream(None, to_replay.config, stream_mode="values"):
     if "messages" in event:
@@ -254,19 +505,16 @@ Tool Calls:
 ================================= Tool Message =================================
 Name: tavily_search_results_json
 
-[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user’s question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
+[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user's question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
 ================================== Ai Message ==================================
 
-Great idea! Building an autonomous agent with LangGraph is indeed an excellent way to apply and deepen your understanding of the technology. Based on the search results, I can provide you with some insights and resources to help you get started:
+Great idea! Building an autonomous agent with LangGraph is definitely an exciting project. Based on the latest information I've found, here are some insights and tips for building autonomous agents with LangGraph:
 
-1. Multi-Tool Agents:
-   LangGraph is well-suited for building autonomous agents that can use multiple tools. This allows your agent to have a variety of capabilities and choose the appropriate tool based on the task at hand.
+1. Multi-Tool Agents: LangGraph is particularly well-suited for creating autonomous agents that can use multiple tools. This allows your agent to have a diverse set of capabilities and choose the right tool for each task.
 
-2. Integration with Large Language Models (LLMs):
-   There's a tutorial that specifically mentions using Gemini 2.0 (Google's LLM) with LangGraph to build autonomous agents. This suggests that LangGraph can be integrated with various LLMs, giving you flexibility in choosing the language model that best fits your needs.
+2. Integration with Large Language Models (LLMs): You can combine LangGraph with powerful LLMs like Gemini 2.0 to create more intelligent and capable agents. The LLM can serve as the "brain" of your agent, making decisions and generating responses.
 
-3. Practical Tutorials:
-   There are tutorials available that provide full code examples for building and running multi-tool agents. These can be invaluable as you start your project, giving you a concrete starting point and demonstrating best practices.
+3. Workflow Management: LangGraph excels at managing complex, multi-step AI workflows. This is crucial for autonomous agents that need to break down tasks into smaller steps and execute them in the right order.
 ...
 
 Remember, building an autonomous agent is an iterative process. Start simple and gradually increase complexity as you become more comfortable with LangGraph and its capabilities.
@@ -275,7 +523,83 @@ Would you like more information on any specific aspect of building your autonomo
 Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
 ```
 
-The graph resumed execution from the `action` node. You can tell this is the case since the first value printed above is the response from our search engine tool.
+The graph resumed execution from the `tools` node. You can tell this is the case since the first value printed above is the response from our search engine tool.
+:::
+
+:::js
+
+The checkpoint's `toReplay.config` contains a `checkpoint_id` timestamp. Providing this `checkpoint_id` value tells LangGraph's checkpointer to **load** the state from that moment in time.
+
+```typescript
+// The `checkpoint_id` in the `toReplay.config` corresponds to a state we've persisted to our checkpointer.
+for await (const event of await graph.stream(null, {
+  ...toReplay?.config,
+  streamMode: "values",
+})) {
+  if ("messages" in event) {
+    const lastMessage = event.messages.at(-1);
+
+    console.log(
+      "=".repeat(32),
+      `${lastMessage?.getType()} Message`,
+      "=".repeat(32)
+    );
+    console.log(lastMessage?.text);
+  }
+}
+```
+
+```
+================================ ai Message ================================
+Let me search for specific information about building autonomous agents with LangGraph.js.
+================================ tool Message ================================
+{
+  "query": "how to build autonomous agents with LangGraph.js examples tutorial",
+  "follow_up_questions": null,
+  "answer": null,
+  "images": [],
+  "results": [
+    {
+      "url": "https://www.mongodb.com/developer/languages/typescript/build-javascript-ai-agent-langgraphjs-mongodb/",
+      "title": "Build a JavaScript AI Agent With LangGraph.js and MongoDB",
+      "content": "(...)",
+      "score": 0.7672197,
+      "raw_content": null
+    },
+    {
+      "url": "https://medium.com/@lorevanoudenhove/how-to-build-ai-agents-with-langgraph-a-step-by-step-guide-5d84d9c7e832",
+      "title": "How to Build AI Agents with LangGraph: A Step-by-Step Guide",
+      "content": "(...)",
+      "score": 0.7407191,
+      "raw_content": null
+    }
+  ],
+  "response_time": 0.82
+}
+================================ ai Message ================================
+Based on the search results, I can share some practical information about building autonomous agents with LangGraph.js. Here are some concrete examples and approaches:
+
+1. Example HR Assistant Agent:
+- Can handle HR-related queries using employee information
+- Features include:
+  - Starting and continuing conversations
+  - Looking up information using vector search
+  - Persisting conversation state using checkpoints
+  - Managing threaded conversations
+
+2. Energy Savings Calculator Agent:
+- Functions as a lead generation tool for solar panel sales
+- Capabilities include:
+  - Calculating potential energy savings
+  - Handling multi-step conversations
+  - Processing user inputs for personalized estimates
+  - Managing conversation state
+
+(...)
+```
+
+The graph resumed execution from the `tools` node. You can tell this is the case since the first value printed above is the response from our search engine tool.
+:::
 
 **Congratulations!** You've now used time-travel checkpoint traversal in LangGraph. Being able to rewind and explore alternative paths opens up a world of possibilities for debugging, experimentation, and interactive applications.
 
@@ -285,4 +609,4 @@ Take your LangGraph journey further by exploring deployment and advanced feature
 
 - **[LangGraph Server quickstart](../../tutorials/langgraph-platform/local-server.md)**: Launch a LangGraph server locally and interact with it using the REST API and LangGraph Studio Web UI.
 - **[LangGraph Platform quickstart](../../cloud/quick_start.md)**: Deploy your LangGraph app using LangGraph Platform.
-- **[LangGraph Platform concepts](../../concepts/langgraph_platform.md)**: Understand the foundational concepts of the LangGraph Platform.
+- **[LangGraph Platform concepts](../../concepts/langgraph_platform.md)**: Understand the foundational concepts of the LangGraph Platform.

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

@@ -12,9 +12,9 @@ Before you begin, ensure you have the following:
 
 === "Python server"
 
-    ```shell
-    # Python >= 3.11 is required.
+    Python >= 3.11 is required.
 
+    ```shell
     pip install --upgrade "langgraph-cli[inmem]"
     ```
 

docs/docs/tutorials/reflection/reflection.ipynb

@@ -322,7 +322,7 @@
     "from typing import Annotated, List, Sequence\n",
     "from langgraph.graph import END, StateGraph, START\n",
     "from langgraph.graph.message import add_messages\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "from typing_extensions import TypedDict\n",
     "\n",
     "\n",
@@ -361,7 +361,7 @@
     "\n",
     "builder.add_conditional_edges(\"generate\", should_continue)\n",
     "builder.add_edge(\"reflect\", \"generate\")\n",
-    "memory = MemorySaver()\n",
+    "memory = InMemorySaver()\n",
     "graph = builder.compile(checkpointer=memory)"
    ]
   },

docs/docs/tutorials/tot/tot.ipynb

@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -280,10 +280,10 @@
     "from typing import Optional, Dict, Any\n",
     "from typing_extensions import Annotated, TypedDict\n",
     "from langgraph.graph import StateGraph\n",
+    "from langgraph.runtime import Runtime\n",
     "\n",
-    "from langchain_core.runnables import RunnableConfig\n",
-    "from langgraph.constants import Send\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
+    "from langgraph.types import Send\n",
     "\n",
     "\n",
     "def update_candidates(\n",
@@ -307,22 +307,27 @@
     "    depth: Annotated[int, operator.add]\n",
     "\n",
     "\n",
-    "class Configuration(TypedDict, total=False):\n",
+    "class Context(TypedDict, total=False):\n",
     "    max_depth: int\n",
     "    threshold: float\n",
     "    k: int\n",
     "    beam_size: int\n",
     "\n",
     "\n",
-    "def _ensure_configurable(config: RunnableConfig) -> Configuration:\n",
+    "class EnsuredContext(TypedDict):\n",
+    "    max_depth: int\n",
+    "    threshold: float\n",
+    "    k: int\n",
+    "    beam_size: int\n",
+    "\n",
+    "\n",
+    "def _ensure_context(ctx: Context) -> EnsuredContext:\n",
     "    \"\"\"Get params that configure the search algorithm.\"\"\"\n",
-    "    configurable = config.get(\"configurable\", {})\n",
     "    return {\n",
-    "        **configurable,\n",
-    "        \"max_depth\": configurable.get(\"max_depth\", 10),\n",
-    "        \"threshold\": config.get(\"threshold\", 0.9),\n",
-    "        \"k\": configurable.get(\"k\", 5),\n",
-    "        \"beam_size\": configurable.get(\"beam_size\", 3),\n",
+    "        \"max_depth\": ctx.get(\"max_depth\", 10),\n",
+    "        \"threshold\": ctx.get(\"threshold\", 0.9),\n",
+    "        \"k\": ctx.get(\"k\", 5),\n",
+    "        \"beam_size\": ctx.get(\"beam_size\", 3),\n",
     "    }\n",
     "\n",
     "\n",
@@ -330,9 +335,11 @@
     "    seed: Optional[Candidate]\n",
     "\n",
     "\n",
-    "def expand(state: ExpansionState, *, config: RunnableConfig) -> Dict[str, List[str]]:\n",
+    "def expand(\n",
+    "    state: ExpansionState, *, runtime: Runtime[Context]\n",
+    ") -> Dict[str, List[Candidate]]:\n",
     "    \"\"\"Generate the next state.\"\"\"\n",
-    "    configurable = _ensure_configurable(config)\n",
+    "    ctx = _ensure_context(runtime.context)\n",
     "    if not state.get(\"seed\"):\n",
     "        candidate_str = \"\"\n",
     "    else:\n",
@@ -342,9 +349,8 @@
     "            {\n",
     "                \"problem\": state[\"problem\"],\n",
     "                \"candidate\": candidate_str,\n",
-    "                \"k\": configurable[\"k\"],\n",
+    "                \"k\": ctx[\"k\"],\n",
     "            },\n",
-    "            config=config,\n",
     "        )\n",
     "    except Exception:\n",
     "        return {\"candidates\": []}\n",
@@ -354,7 +360,7 @@
     "    return {\"candidates\": new_candidates}\n",
     "\n",
     "\n",
-    "def score(state: ToTState) -> Dict[str, List[float]]:\n",
+    "def score(state: ToTState) -> Dict[str, Any]:\n",
     "    \"\"\"Evaluate the candidate generations.\"\"\"\n",
     "    candidates = state[\"candidates\"]\n",
     "    scored = []\n",
@@ -363,11 +369,9 @@
     "    return {\"scored_candidates\": scored, \"candidates\": \"clear\"}\n",
     "\n",
     "\n",
-    "def prune(\n",
-    "    state: ToTState, *, config: RunnableConfig\n",
-    ") -> Dict[str, List[Dict[str, Any]]]:\n",
+    "def prune(state: ToTState, *, runtime: Runtime[Context]) -> Dict[str, Any]:\n",
     "    scored_candidates = state[\"scored_candidates\"]\n",
-    "    beam_size = _ensure_configurable(config)[\"beam_size\"]\n",
+    "    beam_size = _ensure_context(runtime.context)[\"beam_size\"]\n",
     "    organized = sorted(\n",
     "        scored_candidates, key=lambda candidate: candidate[1], reverse=True\n",
     "    )\n",
@@ -383,11 +387,11 @@
     "\n",
     "\n",
     "def should_terminate(\n",
-    "    state: ToTState, config: RunnableConfig\n",
+    "    state: ToTState, runtime: Runtime[Context]\n",
     ") -> Union[Literal[\"__end__\"], Send]:\n",
-    "    configurable = _ensure_configurable(config)\n",
-    "    solved = state[\"candidates\"][0].score >= configurable[\"threshold\"]\n",
-    "    if solved or state[\"depth\"] >= configurable[\"max_depth\"]:\n",
+    "    ctx = _ensure_context(runtime.context)\n",
+    "    solved = state[\"candidates\"][0].score >= ctx[\"threshold\"]\n",
+    "    if solved or state[\"depth\"] >= ctx[\"max_depth\"]:\n",
     "        return \"__end__\"\n",
     "    return [\n",
     "        Send(\"expand\", {**state, \"somevalseed\": candidate})\n",
@@ -396,7 +400,7 @@
     "\n",
     "\n",
     "# Create the graph\n",
-    "builder = StateGraph(state_schema=ToTState, config_schema=Configuration)\n",
+    "builder = StateGraph(state_schema=ToTState, context_schema=Context)\n",
     "\n",
     "# Add nodes\n",
     "builder.add_node(expand)\n",
@@ -412,7 +416,7 @@
     "builder.add_edge(\"__start__\", \"expand\")\n",
     "\n",
     "# Compile the graph\n",
-    "graph = builder.compile(checkpointer=MemorySaver())"
+    "graph = builder.compile(checkpointer=InMemorySaver())"
    ]
   },
   {
@@ -467,13 +471,11 @@
     }
    ],
    "source": [
-    "config = {\n",
-    "    \"configurable\": {\n",
-    "        \"thread_id\": \"test_1\",\n",
-    "        \"depth\": 10,\n",
-    "    }\n",
-    "}\n",
-    "for step in graph.stream({\"problem\": puzzles[42]}, config):\n",
+    "for step in graph.stream(\n",
+    "    {\"problem\": puzzles[42]},\n",
+    "    config={\"configurable\": {\"thread_id\": \"test_1\"}},\n",
+    "    context={\"depth\": 10},\n",
+    "):\n",
     "    print(step)"
    ]
   },
@@ -491,7 +493,7 @@
     }
    ],
    "source": [
-    "final_state = graph.get_state(config)\n",
+    "final_state = graph.get_state({\"configurable\": {\"thread_id\": \"test_1\"}})\n",
     "winning_solution = final_state.values[\"candidates\"][0]\n",
     "search_depth = final_state.values[\"depth\"]\n",
     "if winning_solution[1] == 1:\n",

docs/docs/tutorials/usaco/usaco.ipynb

@@ -1029,7 +1029,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "from langgraph.graph import END, StateGraph, START\n",
     "\n",
     "builder = StateGraph(State)\n",
@@ -1053,7 +1053,7 @@
     "builder.add_conditional_edges(\"evaluate\", control_edge, {END: END, \"solve\": \"solve\"})\n",
     "\n",
     "\n",
-    "checkpointer = MemorySaver()\n",
+    "checkpointer = InMemorySaver()\n",
     "graph = builder.compile(checkpointer=checkpointer)"
    ]
   },
@@ -1327,7 +1327,7 @@
    "outputs": [],
    "source": [
     "# This is all the same as before\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
     "from langgraph.graph import END, StateGraph, START\n",
     "\n",
     "builder = StateGraph(State)\n",
@@ -1353,7 +1353,7 @@
     "\n",
     "\n",
     "builder.add_conditional_edges(\"evaluate\", control_edge, {END: END, \"solve\": \"solve\"})\n",
-    "checkpointer = MemorySaver()"
+    "checkpointer = InMemorySaver()"
    ]
   },
   {

docs/mkdocs.yml

@@ -103,14 +103,15 @@ nav:
           -  5. Customize state: tutorials/get-started/5-customize-state.md
           -  6. Time travel: tutorials/get-started/6-time-travel.md
       - Run a local server: tutorials/langgraph-platform/local-server.md
-    - Agent development:
+    - General concepts:
       - Workflows & agents: tutorials/workflows.md
-      - Prebuilt components: agents/overview.md
-      - Run an agent: agents/run_agents.md
       - Agent architectures: concepts/agentic_concepts.md
 
   - Guides:
     - guides/index.md
+    - Agent development:
+      - Overview: agents/overview.md
+      - Run an agent: agents/run_agents.md
     - LangGraph APIs:
       - Graph API:
           - Overview: concepts/low_level.md
@@ -249,6 +250,7 @@ nav:
       - Storage: reference/store.md
       - Caching: reference/cache.md
       - Types: reference/types.md
+      - Runtime: reference/runtime.md
       - Config: reference/config.md
       - Errors: reference/errors.md
       - Constants: reference/constants.md

docs/uv.lock

@@ -15,16 +15,16 @@ name = "ag2"
 version = "0.9.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "anyio" },
-    { name = "asyncer" },
-    { name = "diskcache" },
-    { name = "docker" },
-    { name = "httpx" },
-    { name = "packaging" },
-    { name = "pydantic" },
-    { name = "python-dotenv" },
-    { name = "termcolor" },
-    { name = "tiktoken" },
+    { name = "anyio", marker = "python_full_version < '3.13'" },
+    { name = "asyncer", marker = "python_full_version < '3.13'" },
+    { name = "diskcache", marker = "python_full_version < '3.13'" },
+    { name = "docker", marker = "python_full_version < '3.13'" },
+    { name = "httpx", marker = "python_full_version < '3.13'" },
+    { name = "packaging", marker = "python_full_version < '3.13'" },
+    { name = "pydantic", marker = "python_full_version < '3.13'" },
+    { name = "python-dotenv", marker = "python_full_version < '3.13'" },
+    { name = "termcolor", marker = "python_full_version < '3.13'" },
+    { name = "tiktoken", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/ee/15/edfbbf217e19ea647225b3ab72a6e3755d2677665f1a7f8e5108da3feabd/ag2-0.9.6.tar.gz", hash = "sha256:d6f7812b1a49654d14113fa3c13ccb593115dee1193744ca428d7178d2b32090", size = 3356270, upload-time = "2025-07-08T14:56:21.63Z" }
 wheels = [
@@ -267,7 +267,7 @@ name = "asyncer"
 version = "0.0.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "anyio" },
+    { name = "anyio", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/ff/67/7ea59c3e69eaeee42e7fc91a5be67ca5849c8979acac2b920249760c6af2/asyncer-0.0.8.tar.gz", hash = "sha256:a589d980f57e20efb07ed91d0dbe67f1d2fd343e7142c66d3a099f05c620739c", size = 18217, upload-time = "2024-08-24T23:15:36.449Z" }
 wheels = [
@@ -288,7 +288,7 @@ name = "autogen"
 version = "0.9.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "ag2" },
+    { name = "ag2", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/67/b9/dc958031b7e08ee50e3d40f5991f4c0bc21538df8d53aa3e9a9f2e2f7818/autogen-0.9.6.tar.gz", hash = "sha256:dc2efbeef61002608983afb120e62f8a109815eb741bcbc9ef398dcff7424a30", size = 43422, upload-time = "2025-07-08T14:56:17.6Z" }
 wheels = [
@@ -914,9 +914,9 @@ name = "docker"
 version = "7.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pywin32", marker = "sys_platform == 'win32'" },
-    { name = "requests" },
-    { name = "urllib3" },
+    { name = "pywin32", marker = "python_full_version < '3.13' and sys_platform == 'win32'" },
+    { name = "requests", marker = "python_full_version < '3.13'" },
+    { name = "urllib3", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/91/9b/4a2ea29aeba62471211598dac5d96825bb49348fa07e906ea930394a83ce/docker-7.1.0.tar.gz", hash = "sha256:ad8c70e6e3f8926cb8a92619b832b4ea5299e2831c14284663184e200546fa6c", size = 117834, upload-time = "2024-05-23T11:13:57.216Z" }
 wheels = [
@@ -2337,7 +2337,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.5.2"
+version = "0.6.0a1"
 source = { editable = "../libs/langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -2365,7 +2365,7 @@ dev = [
     { name = "langgraph-checkpoint", editable = "../libs/checkpoint" },
     { name = "langgraph-checkpoint-postgres", editable = "../libs/checkpoint-postgres" },
     { name = "langgraph-checkpoint-sqlite", editable = "../libs/checkpoint-sqlite" },
-    { name = "langgraph-cli", extras = ["inmem"] },
+    { name = "langgraph-cli", extras = ["inmem"], editable = "../libs/cli" },
     { name = "langgraph-prebuilt", editable = "../libs/prebuilt" },
     { name = "langgraph-sdk", editable = "../libs/sdk-py" },
     { name = "mypy" },
@@ -2388,7 +2388,7 @@ dev = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 source = { editable = "../libs/checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -2433,7 +2433,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.21"
+version = "2.0.23"
 source = { editable = "../libs/checkpoint-postgres" }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -2674,7 +2674,7 @@ dev = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.72"
+version = "0.2.0a1"
 source = { editable = "../libs/sdk-py" }
 dependencies = [
     { name = "httpx" },

docs/yarn.lock

@@ -152,6 +152,14 @@ base64-js@^1.5.1:
   resolved "https://registry.yarnpkg.com/base64-js/-/base64-js-1.5.1.tgz#1b1b440160a5bf7ad40b650f095963481903930a"
   integrity sha512-AKpaYlHn8t4SVbOHCy+b5+KKgvR4vrsD8vbvrbiQJps7fKDTkjkDry6ji0rUJjC0kzbNePLwzxq8iypo41qeWA==
 
+call-bind-apply-helpers@^1.0.1, call-bind-apply-helpers@^1.0.2:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/call-bind-apply-helpers/-/call-bind-apply-helpers-1.0.2.tgz#4b5428c222be985d79c3d82657479dbe0b59b2d6"
+  integrity sha512-Sp1ablJ0ivDkSzjcaJdxEunN5/XvksFJ2sMBFfq6x0ryhQV/2b/KwFe21cMpmHtPOSij8K99/wSfoEuTObmuMQ==
+  dependencies:
+    es-errors "^1.3.0"
+    function-bind "^1.1.2"
+
 camelcase@6:
   version "6.3.0"
   resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-6.3.0.tgz#5685b95eb209ac9c0c177467778c9c84df58ba9a"
@@ -201,6 +209,42 @@ delayed-stream@~1.0.0:
   resolved "https://registry.yarnpkg.com/delayed-stream/-/delayed-stream-1.0.0.tgz#df3ae199acadfb7d440aaae0b29e2272b24ec619"
   integrity sha512-ZySD7Nf91aLB0RxL4KGrKHBXl7Eds1DAmEdcoVawXnLD7SDhpNgtuII2aAkg7a7QS41jxPSZ17p4VdGnMHk3MQ==
 
+dunder-proto@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/dunder-proto/-/dunder-proto-1.0.1.tgz#d7ae667e1dc83482f8b70fd0f6eefc50da30f58a"
+  integrity sha512-KIN/nDJBQRcXw0MLVhZE9iQHmG68qAVIBg9CqmUYjmQIhgij9U5MFvrqkUL5FbtyyzZuOeOt0zdeRe4UY7ct+A==
+  dependencies:
+    call-bind-apply-helpers "^1.0.1"
+    es-errors "^1.3.0"
+    gopd "^1.2.0"
+
+es-define-property@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/es-define-property/-/es-define-property-1.0.1.tgz#983eb2f9a6724e9303f61addf011c72e09e0b0fa"
+  integrity sha512-e3nRfgfUZ4rNGL232gUgX06QNyyez04KdjFrF+LTRoOXmrOgFKDg4BCdsjW8EnT69eqdYGmRpJwiPVYNrCaW3g==
+
+es-errors@^1.3.0:
+  version "1.3.0"
+  resolved "https://registry.yarnpkg.com/es-errors/-/es-errors-1.3.0.tgz#05f75a25dab98e4fb1dcd5e1472c0546d5057c8f"
+  integrity sha512-Zf5H2Kxt2xjTvbJvP2ZWLEICxA6j+hAmMzIlypy4xcBg1vKVnx89Wy0GbS+kf5cwCVFFzdCFh2XSCFNULS6csw==
+
+es-object-atoms@^1.0.0, es-object-atoms@^1.1.1:
+  version "1.1.1"
+  resolved "https://registry.yarnpkg.com/es-object-atoms/-/es-object-atoms-1.1.1.tgz#1c4f2c4837327597ce69d2ca190a7fdd172338c1"
+  integrity sha512-FGgH2h8zKNim9ljj7dankFPcICIK9Cp5bm+c2gQSYePhpaG5+esrLODihIorn+Pe6FGJzWhXQotPv73jTaldXA==
+  dependencies:
+    es-errors "^1.3.0"
+
+es-set-tostringtag@^2.1.0:
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/es-set-tostringtag/-/es-set-tostringtag-2.1.0.tgz#f31dbbe0c183b00a6d26eb6325c810c0fd18bd4d"
+  integrity sha512-j6vWzfrGVfyXxge+O0x5sh6cvxAog0a/4Rdd2K36zCMV5eJ+/+tOAngRO8cODMNWbVRdVlmGZQL2YS3yR8bIUA==
+  dependencies:
+    es-errors "^1.3.0"
+    get-intrinsic "^1.2.6"
+    has-tostringtag "^1.0.2"
+    hasown "^2.0.2"
+
 event-lite@^0.1.1:
   version "0.1.3"
   resolved "https://registry.yarnpkg.com/event-lite/-/event-lite-0.1.3.tgz#3dfe01144e808ac46448f0c19b4ab68e403a901d"
@@ -222,12 +266,14 @@ form-data-encoder@1.7.2:
   integrity sha512-qfqtYan3rxrnCk1VYaA4H+Ms9xdpPqvLZa6xmMgFvhO32x7/3J/ExcTd6qpxM0vH2GdMI+poehyBZvqfMTto8A==
 
 form-data@^4.0.0:
-  version "4.0.1"
-  resolved "https://registry.yarnpkg.com/form-data/-/form-data-4.0.1.tgz#ba1076daaaa5bfd7e99c1a6cb02aa0a5cff90d48"
-  integrity sha512-tzN8e4TX8+kkxGPK8D5u0FNmjPUjw3lwC9lSLxxoB/+GtsJG91CO8bSWy73APlgAZzZbXEYZJuxjkHH2w+Ezhw==
+  version "4.0.4"
+  resolved "https://registry.yarnpkg.com/form-data/-/form-data-4.0.4.tgz#784cdcce0669a9d68e94d11ac4eea98088edd2c4"
+  integrity sha512-KrGhL9Q4zjj0kiUt5OO4Mr/A/jlI2jDYs5eHBpYHPcBEVSiipAvn2Ko2HnPe20rmcuuvMHNdZFp+4IlGTMF0Ow==
   dependencies:
     asynckit "^0.4.0"
     combined-stream "^1.0.8"
+    es-set-tostringtag "^2.1.0"
+    hasown "^2.0.2"
     mime-types "^2.1.12"
 
 formdata-node@^4.3.2:
@@ -238,11 +284,69 @@ formdata-node@^4.3.2:
     node-domexception "1.0.0"
     web-streams-polyfill "4.0.0-beta.3"
 
+function-bind@^1.1.2:
+  version "1.1.2"
+  resolved "https://registry.yarnpkg.com/function-bind/-/function-bind-1.1.2.tgz#2c02d864d97f3ea6c8830c464cbd11ab6eab7a1c"
+  integrity sha512-7XHNxH7qX9xG5mIwxkhumTox/MIRNcOgDrxWsMt2pAr23WHp6MrRlN7FBSFpCpr+oVO0F744iUgR82nJMfG2SA==
+
+get-intrinsic@^1.2.6:
+  version "1.3.0"
+  resolved "https://registry.yarnpkg.com/get-intrinsic/-/get-intrinsic-1.3.0.tgz#743f0e3b6964a93a5491ed1bffaae054d7f98d01"
+  integrity sha512-9fSjSaos/fRIVIp+xSJlE6lfwhES7LNtKaCBIamHsjr2na1BiABJPo0mOjjz8GJDURarmCPGqaiVg5mfjb98CQ==
+  dependencies:
+    call-bind-apply-helpers "^1.0.2"
+    es-define-property "^1.0.1"
+    es-errors "^1.3.0"
+    es-object-atoms "^1.1.1"
+    function-bind "^1.1.2"
+    get-proto "^1.0.1"
+    gopd "^1.2.0"
+    has-symbols "^1.1.0"
+    hasown "^2.0.2"
+    math-intrinsics "^1.1.0"
+
+get-proto@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/get-proto/-/get-proto-1.0.1.tgz#150b3f2743869ef3e851ec0c49d15b1d14d00ee1"
+  integrity sha512-sTSfBjoXBp89JvIKIefqw7U2CCebsc74kiY6awiGogKtoSGbgjYE/G/+l9sF3MWFPNc9IcoOC4ODfKHfxFmp0g==
+  dependencies:
+    dunder-proto "^1.0.1"
+    es-object-atoms "^1.0.0"
+
+gopd@^1.2.0:
+  version "1.2.0"
+  resolved "https://registry.yarnpkg.com/gopd/-/gopd-1.2.0.tgz#89f56b8217bdbc8802bd299df6d7f1081d7e51a1"
+  integrity sha512-ZUKRh6/kUFoAiTAtTYPZJ3hw9wNxx+BIBOijnlG9PnrJsCcSjs1wyyD6vJpaYtgnzDrKYRSqf3OO6Rfa93xsRg==
+
 has-flag@^4.0.0:
   version "4.0.0"
   resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-4.0.0.tgz#944771fd9c81c81265c4d6941860da06bb59479b"
   integrity sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==
 
+has-symbols@^1.0.3, has-symbols@^1.1.0:
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/has-symbols/-/has-symbols-1.1.0.tgz#fc9c6a783a084951d0b971fe1018de813707a338"
+  integrity sha512-1cDNdwJ2Jaohmb3sg4OmKaMBwuC48sYni5HUw2DvsC8LjGTLK9h+eb1X6RyuOHe4hT0ULCW68iomhjUoKUqlPQ==
+
+has-tostringtag@^1.0.2:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/has-tostringtag/-/has-tostringtag-1.0.2.tgz#2cdc42d40bef2e5b4eeab7c01a73c54ce7ab5abc"
+  integrity sha512-NqADB8VjPFLM2V0VvHUewwwsw0ZWBaIdgo+ieHtK3hasLz4qeCRjYcqfB6AQrBggRKppKF8L52/VqdVsO47Dlw==
+  dependencies:
+    has-symbols "^1.0.3"
+
+hasown@^2.0.2:
+  version "2.0.2"
+  resolved "https://registry.yarnpkg.com/hasown/-/hasown-2.0.2.tgz#003eaf91be7adc372e84ec59dc37252cedb80003"
+  integrity sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ==
+  dependencies:
+    function-bind "^1.1.2"
+
+he@^1.2.0:
+  version "1.2.0"
+  resolved "https://registry.yarnpkg.com/he/-/he-1.2.0.tgz#84ae65fa7eafb165fddb61566ae14baf05664f0f"
+  integrity sha512-F/1DnUGPopORZi0ni+CvrCgHQ5FyEAHRLSApuYWMmrbSwoN2Mn/7k+Gl38gJnR7yyDZk6WLXwiGod1JOWNDKGw==
+
 humanize-ms@^1.2.1:
   version "1.2.1"
   resolved "https://registry.yarnpkg.com/humanize-ms/-/humanize-ms-1.2.1.tgz#c46e3159a293f6b896da29316d8b6fe8bb79bbed"
@@ -295,6 +399,11 @@ json-stringify-safe@^5.0.1:
     semver "^7.6.3"
     uuid "^10.0.0"
 
+math-intrinsics@^1.1.0:
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/math-intrinsics/-/math-intrinsics-1.1.0.tgz#a0dd74be81e2aa5c2f27e65ce283605ee4e2b7f9"
+  integrity sha512-/IXtbwEk5HTPyEwyKX6hGkYXxM9nbj64B+ilVJnC/R6B0pH5G4V3b0pVbL7DBj4tkhBAppbQUlf6F6Xl9LHu1g==
+
 mime-db@1.52.0:
   version "1.52.0"
   resolved "https://registry.yarnpkg.com/mime-db/-/mime-db-1.52.0.tgz#bbabcdc02859f4987301c856e3387ce5ec43bf70"

examples/code_assistant/langgraph_code_assistant_mistral.ipynb

@@ -154,7 +154,7 @@
    "id": "2dff2209-44c7-4e2c-b607-ba6675f9e45f",
    "metadata": {},
    "outputs": [],
-   "source": ["from langgraph.checkpoint.memory import MemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate)  # generation solution\nbuilder.add_node(\"check_code\", code_check)  # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n    \"check_code\",\n    decide_to_finish,\n    {\n        \"end\": END,\n        \"generate\": \"generate\",\n    },\n)\n\nmemory = MemorySaver()\ngraph = builder.compile(checkpointer=memory)"]
+   "source": ["from langgraph.checkpoint.memory import InMemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate)  # generation solution\nbuilder.add_node(\"check_code\", code_check)  # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n    \"check_code\",\n    decide_to_finish,\n    {\n        \"end\": END,\n        \"generate\": \"generate\",\n    },\n)\n\nmemory = InMemorySaver()\ngraph = builder.compile(checkpointer=memory)"]
   },
   {
    "cell_type": "code",

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

@@ -284,10 +284,7 @@ class PostgresSaver(BasePostgresSaver):
         configurable = config["configurable"].copy()
         thread_id = configurable.pop("thread_id")
         checkpoint_ns = configurable.pop("checkpoint_ns")
-        checkpoint_id = configurable.pop(
-            "checkpoint_id", configurable.pop("thread_ts", None)
-        )
-
+        checkpoint_id = configurable.pop("checkpoint_id", None)
         copy = checkpoint.copy()
         copy["channel_values"] = copy["channel_values"].copy()
         next_config = {

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

@@ -240,9 +240,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
         configurable = config["configurable"].copy()
         thread_id = configurable.pop("thread_id")
         checkpoint_ns = configurable.pop("checkpoint_ns")
-        checkpoint_id = configurable.pop(
-            "checkpoint_id", configurable.pop("thread_ts", None)
-        )
+        checkpoint_id = configurable.pop("checkpoint_id", None)
 
         copy = checkpoint.copy()
         copy["channel_values"] = copy["channel_values"].copy()

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

@@ -191,7 +191,7 @@ class ShallowPostgresSaver(BasePostgresSaver):
     ) -> None:
         warnings.warn(
             "ShallowPostgresSaver is deprecated as of version 2.0.20 and will be removed in 3.0.0. "
-            "Use PostgresSaver instead, and invoke the graph with `graph.invoke(..., checkpoint_during=False)`.",
+            "Use PostgresSaver instead, and invoke the graph with `graph.invoke(..., durability='exit')`.",
             DeprecationWarning,
             stacklevel=2,
         )
@@ -547,7 +547,7 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
     ) -> None:
         warnings.warn(
             "AsyncShallowPostgresSaver is deprecated as of version 2.0.20 and will be removed in 3.0.0. "
-            "Use AsyncPostgresSaver instead, and invoke the graph with `await graph.ainvoke(..., checkpoint_during=False)`.",
+            "Use AsyncPostgresSaver instead, and invoke the graph with `await graph.ainvoke(..., durability='exit')`.",
             DeprecationWarning,
             stacklevel=2,
         )

libs/checkpoint-postgres/tests/test_async.py

@@ -161,8 +161,7 @@ def test_data():
     config_1: RunnableConfig = {
         "configurable": {
             "thread_id": "thread-1",
-            # for backwards compatibility testing
-            "thread_ts": "1",
+            "checkpoint_id": "1",
             "checkpoint_ns": "",
         }
     }

libs/checkpoint-postgres/tests/test_sync.py

@@ -143,8 +143,7 @@ def test_data():
     config_1: RunnableConfig = {
         "configurable": {
             "thread_id": "thread-1",
-            # for backwards compatibility testing
-            "thread_ts": "1",
+            "checkpoint_id": "1",
             "checkpoint_ns": "",
         }
     }

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

@@ -3,6 +3,7 @@ from __future__ import annotations
 import concurrent.futures
 import datetime
 import logging
+import re
 import sqlite3
 import threading
 from collections import defaultdict
@@ -107,6 +108,23 @@ def _decode_ns_text(namespace: str) -> tuple[str, ...]:
     return tuple(namespace.split("."))
 
 
+def _validate_filter_key(key: str) -> None:
+    """Validate that a filter key is safe for use in SQL queries.
+
+    Args:
+        key: The filter key to validate
+
+    Raises:
+        ValueError: If the key contains invalid characters that could enable SQL injection
+    """
+    # Allow alphanumeric characters, underscores, dots, and hyphens
+    # This covers typical JSON property names while preventing SQL injection
+    if not re.match(r"^[a-zA-Z0-9_.-]+$", key):
+        raise ValueError(
+            f"Invalid filter key: '{key}'. Filter keys must contain only alphanumeric characters, underscores, dots, and hyphens."
+        )
+
+
 def _json_loads(content: bytes | str | orjson.Fragment) -> Any:
     if isinstance(content, orjson.Fragment):
         if hasattr(content, "buf"):
@@ -372,6 +390,8 @@ class BaseSqliteStore:
             filter_conditions = []
             if op.filter:
                 for key, value in op.filter.items():
+                    _validate_filter_key(key)
+
                     if isinstance(value, dict):
                         for op_name, val in value.items():
                             condition, filter_params_ = self._get_filter_condition(
@@ -622,6 +642,8 @@ class BaseSqliteStore:
 
     def _get_filter_condition(self, key: str, op: str, value: Any) -> tuple[str, list]:
         """Helper to generate filter conditions."""
+        _validate_filter_key(key)
+
         # We need to properly format values for SQLite JSON extraction comparison
         if op == "$eq":
             if isinstance(value, str):
@@ -858,6 +880,8 @@ class SqliteStore(BaseSqliteStore, BaseStore):
 
     def _get_filter_condition(self, key: str, op: str, value: Any) -> tuple[str, list]:
         """Helper to generate filter conditions."""
+        _validate_filter_key(key)
+
         # We need to properly format values for SQLite JSON extraction comparison
         if op == "$eq":
             if isinstance(value, str):

libs/checkpoint-sqlite/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-sqlite"
-version = "2.0.10"
+version = "2.0.11"
 description = "Library with a SQLite implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.9"

libs/checkpoint-sqlite/tests/test_aiosqlite.py

@@ -19,8 +19,7 @@ class TestAsyncSqliteSaver:
         self.config_1: RunnableConfig = {
             "configurable": {
                 "thread_id": "thread-1",
-                # for backwards compatibility testing
-                "thread_ts": "1",
+                "checkpoint_id": "1",
                 "checkpoint_ns": "",
             }
         }

libs/checkpoint-sqlite/tests/test_sqlite.py

@@ -21,7 +21,7 @@ class TestSqliteSaver:
             "configurable": {
                 "thread_id": "thread-1",
                 # for backwards compatibility testing
-                "thread_ts": "1",
+                "checkpoint_id": "1",
                 "checkpoint_ns": "",
             }
         }

libs/checkpoint-sqlite/tests/test_store.py

@@ -1047,3 +1047,23 @@ def test_search_items(
         for ns in test_namespaces:
             key = f"item_{ns[-1]}"
             store.delete(ns, key)
+
+
+def test_sql_injection_vulnerability(store: SqliteStore) -> None:
+    """Test that SQL injection via malicious filter keys is prevented."""
+    # Add public and private documents
+    store.put(("docs",), "public", {"access": "public", "data": "public info"})
+    store.put(
+        ("docs",), "private", {"access": "private", "data": "secret", "password": "123"}
+    )
+
+    # Normal query - returns 1 public document
+    normal = store.search(("docs",), filter={"access": "public"})
+    assert len(normal) == 1
+    assert normal[0].value["access"] == "public"
+
+    # SQL injection attempt via malicious key should raise ValueError
+    malicious_key = "access') = 'public' OR '1'='1' OR json_extract(value, '$."
+
+    with pytest.raises(ValueError, match="Invalid filter key"):
+        store.search(("docs",), filter={malicious_key: "dummy"})

libs/checkpoint-sqlite/uv.lock

@@ -346,7 +346,7 @@ dev = [
 
 [[package]]
 name = "langgraph-checkpoint-sqlite"
-version = "2.0.10"
+version = "2.0.11"
 source = { editable = "." }
 dependencies = [
     { name = "aiosqlite" },

libs/checkpoint/README.md

@@ -36,7 +36,7 @@ Each checkpointer should conform to `langgraph.checkpoint.base.BaseCheckpointSav
 
 - `.put` - Store a checkpoint with its configuration and metadata.
 - `.put_writes` - Store intermediate writes linked to a checkpoint (i.e. pending writes).
-- `.get_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `thread_ts`).
+- `.get_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `checkpoint_id`).
 - `.list` - List checkpoints that match a given configuration and filter criteria.
 
 If the checkpointer will be used with asynchronous graph execution (i.e. executing the graph via `.ainvoke`, `.astream`, `.abatch`), checkpointer must implement asynchronous versions of the above methods (`.aput`, `.aput_writes`, `.aget_tuple`, `.alist`).
@@ -44,12 +44,12 @@ If the checkpointer will be used with asynchronous graph execution (i.e. executi
 ## Usage
 
 ```python
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 
 write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
 read_config = {"configurable": {"thread_id": "1"}}
 
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 checkpoint = {
     "v": 4,
     "ts": "2024-07-31T20:14:19.804150+00:00",

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

@@ -375,10 +375,8 @@ class EmptyChannelError(Exception):
 
 
 def get_checkpoint_id(config: RunnableConfig) -> str | None:
-    """Get checkpoint ID in a backwards-compatible manner (fallback on thread_ts)."""
-    return config["configurable"].get(
-        "checkpoint_id", config["configurable"].get("thread_ts")
-    )
+    """Get checkpoint ID."""
+    return config["configurable"].get("checkpoint_id")
 
 
 def get_checkpoint_metadata(
@@ -413,7 +411,6 @@ WRITES_IDX_MAP = {ERROR: -1, SCHEDULED: -2, INTERRUPT: -3, RESUME: -4}
 
 EXCLUDED_METADATA_KEYS = {
     "thread_id",
-    "thread_ts",
     "checkpoint_id",
     "checkpoint_ns",
     "checkpoint_map",

libs/checkpoint/tests/test_memory.py

@@ -22,8 +22,7 @@ class TestMemorySaver:
             "configurable": {
                 "thread_id": "thread-1",
                 "checkpoint_ns": "",
-                # for backwards compatibility testing
-                "thread_ts": "1",
+                "checkpoint_id": "1",
             }
         }
         self.config_2: RunnableConfig = {
@@ -190,6 +189,6 @@ class TestMemorySaver:
 
 
 def test_memory_saver() -> None:
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
-    assert isinstance(MemorySaver(), InMemorySaver)
+    assert isinstance(InMemorySaver(), InMemorySaver)

libs/cli/examples/graphs/agent.py

@@ -49,12 +49,12 @@ def call_model(state, config):
 tool_node = ToolNode(tools)
 
 
-class ConfigSchema(TypedDict):
+class ContextSchema(TypedDict):
     model: Literal["anthropic", "openai"]
 
 
 # Define a new graph
-workflow = StateGraph(AgentState, config_schema=ConfigSchema)
+workflow = StateGraph(AgentState, context_schema=ContextSchema)
 
 # Define the two nodes we will cycle between
 workflow.add_node("agent", call_model)

libs/cli/langgraph_cli/cli.py

@@ -153,6 +153,12 @@ OPT_POSTGRES_URI = click.option(
     help="Postgres URI to use for the database. Defaults to launching a local database",
 )
 
+OPT_API_VERSION = click.option(
+    "--api-version",
+    type=str,
+    help="API server version to use for the base image. If unspecified, the latest version will be used.",
+)
+
 
 @click.group()
 @click.version_option(version=__version__, prog_name="LangGraph CLI")
@@ -170,6 +176,7 @@ def cli():
 @OPT_DEBUGGER_BASE_URL
 @OPT_WATCH
 @OPT_POSTGRES_URI
+@OPT_API_VERSION
 @click.option(
     "--image",
     type=str,
@@ -203,6 +210,7 @@ def up(
     debugger_port: Optional[int],
     debugger_base_url: Optional[str],
     postgres_uri: Optional[str],
+    api_version: Optional[str],
     image: Optional[str],
     base_image: Optional[str],
 ):
@@ -225,6 +233,7 @@ For production use, requires a license key in env var LANGGRAPH_CLOUD_LICENSE_KE
             debugger_port=debugger_port,
             debugger_base_url=debugger_base_url,
             postgres_uri=postgres_uri,
+            api_version=api_version,
             image=image,
             base_image=base_image,
         )
@@ -290,6 +299,7 @@ def _build(
     config: pathlib.Path,
     config_json: dict,
     base_image: Optional[str],
+    api_version: Optional[str],
     pull: bool,
     tag: str,
     passthrough: Sequence[str] = (),
@@ -300,7 +310,7 @@ def _build(
             subp_exec(
                 "docker",
                 "pull",
-                langgraph_cli.config.docker_tag(config_json, base_image),
+                langgraph_cli.config.docker_tag(config_json, base_image, api_version),
                 verbose=True,
             )
         )
@@ -314,7 +324,7 @@ def _build(
     ]
     # apply config
     stdin, additional_contexts = langgraph_cli.config.config_to_docker(
-        config, config_json, base_image
+        config, config_json, base_image, api_version
     )
     # add additional_contexts
     if additional_contexts:
@@ -355,6 +365,7 @@ def _build(
     "\n\n    \b\nExamples:\n    --base-image langchain/langgraph-server:0.2.18  # Pin to a specific patch version"
     "\n    --base-image langchain/langgraph-server:0.2  # Pin to a minor version (Python)",
 )
+@OPT_API_VERSION
 @click.argument("docker_build_args", nargs=-1, type=click.UNPROCESSED)
 @cli.command(
     help="📦 Build LangGraph API server Docker image.",
@@ -367,6 +378,7 @@ def build(
     config: pathlib.Path,
     docker_build_args: Sequence[str],
     base_image: Optional[str],
+    api_version: Optional[str],
     pull: bool,
     tag: str,
 ):
@@ -376,7 +388,15 @@ def build(
         config_json = langgraph_cli.config.validate_config_file(config)
         warn_non_wolfi_distro(config_json)
         _build(
-            runner, set, config, config_json, base_image, pull, tag, docker_build_args
+            runner,
+            set,
+            config,
+            config_json,
+            base_image,
+            api_version,
+            pull,
+            tag,
+            docker_build_args,
         )
 
 
@@ -456,12 +476,14 @@ tests
     "\n\n    \b\nExamples:\n    --base-image langchain/langgraph-server:0.2.18  # Pin to a specific patch version"
     "\n    --base-image langchain/langgraph-server:0.2  # Pin to a minor version (Python)",
 )
+@OPT_API_VERSION
 @log_command
 def dockerfile(
     save_path: str,
     config: pathlib.Path,
     add_docker_compose: bool,
     base_image: Optional[str] = None,
+    api_version: Optional[str] = None,
 ) -> None:
     save_path = pathlib.Path(save_path).absolute()
     secho(f"🔍 Validating configuration at path: {config}", fg="yellow")
@@ -474,6 +496,7 @@ def dockerfile(
         config,
         config_json,
         base_image=base_image,
+        api_version=api_version,
     )
     with open(str(save_path), "w", encoding="utf-8") as f:
         f.write(dockerfile)
@@ -739,6 +762,7 @@ def prepare_args_and_stdin(
     debugger_port: Optional[int] = None,
     debugger_base_url: Optional[str] = None,
     postgres_uri: Optional[str] = None,
+    api_version: Optional[str] = None,
     # Like "my-tag" (if you already built it locally)
     image: Optional[str] = None,
     # Like "langchain/langgraphjs-api" or "langchain/langgraph-api
@@ -754,6 +778,7 @@ def prepare_args_and_stdin(
         postgres_uri=postgres_uri,
         image=image,  # Pass image to compose YAML generator
         base_image=base_image,
+        api_version=api_version,
     )
     args = [
         "--project-directory",
@@ -769,6 +794,7 @@ def prepare_args_and_stdin(
         config,
         watch=watch,
         base_image=langgraph_cli.config.default_base_image(config),
+        api_version=api_version,
         image=image,
     )
     return args, stdin
@@ -787,6 +813,7 @@ def prepare(
     debugger_port: Optional[int] = None,
     debugger_base_url: Optional[str] = None,
     postgres_uri: Optional[str] = None,
+    api_version: Optional[str] = None,
     image: Optional[str] = None,
     base_image: Optional[str] = None,
 ) -> tuple[list[str], str]:
@@ -799,7 +826,7 @@ def prepare(
             subp_exec(
                 "docker",
                 "pull",
-                langgraph_cli.config.docker_tag(config_json, base_image),
+                langgraph_cli.config.docker_tag(config_json, base_image, api_version),
                 verbose=verbose,
             )
         )
@@ -814,6 +841,7 @@ def prepare(
         debugger_port=debugger_port,
         debugger_base_url=debugger_base_url or f"http://127.0.0.1:{port}",
         postgres_uri=postgres_uri,
+        api_version=api_version,
         image=image,
         base_image=base_image,
     )

libs/cli/langgraph_cli/config.py

@@ -1213,6 +1213,7 @@ def python_config_to_docker(
     config_path: pathlib.Path,
     config: Config,
     base_image: str,
+    api_version: Optional[str] = None,
 ) -> tuple[str, dict[str, str]]:
     """Generate a Dockerfile from the configuration."""
     pip_installer = config.get("pip_installer", "auto")
@@ -1360,7 +1361,7 @@ ADD {relpath} /deps/{name}
                 "# -- End of JS dependencies install --",
             ]
         )
-    image_str = docker_tag(config, base_image)
+    image_str = docker_tag(config, base_image, api_version)
     docker_file_contents = [
         f"FROM {image_str}",
         "",
@@ -1402,10 +1403,11 @@ def node_config_to_docker(
     config_path: pathlib.Path,
     config: Config,
     base_image: str,
+    api_version: Optional[str] = None,
 ) -> tuple[str, dict[str, str]]:
     faux_path = f"/deps/{config_path.parent.name}"
     install_cmd = _get_node_pm_install_cmd(config_path, config)
-    image_str = docker_tag(config, base_image)
+    image_str = docker_tag(config, base_image, api_version)
 
     env_vars: list[str] = []
 
@@ -1461,6 +1463,7 @@ def default_base_image(config: Config) -> str:
 def docker_tag(
     config: Config,
     base_image: Optional[str] = None,
+    api_version: Optional[str] = None,
 ) -> str:
     base_image = base_image or default_base_image(config)
 
@@ -1473,28 +1476,43 @@ def docker_tag(
     if "/langgraph-server" in base_image:
         return f"{base_image}-py{config['python_version']}"
 
+    # Build the standard tag format
+    language, version = None, None
     if config.get("node_version") and not config.get("python_version"):
-        return f"{base_image}:{config['node_version']}{distro_tag}"
-    return f"{base_image}:{config['python_version']}{distro_tag}"
+        language, version = "node", config["node_version"]
+    else:
+        language, version = "py", config["python_version"]
+
+    version_distro_tag = f"{version}{distro_tag}"
+
+    # Prepend API version if provided
+    if api_version:
+        full_tag = f"{api_version}-{language}{version_distro_tag}"
+    else:
+        full_tag = version_distro_tag
+
+    return f"{base_image}:{full_tag}"
 
 
 def config_to_docker(
     config_path: pathlib.Path,
     config: Config,
     base_image: Optional[str] = None,
+    api_version: Optional[str] = None,
 ) -> tuple[str, dict[str, str]]:
     base_image = base_image or default_base_image(config)
 
     if config.get("node_version") and not config.get("python_version"):
-        return node_config_to_docker(config_path, config, base_image)
+        return node_config_to_docker(config_path, config, base_image, api_version)
 
-    return python_config_to_docker(config_path, config, base_image)
+    return python_config_to_docker(config_path, config, base_image, api_version)
 
 
 def config_to_compose(
     config_path: pathlib.Path,
     config: Config,
     base_image: Optional[str] = None,
+    api_version: Optional[str] = None,
     image: Optional[str] = None,
     watch: bool = False,
 ) -> str:
@@ -1531,7 +1549,7 @@ def config_to_compose(
 
     else:
         dockerfile, additional_contexts = config_to_docker(
-            config_path, config, base_image
+            config_path, config, base_image, api_version
         )
 
         additional_contexts_str = "\n".join(

libs/cli/langgraph_cli/docker.py

@@ -147,6 +147,8 @@ def compose_as_dict(
     image: Optional[str] = None,
     # Base image to use for the LangGraph API server
     base_image: Optional[str] = None,
+    # API version of the base image
+    api_version: Optional[str] = None,
 ) -> dict:
     """Create a docker compose file as a dictionary in YML style."""
     if postgres_uri is None:
@@ -252,6 +254,7 @@ def compose(
     postgres_uri: Optional[str] = None,
     image: Optional[str] = None,
     base_image: Optional[str] = None,
+    api_version: Optional[str] = None,
 ) -> str:
     """Create a docker compose file as a string."""
     compose_content = compose_as_dict(
@@ -262,6 +265,7 @@ def compose(
         postgres_uri=postgres_uri,
         image=image,
         base_image=base_image,
+        api_version=api_version,
     )
     compose_str = dict_to_yaml(compose_content)
     return compose_str

libs/cli/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-cli"
-version = "0.3.4"
+version = "0.3.6"
 description = "CLI for interacting with LangGraph API"
 authors = []
 requires-python = ">=3.9"
@@ -19,7 +19,7 @@ dependencies = [
 [project.optional-dependencies]
 inmem = [
     "langgraph-api>=0.2.67,<0.3.0 ; python_version >= '3.11'",
-    "langgraph-runtime-inmem>=0.3.0,<0.4.0 ; python_version >= '3.11'",
+    "langgraph-runtime-inmem>=0.6.0 ; python_version >= '3.11'",
     "python-dotenv>=0.8.0",
 ]
 

libs/cli/tests/unit_tests/cli/test_cli.py

@@ -574,3 +574,248 @@ def test_build_generate_proper_build_context():
         assert len(build_contexts) == 2, (
             f"Expected 2 build contexts, but found {len(build_contexts)}"
         )
+
+
+def test_dockerfile_command_with_api_version() -> None:
+    """Test the 'dockerfile' command with --api-version flag."""
+    runner = CliRunner()
+    config_content = {
+        "python_version": "3.11",
+        "graphs": {"agent": "agent.py:graph"},
+        "dependencies": ["."],
+    }
+
+    with temporary_config_folder(config_content) as temp_dir:
+        save_path = temp_dir / "Dockerfile"
+        agent_path = temp_dir / "agent.py"
+        agent_path.touch()
+
+        result = runner.invoke(
+            cli,
+            [
+                "dockerfile",
+                str(save_path),
+                "--config",
+                str(temp_dir / "config.json"),
+                "--api-version",
+                "0.2.74",
+            ],
+        )
+
+        # Assert command was successful
+        assert result.exit_code == 0, result.output
+        assert "✅ Created: Dockerfile" in result.output
+
+        # Check if Dockerfile was created and contains correct FROM line
+        assert save_path.exists()
+        with open(save_path) as f:
+            dockerfile = f.read()
+            assert "FROM langchain/langgraph-api:0.2.74-py3.11" in dockerfile
+
+
+def test_dockerfile_command_with_api_version_and_base_image() -> None:
+    """Test the 'dockerfile' command with both --api-version and --base-image flags."""
+    runner = CliRunner()
+    config_content = {
+        "python_version": "3.12",
+        "graphs": {"agent": "agent.py:graph"},
+        "dependencies": ["."],
+        "image_distro": "wolfi",
+    }
+
+    with temporary_config_folder(config_content) as temp_dir:
+        save_path = temp_dir / "Dockerfile"
+        agent_path = temp_dir / "agent.py"
+        agent_path.touch()
+
+        result = runner.invoke(
+            cli,
+            [
+                "dockerfile",
+                str(save_path),
+                "--config",
+                str(temp_dir / "config.json"),
+                "--api-version",
+                "1.0.0",
+                "--base-image",
+                "my-registry/custom-api",
+            ],
+        )
+
+        # Assert command was successful
+        assert result.exit_code == 0, result.output
+        assert "✅ Created: Dockerfile" in result.output
+
+        # Check if Dockerfile was created and contains correct FROM line
+        assert save_path.exists()
+        with open(save_path) as f:
+            dockerfile = f.read()
+            assert "FROM my-registry/custom-api:1.0.0-py3.12-wolfi" in dockerfile
+
+
+def test_dockerfile_command_with_api_version_nodejs() -> None:
+    """Test the 'dockerfile' command with --api-version flag for Node.js config."""
+    runner = CliRunner()
+    config_content = {
+        "node_version": "20",
+        "graphs": {"agent": "agent.js:graph"},
+    }
+
+    with temporary_config_folder(config_content) as temp_dir:
+        save_path = temp_dir / "Dockerfile"
+        agent_path = temp_dir / "agent.js"
+        agent_path.touch()
+
+        result = runner.invoke(
+            cli,
+            [
+                "dockerfile",
+                str(save_path),
+                "--config",
+                str(temp_dir / "config.json"),
+                "--api-version",
+                "0.2.74",
+            ],
+        )
+
+        # Assert command was successful
+        assert result.exit_code == 0, result.output
+        assert "✅ Created: Dockerfile" in result.output
+
+        # Check if Dockerfile was created and contains correct FROM line
+        assert save_path.exists()
+        with open(save_path) as f:
+            dockerfile = f.read()
+            assert "FROM langchain/langgraphjs-api:0.2.74-node20" in dockerfile
+
+
+def test_build_command_with_api_version() -> None:
+    """Test the 'build' command with --api-version flag."""
+    runner = CliRunner()
+    config_content = {
+        "python_version": "3.11",
+        "graphs": {"agent": "agent.py:graph"},
+        "dependencies": ["."],
+        "image_distro": "wolfi",  # Use wolfi to avoid warning messages
+    }
+
+    with temporary_config_folder(config_content) as temp_dir:
+        agent_path = temp_dir / "agent.py"
+        agent_path.touch()
+
+        # Mock docker command since we don't want to actually build
+        with runner.isolated_filesystem():
+            result = runner.invoke(
+                cli,
+                [
+                    "build",
+                    "--tag",
+                    "test-image",
+                    "--config",
+                    str(temp_dir / "config.json"),
+                    "--api-version",
+                    "0.2.74",
+                    "--no-pull",  # Avoid pulling non-existent images
+                ],
+                catch_exceptions=True,
+            )
+
+        # Check that the build command is called with the correct tag
+        # The output should contain the docker build command with the api_version tag
+        assert "langchain/langgraph-api:0.2.74-py3.11-wolfi" in result.output
+
+
+def test_build_command_with_api_version_and_base_image() -> None:
+    """Test the 'build' command with both --api-version and --base-image flags."""
+    runner = CliRunner()
+    config_content = {
+        "python_version": "3.12",
+        "graphs": {"agent": "agent.py:graph"},
+        "dependencies": ["."],
+        "image_distro": "wolfi",  # Use wolfi to avoid warning messages
+    }
+
+    with temporary_config_folder(config_content) as temp_dir:
+        agent_path = temp_dir / "agent.py"
+        agent_path.touch()
+
+        # Mock docker command since we don't want to actually build
+        with runner.isolated_filesystem():
+            result = runner.invoke(
+                cli,
+                [
+                    "build",
+                    "--tag",
+                    "test-image",
+                    "--config",
+                    str(temp_dir / "config.json"),
+                    "--api-version",
+                    "1.0.0",
+                    "--base-image",
+                    "my-registry/custom-api",
+                    "--no-pull",  # Avoid pulling non-existent images
+                ],
+                catch_exceptions=True,
+            )
+
+        # Check that the build command includes the api_version
+        assert "my-registry/custom-api:1.0.0-py3.12-wolfi" in result.output
+
+
+def test_prepare_args_and_stdin_with_api_version() -> None:
+    """Test prepare_args_and_stdin function with api_version parameter."""
+    config_path = pathlib.Path(__file__).parent / "langgraph.json"
+    config = validate_config(
+        Config(dependencies=["."], graphs={"agent": "agent.py:graph"})
+    )
+    port = 8000
+    api_version = "0.2.74"
+
+    actual_args, actual_stdin = prepare_args_and_stdin(
+        capabilities=DEFAULT_DOCKER_CAPABILITIES,
+        config_path=config_path,
+        config=config,
+        docker_compose=None,
+        port=port,
+        watch=False,
+        api_version=api_version,
+    )
+
+    expected_args = [
+        "--project-directory",
+        str(pathlib.Path(__file__).parent.absolute()),
+        "-f",
+        "-",
+    ]
+
+    # Check that the args are correct
+    assert actual_args == expected_args
+
+    # Check that the stdin contains the correct FROM line with api_version
+    assert "FROM langchain/langgraph-api:0.2.74-py3.11" in actual_stdin
+
+
+def test_prepare_args_and_stdin_with_api_version_and_image() -> None:
+    """Test prepare_args_and_stdin function with both api_version and image parameters."""
+    config_path = pathlib.Path(__file__).parent / "langgraph.json"
+    config = validate_config(
+        Config(dependencies=["."], graphs={"agent": "agent.py:graph"})
+    )
+    port = 8000
+    api_version = "0.2.74"
+    image = "my-custom-image:latest"
+
+    actual_args, actual_stdin = prepare_args_and_stdin(
+        capabilities=DEFAULT_DOCKER_CAPABILITIES,
+        config_path=config_path,
+        config=config,
+        docker_compose=None,
+        port=port,
+        watch=False,
+        api_version=api_version,
+        image=image,
+    )
+
+    # When image is provided, api_version should be ignored for the image
+    # but the stdin should not contain a build section (since image is provided)
+    assert "pull_policy: build" not in actual_stdin

libs/cli/tests/unit_tests/test_config.py

@@ -1337,3 +1337,195 @@ def test_docker_tag_different_node_versions_with_distro():
         )
         tag = docker_tag(config)
         assert tag == expected_tag, f"Failed for Node.js {node_version}"
+
+
+def test_docker_tag_with_api_version():
+    """Test docker_tag function with api_version parameter."""
+
+    # Test 1: Python config with api_version and default distro
+    config = validate_config(
+        {
+            "python_version": "3.11",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+        }
+    )
+    tag = docker_tag(config, api_version="0.2.74")
+    assert tag == "langchain/langgraph-api:0.2.74-py3.11"
+
+    # Test 2: Python config with api_version and wolfi distro
+    config = validate_config(
+        {
+            "python_version": "3.12",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+            "image_distro": "wolfi",
+        }
+    )
+    tag = docker_tag(config, api_version="0.2.74")
+    assert tag == "langchain/langgraph-api:0.2.74-py3.12-wolfi"
+
+    # Test 3: Node.js config with api_version and default distro
+    config = validate_config(
+        {
+            "node_version": "20",
+            "graphs": {"agent": "./agent.js:graph"},
+        }
+    )
+    tag = docker_tag(config, api_version="0.2.74")
+    assert tag == "langchain/langgraphjs-api:0.2.74-node20"
+
+    # Test 4: Node.js config with api_version and wolfi distro
+    config = validate_config(
+        {
+            "node_version": "20",
+            "graphs": {"agent": "./agent.js:graph"},
+            "image_distro": "wolfi",
+        }
+    )
+    tag = docker_tag(config, api_version="0.2.74")
+    assert tag == "langchain/langgraphjs-api:0.2.74-node20-wolfi"
+
+    # Test 5: Custom base image with api_version
+    config = validate_config(
+        {
+            "python_version": "3.11",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+            "base_image": "my-registry/custom-image",
+        }
+    )
+    tag = docker_tag(config, base_image="my-registry/custom-image", api_version="1.0.0")
+    assert tag == "my-registry/custom-image:1.0.0-py3.11"
+
+    # Test 6: api_version with different Python versions
+    for python_version in ["3.11", "3.12", "3.13"]:
+        config = validate_config(
+            {
+                "python_version": python_version,
+                "dependencies": ["."],
+                "graphs": {"agent": "./agent.py:graph"},
+            }
+        )
+        tag = docker_tag(config, api_version="0.2.74")
+        assert tag == f"langchain/langgraph-api:0.2.74-py{python_version}"
+
+    # Test 7: Without api_version should work as before
+    config = validate_config(
+        {
+            "python_version": "3.11",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+        }
+    )
+    tag = docker_tag(config)
+    assert tag == "langchain/langgraph-api:3.11"
+
+    # Test 8: api_version with multiplatform config (should default to Python)
+    config = validate_config(
+        {
+            "python_version": "3.11",
+            "node_version": "20",
+            "dependencies": ["."],
+            "graphs": {"python": "./agent.py:graph", "js": "./agent.js:graph"},
+        }
+    )
+    tag = docker_tag(config, api_version="0.2.74")
+    assert tag == "langchain/langgraph-api:0.2.74-py3.11"
+
+    # Test 9: api_version with _INTERNAL_docker_tag should ignore api_version
+    config = validate_config(
+        {
+            "python_version": "3.11",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+            "_INTERNAL_docker_tag": "internal-tag",
+        }
+    )
+    tag = docker_tag(config, api_version="0.2.74")
+    assert tag == "langchain/langgraph-api:internal-tag"
+
+    # Test 10: api_version with langgraph-server base image should follow special format
+    config = validate_config(
+        {
+            "python_version": "3.11",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+        }
+    )
+    tag = docker_tag(
+        config, base_image="langchain/langgraph-server:0.2", api_version="0.2.74"
+    )
+    assert tag == "langchain/langgraph-server:0.2-py3.11"
+
+
+def test_config_to_docker_with_api_version():
+    """Test config_to_docker function with api_version parameter."""
+
+    # Test Python config with api_version
+    graphs = {"agent": "./agent.py:graph"}
+    actual_docker_stdin, additional_contexts = config_to_docker(
+        PATH_TO_CONFIG,
+        validate_config({"dependencies": ["."], "graphs": graphs}),
+        "langchain/langgraph-api",
+        api_version="0.2.74",
+    )
+
+    # Check that the FROM line uses the api_version
+    lines = actual_docker_stdin.split("\n")
+    from_line = lines[0]
+    assert from_line == "FROM langchain/langgraph-api:0.2.74-py3.11"
+
+    # Test Node.js config with api_version
+    graphs = {"agent": "./agent.js:graph"}
+    actual_docker_stdin, additional_contexts = config_to_docker(
+        PATH_TO_CONFIG,
+        validate_config({"node_version": "20", "graphs": graphs}),
+        "langchain/langgraphjs-api",
+        api_version="0.2.74",
+    )
+
+    # Check that the FROM line uses the api_version
+    lines = actual_docker_stdin.split("\n")
+    from_line = lines[0]
+    assert from_line == "FROM langchain/langgraphjs-api:0.2.74-node20"
+
+
+def test_config_to_compose_with_api_version():
+    """Test config_to_compose function with api_version parameter."""
+
+    # Test Python config with api_version
+    config = validate_config(
+        {
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+        }
+    )
+
+    actual_compose_str = config_to_compose(
+        PATH_TO_CONFIG,
+        config,
+        "langchain/langgraph-api",
+        api_version="0.2.74",
+    )
+
+    # Check that the compose file includes the correct FROM line with api_version
+    assert "FROM langchain/langgraph-api:0.2.74-py3.11" in actual_compose_str
+
+    # Test Node.js config with api_version
+    config = validate_config(
+        {
+            "node_version": "20",
+            "graphs": {"agent": "./agent.js:graph"},
+        }
+    )
+
+    actual_compose_str = config_to_compose(
+        PATH_TO_CONFIG,
+        config,
+        "langchain/langgraphjs-api",
+        api_version="0.2.74",
+    )
+
+    # Check that the compose file includes the correct FROM line with api_version
+    assert "FROM langchain/langgraphjs-api:0.2.74-node20" in actual_compose_str

libs/cli/tests/unit_tests/test_docker.py

@@ -146,3 +146,220 @@ services:
             REDIS_URI: redis://langgraph-redis:6379
             POSTGRES_URI: {DEFAULT_POSTGRES_URI}"""
     assert clean_empty_lines(actual_compose_str) == expected_compose_str
+
+
+def test_compose_with_api_version():
+    """Test compose function with api_version parameter."""
+    port = 8123
+    api_version = "0.2.74"
+
+    actual_compose_str = compose(
+        DEFAULT_DOCKER_CAPABILITIES, port=port, api_version=api_version
+    )
+
+    # The compose function should generate a compose file that doesn't directly
+    # reference the api_version, since it's handled in the docker tag creation
+    # when building the image. The compose function mainly sets up services.
+    expected_compose_str = f"""volumes:
+    langgraph-data:
+        driver: local
+services:
+    langgraph-redis:
+        image: redis:6
+        healthcheck:
+            test: redis-cli ping
+            interval: 5s
+            timeout: 1s
+            retries: 5
+    langgraph-postgres:
+        image: pgvector/pgvector:pg16
+        ports:
+            - "5433:5432"
+        environment:
+            POSTGRES_DB: postgres
+            POSTGRES_USER: postgres
+            POSTGRES_PASSWORD: postgres
+        command:
+            - postgres
+            - -c
+            - shared_preload_libraries=vector
+        volumes:
+            - langgraph-data:/var/lib/postgresql/data
+        healthcheck:
+            test: pg_isready -U postgres
+            start_period: 10s
+            timeout: 1s
+            retries: 5
+            interval: 5s
+    langgraph-api:
+        ports:
+            - "{port}:8000"
+        depends_on:
+            langgraph-redis:
+                condition: service_healthy
+            langgraph-postgres:
+                condition: service_healthy
+        environment:
+            REDIS_URI: redis://langgraph-redis:6379
+            POSTGRES_URI: {DEFAULT_POSTGRES_URI}"""
+    assert clean_empty_lines(actual_compose_str) == expected_compose_str
+
+
+def test_compose_with_api_version_and_base_image():
+    """Test compose function with both api_version and base_image parameters."""
+    port = 8123
+    api_version = "1.0.0"
+    base_image = "my-registry/custom-api"
+
+    actual_compose_str = compose(
+        DEFAULT_DOCKER_CAPABILITIES,
+        port=port,
+        api_version=api_version,
+        base_image=base_image,
+    )
+
+    # Similar to the previous test - the compose function doesn't directly embed
+    # the api_version or base_image into the compose file since those are handled
+    # during the docker build process
+    expected_compose_str = f"""volumes:
+    langgraph-data:
+        driver: local
+services:
+    langgraph-redis:
+        image: redis:6
+        healthcheck:
+            test: redis-cli ping
+            interval: 5s
+            timeout: 1s
+            retries: 5
+    langgraph-postgres:
+        image: pgvector/pgvector:pg16
+        ports:
+            - "5433:5432"
+        environment:
+            POSTGRES_DB: postgres
+            POSTGRES_USER: postgres
+            POSTGRES_PASSWORD: postgres
+        command:
+            - postgres
+            - -c
+            - shared_preload_libraries=vector
+        volumes:
+            - langgraph-data:/var/lib/postgresql/data
+        healthcheck:
+            test: pg_isready -U postgres
+            start_period: 10s
+            timeout: 1s
+            retries: 5
+            interval: 5s
+    langgraph-api:
+        ports:
+            - "{port}:8000"
+        depends_on:
+            langgraph-redis:
+                condition: service_healthy
+            langgraph-postgres:
+                condition: service_healthy
+        environment:
+            REDIS_URI: redis://langgraph-redis:6379
+            POSTGRES_URI: {DEFAULT_POSTGRES_URI}"""
+    assert clean_empty_lines(actual_compose_str) == expected_compose_str
+
+
+def test_compose_with_api_version_and_custom_postgres():
+    """Test compose function with api_version and custom postgres URI."""
+    port = 8123
+    api_version = "0.2.74"
+    custom_postgres_uri = "postgresql://user:pass@external-db:5432/mydb"
+
+    actual_compose_str = compose(
+        DEFAULT_DOCKER_CAPABILITIES,
+        port=port,
+        api_version=api_version,
+        postgres_uri=custom_postgres_uri,
+    )
+
+    expected_compose_str = f"""services:
+    langgraph-redis:
+        image: redis:6
+        healthcheck:
+            test: redis-cli ping
+            interval: 5s
+            timeout: 1s
+            retries: 5
+    langgraph-api:
+        ports:
+            - "{port}:8000"
+        depends_on:
+            langgraph-redis:
+                condition: service_healthy
+        environment:
+            REDIS_URI: redis://langgraph-redis:6379
+            POSTGRES_URI: {custom_postgres_uri}"""
+    assert clean_empty_lines(actual_compose_str) == expected_compose_str
+
+
+def test_compose_with_api_version_and_debugger():
+    """Test compose function with api_version and debugger port."""
+    port = 8123
+    debugger_port = 8001
+    api_version = "0.2.74"
+
+    actual_compose_str = compose(
+        DEFAULT_DOCKER_CAPABILITIES,
+        port=port,
+        api_version=api_version,
+        debugger_port=debugger_port,
+    )
+
+    expected_compose_str = f"""volumes:
+    langgraph-data:
+        driver: local
+services:
+    langgraph-redis:
+        image: redis:6
+        healthcheck:
+            test: redis-cli ping
+            interval: 5s
+            timeout: 1s
+            retries: 5
+    langgraph-postgres:
+        image: pgvector/pgvector:pg16
+        ports:
+            - "5433:5432"
+        environment:
+            POSTGRES_DB: postgres
+            POSTGRES_USER: postgres
+            POSTGRES_PASSWORD: postgres
+        command:
+            - postgres
+            - -c
+            - shared_preload_libraries=vector
+        volumes:
+            - langgraph-data:/var/lib/postgresql/data
+        healthcheck:
+            test: pg_isready -U postgres
+            start_period: 10s
+            timeout: 1s
+            retries: 5
+            interval: 5s
+    langgraph-debugger:
+        image: langchain/langgraph-debugger
+        restart: on-failure
+        depends_on:
+            langgraph-postgres:
+                condition: service_healthy
+        ports:
+            - "{debugger_port}:3968"
+    langgraph-api:
+        ports:
+            - "{port}:8000"
+        depends_on:
+            langgraph-redis:
+                condition: service_healthy
+            langgraph-postgres:
+                condition: service_healthy
+        environment:
+            REDIS_URI: redis://langgraph-redis:6379
+            POSTGRES_URI: {DEFAULT_POSTGRES_URI}"""
+    assert clean_empty_lines(actual_compose_str) == expected_compose_str

libs/cli/uv.lock

@@ -30,25 +30,34 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916, upload-time = "2025-03-17T00:02:52.713Z" },
 ]
 
+[[package]]
+name = "backports-asyncio-runner"
+version = "1.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8e/ff/70dca7d7cb1cbc0edb2c6cc0c38b65cba36cccc491eca64cabd5fe7f8670/backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162", size = 69893, upload-time = "2025-07-02T02:27:15.685Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a0/59/76ab57e3fe74484f48a53f8e337171b4a2349e506eabe136d7e01d059086/backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5", size = 12313, upload-time = "2025-07-02T02:27:14.263Z" },
+]
+
 [[package]]
 name = "blockbuster"
-version = "1.5.24"
+version = "1.5.25"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "forbiddenfruit", marker = "python_full_version >= '3.11' and implementation_name == 'cpython'" },
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 ]
 
 [[package]]

libs/langgraph/bench/__main__.py

@@ -11,7 +11,7 @@ from bench.react_agent import react_agent
 from bench.sequential import create_sequential
 from bench.wide_dict import wide_dict
 from bench.wide_state import wide_state
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph import StateGraph
 from langgraph.pregel import Pregel
 
@@ -26,7 +26,7 @@ async def arun(graph: Pregel, input: dict):
                     "configurable": {"thread_id": str(uuid4())},
                     "recursion_limit": 1000000000,
                 },
-                checkpoint_during=False,
+                durability="exit",
             )
         ]
     )
@@ -43,7 +43,7 @@ async def arun_first_event_latency(graph: Pregel, input: dict) -> None:
             "configurable": {"thread_id": str(uuid4())},
             "recursion_limit": 1000000000,
         },
-        checkpoint_during=False,
+        durability="exit",
     )
 
     try:
@@ -63,7 +63,7 @@ def run(graph: Pregel, input: dict):
                     "configurable": {"thread_id": str(uuid4())},
                     "recursion_limit": 1000000000,
                 },
-                checkpoint_during=False,
+                durability="exit",
             )
         ]
     )
@@ -80,7 +80,7 @@ def run_first_event_latency(graph: Pregel, input: dict) -> None:
             "configurable": {"thread_id": str(uuid4())},
             "recursion_limit": 1000000000,
         },
-        checkpoint_during=False,
+        durability="exit",
     )
 
     try:
@@ -108,8 +108,8 @@ benchmarks = (
     ),
     (
         "fanout_to_subgraph_10x_checkpoint",
-        fanout_to_subgraph().compile(checkpointer=MemorySaver()),
-        fanout_to_subgraph_sync().compile(checkpointer=MemorySaver()),
+        fanout_to_subgraph().compile(checkpointer=InMemorySaver()),
+        fanout_to_subgraph_sync().compile(checkpointer=InMemorySaver()),
         {
             "subjects": [
                 random.choices("abcdefghijklmnopqrstuvwxyz", k=1000) for _ in range(10)
@@ -128,8 +128,8 @@ benchmarks = (
     ),
     (
         "fanout_to_subgraph_100x_checkpoint",
-        fanout_to_subgraph().compile(checkpointer=MemorySaver()),
-        fanout_to_subgraph_sync().compile(checkpointer=MemorySaver()),
+        fanout_to_subgraph().compile(checkpointer=InMemorySaver()),
+        fanout_to_subgraph_sync().compile(checkpointer=InMemorySaver()),
         {
             "subjects": [
                 random.choices("abcdefghijklmnopqrstuvwxyz", k=1000) for _ in range(100)
@@ -144,8 +144,8 @@ benchmarks = (
     ),
     (
         "react_agent_10x_checkpoint",
-        react_agent(10, checkpointer=MemorySaver()),
-        react_agent(10, checkpointer=MemorySaver()),
+        react_agent(10, checkpointer=InMemorySaver()),
+        react_agent(10, checkpointer=InMemorySaver()),
         {"messages": [HumanMessage("hi?")]},
     ),
     (
@@ -156,8 +156,8 @@ benchmarks = (
     ),
     (
         "react_agent_100x_checkpoint",
-        react_agent(100, checkpointer=MemorySaver()),
-        react_agent(100, checkpointer=MemorySaver()),
+        react_agent(100, checkpointer=InMemorySaver()),
+        react_agent(100, checkpointer=InMemorySaver()),
         {"messages": [HumanMessage("hi?")]},
     ),
     (
@@ -178,8 +178,8 @@ benchmarks = (
     ),
     (
         "wide_state_25x300_checkpoint",
-        wide_state(300).compile(checkpointer=MemorySaver()),
-        wide_state(300).compile(checkpointer=MemorySaver()),
+        wide_state(300).compile(checkpointer=InMemorySaver()),
+        wide_state(300).compile(checkpointer=InMemorySaver()),
         {
             "messages": [
                 {
@@ -210,8 +210,8 @@ benchmarks = (
     ),
     (
         "wide_state_15x600_checkpoint",
-        wide_state(600).compile(checkpointer=MemorySaver()),
-        wide_state(600).compile(checkpointer=MemorySaver()),
+        wide_state(600).compile(checkpointer=InMemorySaver()),
+        wide_state(600).compile(checkpointer=InMemorySaver()),
         {
             "messages": [
                 {
@@ -242,8 +242,8 @@ benchmarks = (
     ),
     (
         "wide_state_9x1200_checkpoint",
-        wide_state(1200).compile(checkpointer=MemorySaver()),
-        wide_state(1200).compile(checkpointer=MemorySaver()),
+        wide_state(1200).compile(checkpointer=InMemorySaver()),
+        wide_state(1200).compile(checkpointer=InMemorySaver()),
         {
             "messages": [
                 {
@@ -274,8 +274,8 @@ benchmarks = (
     ),
     (
         "wide_dict_25x300_checkpoint",
-        wide_dict(300).compile(checkpointer=MemorySaver()),
-        wide_dict(300).compile(checkpointer=MemorySaver()),
+        wide_dict(300).compile(checkpointer=InMemorySaver()),
+        wide_dict(300).compile(checkpointer=InMemorySaver()),
         {
             "messages": [
                 {
@@ -306,8 +306,8 @@ benchmarks = (
     ),
     (
         "wide_dict_15x600_checkpoint",
-        wide_dict(600).compile(checkpointer=MemorySaver()),
-        wide_dict(600).compile(checkpointer=MemorySaver()),
+        wide_dict(600).compile(checkpointer=InMemorySaver()),
+        wide_dict(600).compile(checkpointer=InMemorySaver()),
         {
             "messages": [
                 {
@@ -338,8 +338,8 @@ benchmarks = (
     ),
     (
         "wide_dict_9x1200_checkpoint",
-        wide_dict(1200).compile(checkpointer=MemorySaver()),
-        wide_dict(1200).compile(checkpointer=MemorySaver()),
+        wide_dict(1200).compile(checkpointer=InMemorySaver()),
+        wide_dict(1200).compile(checkpointer=InMemorySaver()),
         {
             "messages": [
                 {
@@ -382,8 +382,8 @@ benchmarks = (
     ),
     (
         "pydantic_state_25x300_checkpoint",
-        pydantic_state(300).compile(checkpointer=MemorySaver()),
-        pydantic_state(300).compile(checkpointer=MemorySaver()),
+        pydantic_state(300).compile(checkpointer=InMemorySaver()),
+        pydantic_state(300).compile(checkpointer=InMemorySaver()),
         {
             "messages": [
                 {
@@ -414,8 +414,8 @@ benchmarks = (
     ),
     (
         "pydantic_state_15x600_checkpoint",
-        pydantic_state(600).compile(checkpointer=MemorySaver()),
-        pydantic_state(600).compile(checkpointer=MemorySaver()),
+        pydantic_state(600).compile(checkpointer=InMemorySaver()),
+        pydantic_state(600).compile(checkpointer=InMemorySaver()),
         {
             "messages": [
                 {
@@ -446,8 +446,8 @@ benchmarks = (
     ),
     (
         "pydantic_state_9x1200_checkpoint",
-        pydantic_state(1200).compile(checkpointer=MemorySaver()),
-        pydantic_state(1200).compile(checkpointer=MemorySaver()),
+        pydantic_state(1200).compile(checkpointer=InMemorySaver()),
+        pydantic_state(1200).compile(checkpointer=InMemorySaver()),
         {
             "messages": [
                 {

libs/langgraph/bench/fanout_to_subgraph.py

@@ -3,8 +3,9 @@ from typing import Annotated
 
 from typing_extensions import TypedDict
 
-from langgraph.constants import END, START, Send
+from langgraph.constants import END, START
 from langgraph.graph.state import StateGraph
+from langgraph.types import Send
 
 
 def fanout_to_subgraph() -> StateGraph:
@@ -114,9 +115,9 @@ if __name__ == "__main__":
 
     import uvloop
 
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
-    graph = fanout_to_subgraph().compile(checkpointer=MemorySaver())
+    graph = fanout_to_subgraph().compile(checkpointer=InMemorySaver())
     input = {
         "subjects": [
             random.choices("abcdefghijklmnopqrstuvwxyz", k=1000) for _ in range(1000)

libs/langgraph/bench/pydantic_state.py

@@ -304,9 +304,9 @@ if __name__ == "__main__":
 
     import uvloop
 
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
-    graph = pydantic_state(1000).compile(checkpointer=MemorySaver())
+    graph = pydantic_state(1000).compile(checkpointer=InMemorySaver())
     input = {
         "messages": [
             {

libs/langgraph/bench/react_agent.py

@@ -68,9 +68,9 @@ if __name__ == "__main__":
 
     import uvloop
 
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
-    graph = react_agent(100, checkpointer=MemorySaver())
+    graph = react_agent(100, checkpointer=InMemorySaver())
     input = {"messages": [HumanMessage("hi?")]}
     config = {"configurable": {"thread_id": "1"}, "recursion_limit": 20000000000}
 

libs/langgraph/bench/sequential.py

@@ -1,7 +1,7 @@
 """Create a sequential no-op graph consisting of a few hundred nodes."""
 
+from langgraph._internal._runnable import RunnableCallable
 from langgraph.graph import MessagesState, StateGraph
-from langgraph.utils.runnable import RunnableCallable
 
 
 def create_sequential(number_nodes: int) -> StateGraph:

libs/langgraph/bench/wide_dict.py

@@ -130,9 +130,9 @@ if __name__ == "__main__":
 
     import uvloop
 
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
-    graph = wide_dict(1000).compile(checkpointer=MemorySaver())
+    graph = wide_dict(1000).compile(checkpointer=InMemorySaver())
     input = {
         "messages": [
             {

libs/langgraph/bench/wide_state.py

@@ -140,9 +140,9 @@ if __name__ == "__main__":
 
     import uvloop
 
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
 
-    graph = wide_state(1000).compile(checkpointer=MemorySaver())
+    graph = wide_state(1000).compile(checkpointer=InMemorySaver())
     input = {
         "messages": [
             {

libs/langgraph/langgraph/_internal/__init__.py

@@ -0,0 +1,4 @@
+"""Internal modules for LangGraph.
+
+This module is not part of the public API, and thus stability is not guaranteed.
+"""

libs/langgraph/langgraph/_internal/_config.py

@@ -0,0 +1,322 @@
+from __future__ import annotations
+
+from collections import ChainMap
+from collections.abc import Sequence
+from os import getenv
+from typing import Any, cast
+
+from langchain_core.callbacks import (
+    AsyncCallbackManager,
+    BaseCallbackManager,
+    CallbackManager,
+    Callbacks,
+)
+from langchain_core.runnables import RunnableConfig
+from langchain_core.runnables.config import (
+    CONFIG_KEYS,
+    COPIABLE_KEYS,
+    var_child_runnable_config,
+)
+
+from langgraph._internal._constants import (
+    CONF,
+    CONFIG_KEY_CHECKPOINT_ID,
+    CONFIG_KEY_CHECKPOINT_MAP,
+    CONFIG_KEY_CHECKPOINT_NS,
+    NS_END,
+    NS_SEP,
+)
+from langgraph.checkpoint.base import CheckpointMetadata
+
+DEFAULT_RECURSION_LIMIT = int(getenv("LANGGRAPH_DEFAULT_RECURSION_LIMIT", "25"))
+
+
+def recast_checkpoint_ns(ns: str) -> str:
+    """Remove task IDs from checkpoint namespace.
+
+    Args:
+        ns: The checkpoint namespace with task IDs.
+
+    Returns:
+        str: The checkpoint namespace without task IDs.
+    """
+    return NS_SEP.join(
+        part.split(NS_END)[0] for part in ns.split(NS_SEP) if not part.isdigit()
+    )
+
+
+def patch_configurable(
+    config: RunnableConfig | None, patch: dict[str, Any]
+) -> RunnableConfig:
+    if config is None:
+        return {CONF: patch}
+    elif CONF not in config:
+        return {**config, CONF: patch}
+    else:
+        return {**config, CONF: {**config[CONF], **patch}}
+
+
+def patch_checkpoint_map(
+    config: RunnableConfig | None, metadata: CheckpointMetadata | None
+) -> RunnableConfig:
+    if config is None:
+        return config
+    elif parents := (metadata.get("parents") if metadata else None):
+        conf = config[CONF]
+        return patch_configurable(
+            config,
+            {
+                CONFIG_KEY_CHECKPOINT_MAP: {
+                    **parents,
+                    conf[CONFIG_KEY_CHECKPOINT_NS]: conf[CONFIG_KEY_CHECKPOINT_ID],
+                },
+            },
+        )
+    else:
+        return config
+
+
+def merge_configs(*configs: RunnableConfig | None) -> RunnableConfig:
+    """Merge multiple configs into one.
+
+    Args:
+        *configs: The configs to merge.
+
+    Returns:
+        RunnableConfig: The merged config.
+    """
+    base: RunnableConfig = {}
+    # Even though the keys aren't literals, this is correct
+    # because both dicts are the same type
+    for config in configs:
+        if config is None:
+            continue
+        for key, value in config.items():
+            if not value:
+                continue
+            if key == "metadata":
+                if base_value := base.get(key):
+                    base[key] = {**base_value, **value}  # type: ignore
+                else:
+                    base[key] = value  # type: ignore[literal-required]
+            elif key == "tags":
+                if base_value := base.get(key):
+                    base[key] = [*base_value, *value]  # type: ignore
+                else:
+                    base[key] = value  # type: ignore[literal-required]
+            elif key == CONF:
+                if base_value := base.get(key):
+                    base[key] = {**base_value, **value}  # type: ignore[dict-item]
+                else:
+                    base[key] = value
+            elif key == "callbacks":
+                base_callbacks = base.get("callbacks")
+                # callbacks can be either None, list[handler] or manager
+                # so merging two callbacks values has 6 cases
+                if isinstance(value, list):
+                    if base_callbacks is None:
+                        base["callbacks"] = value.copy()
+                    elif isinstance(base_callbacks, list):
+                        base["callbacks"] = base_callbacks + value
+                    else:
+                        # base_callbacks is a manager
+                        mngr = base_callbacks.copy()
+                        for callback in value:
+                            mngr.add_handler(callback, inherit=True)
+                        base["callbacks"] = mngr
+                elif isinstance(value, BaseCallbackManager):
+                    # value is a manager
+                    if base_callbacks is None:
+                        base["callbacks"] = value.copy()
+                    elif isinstance(base_callbacks, list):
+                        mngr = value.copy()
+                        for callback in base_callbacks:
+                            mngr.add_handler(callback, inherit=True)
+                        base["callbacks"] = mngr
+                    else:
+                        # base_callbacks is also a manager
+                        base["callbacks"] = base_callbacks.merge(value)
+                else:
+                    raise NotImplementedError
+            elif key == "recursion_limit":
+                if config["recursion_limit"] != DEFAULT_RECURSION_LIMIT:
+                    base["recursion_limit"] = config["recursion_limit"]
+            else:
+                base[key] = config[key]  # type: ignore[literal-required]
+    if CONF not in base:
+        base[CONF] = {}
+    return base
+
+
+def patch_config(
+    config: RunnableConfig | None,
+    *,
+    callbacks: Callbacks = None,
+    recursion_limit: int | None = None,
+    max_concurrency: int | None = None,
+    run_name: str | None = None,
+    configurable: dict[str, Any] | None = None,
+) -> RunnableConfig:
+    """Patch a config with new values.
+
+    Args:
+        config: The config to patch.
+        callbacks: The callbacks to set.
+          Defaults to None.
+        recursion_limit: The recursion limit to set.
+          Defaults to None.
+        max_concurrency: The max concurrency to set.
+          Defaults to None.
+        run_name: The run name to set. Defaults to None.
+        configurable: The configurable to set.
+          Defaults to None.
+
+    Returns:
+        RunnableConfig: The patched config.
+    """
+    config = config.copy() if config is not None else {}
+    if callbacks is not None:
+        # If we're replacing callbacks, we need to unset run_name
+        # As that should apply only to the same run as the original callbacks
+        config["callbacks"] = callbacks
+        if "run_name" in config:
+            del config["run_name"]
+        if "run_id" in config:
+            del config["run_id"]
+    if recursion_limit is not None:
+        config["recursion_limit"] = recursion_limit
+    if max_concurrency is not None:
+        config["max_concurrency"] = max_concurrency
+    if run_name is not None:
+        config["run_name"] = run_name
+    if configurable is not None:
+        config[CONF] = {**config.get(CONF, {}), **configurable}
+    return config
+
+
+def get_callback_manager_for_config(
+    config: RunnableConfig, tags: Sequence[str] | None = None
+) -> CallbackManager:
+    """Get a callback manager for a config.
+
+    Args:
+        config: The config.
+
+    Returns:
+        CallbackManager: The callback manager.
+    """
+    from langchain_core.callbacks.manager import CallbackManager
+
+    # merge tags
+    all_tags = config.get("tags")
+    if all_tags is not None and tags is not None:
+        all_tags = [*all_tags, *tags]
+    elif tags is not None:
+        all_tags = list(tags)
+    # use existing callbacks if they exist
+    if (callbacks := config.get("callbacks")) and isinstance(
+        callbacks, CallbackManager
+    ):
+        if all_tags:
+            callbacks.add_tags(all_tags)
+        if metadata := config.get("metadata"):
+            callbacks.add_metadata(metadata)
+        return callbacks
+    else:
+        # otherwise create a new manager
+        return CallbackManager.configure(
+            inheritable_callbacks=config.get("callbacks"),
+            inheritable_tags=all_tags,
+            inheritable_metadata=config.get("metadata"),
+        )
+
+
+def get_async_callback_manager_for_config(
+    config: RunnableConfig,
+    tags: Sequence[str] | None = None,
+) -> AsyncCallbackManager:
+    """Get an async callback manager for a config.
+
+    Args:
+        config: The config.
+
+    Returns:
+        AsyncCallbackManager: The async callback manager.
+    """
+    from langchain_core.callbacks.manager import AsyncCallbackManager
+
+    # merge tags
+    all_tags = config.get("tags")
+    if all_tags is not None and tags is not None:
+        all_tags = [*all_tags, *tags]
+    elif tags is not None:
+        all_tags = list(tags)
+    # use existing callbacks if they exist
+    if (callbacks := config.get("callbacks")) and isinstance(
+        callbacks, AsyncCallbackManager
+    ):
+        if all_tags:
+            callbacks.add_tags(all_tags)
+        if metadata := config.get("metadata"):
+            callbacks.add_metadata(metadata)
+        return callbacks
+    else:
+        # otherwise create a new manager
+        return AsyncCallbackManager.configure(
+            inheritable_callbacks=config.get("callbacks"),
+            inheritable_tags=all_tags,
+            inheritable_metadata=config.get("metadata"),
+        )
+
+
+def _is_not_empty(value: Any) -> bool:
+    if isinstance(value, (list, tuple, dict)):
+        return len(value) > 0
+    else:
+        return value is not None
+
+
+def ensure_config(*configs: RunnableConfig | None) -> RunnableConfig:
+    """Return a config with all keys, merging any provided configs.
+
+    Args:
+        *configs: Configs to merge before ensuring defaults.
+
+    Returns:
+        RunnableConfig: The merged and ensured config.
+    """
+    empty = RunnableConfig(
+        tags=[],
+        metadata=ChainMap(),
+        callbacks=None,
+        recursion_limit=DEFAULT_RECURSION_LIMIT,
+        configurable={},
+    )
+    if var_config := var_child_runnable_config.get():
+        empty.update(
+            {
+                k: v.copy() if k in COPIABLE_KEYS else v  # type: ignore[attr-defined]
+                for k, v in var_config.items()
+                if _is_not_empty(v)
+            },
+        )
+    for config in configs:
+        if config is None:
+            continue
+        for k, v in config.items():
+            if _is_not_empty(v) and k in CONFIG_KEYS:
+                if k == CONF:
+                    empty[k] = cast(dict, v).copy()
+                else:
+                    empty[k] = v  # type: ignore[literal-required]
+        for k, v in config.items():
+            if _is_not_empty(v) and k not in CONFIG_KEYS:
+                empty[CONF][k] = v
+    for key, value in empty[CONF].items():
+        if (
+            not key.startswith("__")
+            and isinstance(value, (str, int, float, bool))
+            and key not in empty["metadata"]
+        ):
+            empty["metadata"][key] = value
+    return empty

libs/langgraph/langgraph/_internal/_constants.py

@@ -0,0 +1,110 @@
+"""Constants used for Pregel operations."""
+
+import sys
+from typing import Literal, cast
+
+# --- Reserved write keys ---
+INPUT = sys.intern("__input__")
+# for values passed as input to the graph
+INTERRUPT = sys.intern("__interrupt__")
+# for dynamic interrupts raised by nodes
+RESUME = sys.intern("__resume__")
+# for values passed to resume a node after an interrupt
+ERROR = sys.intern("__error__")
+# for errors raised by nodes
+NO_WRITES = sys.intern("__no_writes__")
+# marker to signal node didn't write anything
+TASKS = sys.intern("__pregel_tasks")
+# for Send objects returned by nodes/edges, corresponds to PUSH below
+RETURN = sys.intern("__return__")
+# for writes of a task where we simply record the return value
+PREVIOUS = sys.intern("__previous__")
+# the implicit branch that handles each node's Control values
+
+
+# --- Reserved cache namespaces ---
+CACHE_NS_WRITES = sys.intern("__pregel_ns_writes")
+# cache namespace for node writes
+
+# --- Reserved config.configurable keys ---
+CONFIG_KEY_SEND = sys.intern("__pregel_send")
+# holds the `write` function that accepts writes to state/edges/reserved keys
+CONFIG_KEY_READ = sys.intern("__pregel_read")
+# holds the `read` function that returns a copy of the current state
+CONFIG_KEY_CALL = sys.intern("__pregel_call")
+# holds the `call` function that accepts a node/func, args and returns a future
+CONFIG_KEY_CHECKPOINTER = sys.intern("__pregel_checkpointer")
+# holds a `BaseCheckpointSaver` passed from parent graph to child graphs
+CONFIG_KEY_STREAM = sys.intern("__pregel_stream")
+# holds a `StreamProtocol` passed from parent graph to child graphs
+CONFIG_KEY_CACHE = sys.intern("__pregel_cache")
+# holds a `BaseCache` made available to subgraphs
+CONFIG_KEY_RESUMING = sys.intern("__pregel_resuming")
+# holds a boolean indicating if subgraphs should resume from a previous checkpoint
+CONFIG_KEY_TASK_ID = sys.intern("__pregel_task_id")
+# holds the task ID for the current task
+CONFIG_KEY_THREAD_ID = sys.intern("thread_id")
+# holds the thread ID for the current invocation
+CONFIG_KEY_CHECKPOINT_MAP = sys.intern("checkpoint_map")
+# holds a mapping of checkpoint_ns -> checkpoint_id for parent graphs
+CONFIG_KEY_CHECKPOINT_ID = sys.intern("checkpoint_id")
+# holds the current checkpoint_id, if any
+CONFIG_KEY_CHECKPOINT_NS = sys.intern("checkpoint_ns")
+# holds the current checkpoint_ns, "" for root graph
+CONFIG_KEY_NODE_FINISHED = sys.intern("__pregel_node_finished")
+# holds a callback to be called when a node is finished
+CONFIG_KEY_SCRATCHPAD = sys.intern("__pregel_scratchpad")
+# holds a mutable dict for temporary storage scoped to the current task
+CONFIG_KEY_RUNNER_SUBMIT = sys.intern("__pregel_runner_submit")
+# holds a function that receives tasks from runner, executes them and returns results
+CONFIG_KEY_DURABILITY = sys.intern("__pregel_durability")
+# holds the durability mode, one of "sync", "async", or "exit"
+CONFIG_KEY_RUNTIME = sys.intern("__pregel_runtime")
+# holds a `Runtime` instance with context, store, stream writer, etc.
+CONFIG_KEY_RESUME_MAP = sys.intern("__pregel_resume_map")
+# holds a mapping of task ns -> resume value for resuming tasks
+
+# --- Other constants ---
+PUSH = sys.intern("__pregel_push")
+# denotes push-style tasks, ie. those created by Send objects
+PULL = sys.intern("__pregel_pull")
+# denotes pull-style tasks, ie. those triggered by edges
+NS_SEP = sys.intern("|")
+# for checkpoint_ns, separates each level (ie. graph|subgraph|subsubgraph)
+NS_END = sys.intern(":")
+# for checkpoint_ns, for each level, separates the namespace from the task_id
+CONF = cast(Literal["configurable"], sys.intern("configurable"))
+# key for the configurable dict in RunnableConfig
+NULL_TASK_ID = sys.intern("00000000-0000-0000-0000-000000000000")
+# the task_id to use for writes that are not associated with a task
+
+# redefined to avoid circular import with langgraph.constants
+_TAG_HIDDEN = sys.intern("langsmith:hidden")
+
+RESERVED = {
+    _TAG_HIDDEN,
+    # reserved write keys
+    INPUT,
+    INTERRUPT,
+    RESUME,
+    ERROR,
+    NO_WRITES,
+    # reserved config.configurable keys
+    CONFIG_KEY_SEND,
+    CONFIG_KEY_READ,
+    CONFIG_KEY_CHECKPOINTER,
+    CONFIG_KEY_STREAM,
+    CONFIG_KEY_CHECKPOINT_MAP,
+    CONFIG_KEY_RESUMING,
+    CONFIG_KEY_TASK_ID,
+    CONFIG_KEY_CHECKPOINT_MAP,
+    CONFIG_KEY_CHECKPOINT_ID,
+    CONFIG_KEY_CHECKPOINT_NS,
+    CONFIG_KEY_RESUME_MAP,
+    # other constants
+    PUSH,
+    PULL,
+    NS_SEP,
+    NS_END,
+    CONF,
+}

libs/langgraph/langgraph/_internal/_fields.py

@@ -9,9 +9,7 @@ from typing import Annotated, Any, Optional, Union, get_type_hints
 from pydantic import BaseModel
 from typing_extensions import NotRequired, ReadOnly, Required, get_origin
 
-# NOTE: this is redefined here separately from langgraph.constants
-# to avoid a circular import
-MISSING = object()
+from langgraph._internal._typing import MISSING
 
 
 def _is_optional_type(type_: Any) -> bool:

libs/langgraph/langgraph/_internal/_queue.py

@@ -128,6 +128,3 @@ class SyncQueue:
         return len(self._queue)
 
     __class_getitem__ = classmethod(types.GenericAlias)
-
-
-__all__ = ["AsyncQueue", "SyncQueue"]

libs/langgraph/langgraph/_internal/_retry.py

@@ -0,0 +1,29 @@
+def default_retry_on(exc: Exception) -> bool:
+    import httpx
+    import requests
+
+    if isinstance(exc, ConnectionError):
+        return True
+    if isinstance(exc, httpx.HTTPStatusError):
+        return 500 <= exc.response.status_code < 600
+    if isinstance(exc, requests.HTTPError):
+        return 500 <= exc.response.status_code < 600 if exc.response else True
+    if isinstance(
+        exc,
+        (
+            ValueError,
+            TypeError,
+            ArithmeticError,
+            ImportError,
+            LookupError,
+            NameError,
+            SyntaxError,
+            RuntimeError,
+            ReferenceError,
+            StopIteration,
+            StopAsyncIteration,
+            OSError,
+        ),
+    ):
+        return False
+    return True

libs/langgraph/langgraph/_internal/_runnable.py

@@ -0,0 +1,898 @@
+from __future__ import annotations
+
+import asyncio
+import enum
+import inspect
+import sys
+from collections.abc import (
+    AsyncIterator,
+    Awaitable,
+    Coroutine,
+    Generator,
+    Iterator,
+    Sequence,
+)
+from contextlib import AsyncExitStack, contextmanager
+from contextvars import Context, Token, copy_context
+from functools import partial, wraps
+from typing import (
+    Any,
+    Callable,
+    Optional,
+    Protocol,
+    Union,
+    cast,
+)
+
+from langchain_core.runnables.base import (
+    Runnable,
+    RunnableConfig,
+    RunnableLambda,
+    RunnableParallel,
+    RunnableSequence,
+)
+from langchain_core.runnables.base import (
+    RunnableLike as LCRunnableLike,
+)
+from langchain_core.runnables.config import (
+    run_in_executor,
+    var_child_runnable_config,
+)
+from langchain_core.runnables.utils import Input, Output
+from langchain_core.tracers.langchain import LangChainTracer
+from typing_extensions import TypeGuard
+
+from langgraph._internal._config import (
+    ensure_config,
+    get_async_callback_manager_for_config,
+    get_callback_manager_for_config,
+    patch_config,
+)
+from langgraph._internal._constants import (
+    CONF,
+    CONFIG_KEY_RUNTIME,
+)
+from langgraph._internal._typing import MISSING
+from langgraph.store.base import BaseStore
+from langgraph.types import StreamWriter
+
+try:
+    from langchain_core.tracers._streaming import _StreamingCallbackHandler
+except ImportError:
+    _StreamingCallbackHandler = None  # type: ignore
+
+
+def _set_config_context(
+    config: RunnableConfig, run: Any = None
+) -> Token[RunnableConfig | None]:
+    """Set the child Runnable config + tracing context.
+
+    Args:
+        config: The config to set.
+    """
+    config_token = var_child_runnable_config.set(config)
+    if run is not None:
+        from langsmith.run_helpers import _set_tracing_context
+
+        _set_tracing_context({"parent": run})
+    return config_token
+
+
+def _unset_config_context(token: Token[RunnableConfig | None], run: Any = None) -> None:
+    """Set the child Runnable config + tracing context.
+
+    Args:
+        token: The config token to reset.
+    """
+    var_child_runnable_config.reset(token)
+    if run is not None:
+        from langsmith.run_helpers import _set_tracing_context
+
+        _set_tracing_context(
+            {
+                "parent": None,
+                "project_name": None,
+                "tags": None,
+                "metadata": None,
+                "enabled": None,
+                "client": None,
+            }
+        )
+
+
+@contextmanager
+def set_config_context(
+    config: RunnableConfig, run: Any = None
+) -> Generator[Context, None, None]:
+    """Set the child Runnable config + tracing context.
+
+    Args:
+        config: The config to set.
+    """
+    ctx = copy_context()
+    config_token = ctx.run(_set_config_context, config, run)
+    try:
+        yield ctx
+    finally:
+        ctx.run(_unset_config_context, config_token, run)
+
+
+# Before Python 3.11 native StrEnum is not available
+class StrEnum(str, enum.Enum):
+    """A string enum."""
+
+
+# Special type to denote any type is accepted
+ANY_TYPE = object()
+
+ASYNCIO_ACCEPTS_CONTEXT = sys.version_info >= (3, 11)
+
+# List of keyword arguments that can be injected into nodes / tasks / tools at runtime.
+# A named argument may appear multiple times if it appears with distinct types.
+KWARGS_CONFIG_KEYS: tuple[tuple[str, tuple[Any, ...], str, Any], ...] = (
+    (
+        "config",
+        (RunnableConfig, "RunnableConfig", inspect.Parameter.empty),
+        # for now, use config directly, eventually, will pop off of Runtime
+        "N/A",
+        inspect.Parameter.empty,
+    ),
+    (
+        "writer",
+        (StreamWriter, "StreamWriter", inspect.Parameter.empty),
+        "stream_writer",
+        lambda _: None,
+    ),
+    (
+        "store",
+        (
+            BaseStore,
+            "BaseStore",
+            inspect.Parameter.empty,
+        ),
+        "store",
+        inspect.Parameter.empty,
+    ),
+    (
+        "store",
+        (
+            Optional[BaseStore],
+            "Optional[BaseStore]",
+        ),
+        "store",
+        None,
+    ),
+    (
+        "previous",
+        (ANY_TYPE,),
+        "previous",
+        inspect.Parameter.empty,
+    ),
+    (
+        "runtime",
+        (ANY_TYPE,),
+        # we never hit this block, we just inject runtime directly
+        "N/A",
+        inspect.Parameter.empty,
+    ),
+)
+"""List of kwargs that can be passed to functions, and their corresponding
+config keys, default values and type annotations.
+
+Used to configure keyword arguments that can be injected at runtime
+from the `Runtime` object as kwargs to `invoke`, `ainvoke`, `stream` and `astream`.
+
+For a keyword to be injected from the config object, the function signature
+must contain a kwarg with the same name and a matching type annotation.
+
+Each tuple contains:
+- the name of the kwarg in the function signature
+- the type annotation(s) for the kwarg
+- the `Runtime` attribute for fetching the value (N/A if not applicable)
+
+This is fully internal and should be further refactored to use `get_type_hints`
+to resolve forward references and optional types formatted like BaseStore | None.
+"""
+
+VALID_KINDS = (inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.KEYWORD_ONLY)
+
+
+class _RunnableWithWriter(Protocol[Input, Output]):
+    def __call__(self, state: Input, *, writer: StreamWriter) -> Output: ...
+
+
+class _RunnableWithStore(Protocol[Input, Output]):
+    def __call__(self, state: Input, *, store: BaseStore) -> Output: ...
+
+
+class _RunnableWithWriterStore(Protocol[Input, Output]):
+    def __call__(
+        self, state: Input, *, writer: StreamWriter, store: BaseStore
+    ) -> Output: ...
+
+
+class _RunnableWithConfigWriter(Protocol[Input, Output]):
+    def __call__(
+        self, state: Input, *, config: RunnableConfig, writer: StreamWriter
+    ) -> Output: ...
+
+
+class _RunnableWithConfigStore(Protocol[Input, Output]):
+    def __call__(
+        self, state: Input, *, config: RunnableConfig, store: BaseStore
+    ) -> Output: ...
+
+
+class _RunnableWithConfigWriterStore(Protocol[Input, Output]):
+    def __call__(
+        self,
+        state: Input,
+        *,
+        config: RunnableConfig,
+        writer: StreamWriter,
+        store: BaseStore,
+    ) -> Output: ...
+
+
+RunnableLike = Union[
+    LCRunnableLike,
+    _RunnableWithWriter[Input, Output],
+    _RunnableWithStore[Input, Output],
+    _RunnableWithWriterStore[Input, Output],
+    _RunnableWithConfigWriter[Input, Output],
+    _RunnableWithConfigStore[Input, Output],
+    _RunnableWithConfigWriterStore[Input, Output],
+]
+
+
+class RunnableCallable(Runnable):
+    """A much simpler version of RunnableLambda that requires sync and async functions."""
+
+    def __init__(
+        self,
+        func: Callable[..., Any | Runnable] | None,
+        afunc: Callable[..., Awaitable[Any | Runnable]] | None = None,
+        *,
+        name: str | None = None,
+        tags: Sequence[str] | None = None,
+        trace: bool = True,
+        recurse: bool = True,
+        explode_args: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        self.name = name
+        if self.name is None:
+            if func:
+                try:
+                    if func.__name__ != "<lambda>":
+                        self.name = func.__name__
+                except AttributeError:
+                    pass
+            elif afunc:
+                try:
+                    self.name = afunc.__name__
+                except AttributeError:
+                    pass
+        self.func = func
+        self.afunc = afunc
+        self.tags = tags
+        self.kwargs = kwargs
+        self.trace = trace
+        self.recurse = recurse
+        self.explode_args = explode_args
+        # check signature
+        if func is None and afunc is None:
+            raise ValueError("At least one of func or afunc must be provided.")
+
+        self.func_accepts: dict[str, tuple[str, Any]] = {}
+        params = inspect.signature(cast(Callable, func or afunc)).parameters
+
+        for kw, typ, runtime_key, default in KWARGS_CONFIG_KEYS:
+            p = params.get(kw)
+
+            if p is None or p.kind not in VALID_KINDS:
+                # If parameter is not found or is not a valid kind, skip
+                continue
+
+            if typ != (ANY_TYPE,) and p.annotation not in typ:
+                # A specific type is required, but the function annotation does
+                # not match the expected type.
+                continue
+
+            # If the kwarg is accepted by the function, store the key / runtime attribute to inject
+            self.func_accepts[kw] = (runtime_key, default)
+
+    def __repr__(self) -> str:
+        repr_args = {
+            k: v
+            for k, v in self.__dict__.items()
+            if k not in {"name", "func", "afunc", "config", "kwargs", "trace"}
+        }
+        return f"{self.get_name()}({', '.join(f'{k}={v!r}' for k, v in repr_args.items())})"
+
+    def invoke(
+        self, input: Any, config: RunnableConfig | None = None, **kwargs: Any
+    ) -> Any:
+        if self.func is None:
+            raise TypeError(
+                f'No synchronous function provided to "{self.name}".'
+                "\nEither initialize with a synchronous function or invoke"
+                " via the async API (ainvoke, astream, etc.)"
+            )
+        if config is None:
+            config = ensure_config()
+        if self.explode_args:
+            args, _kwargs = input
+            kwargs = {**self.kwargs, **_kwargs, **kwargs}
+        else:
+            args = (input,)
+            kwargs = {**self.kwargs, **kwargs}
+
+        runtime = config[CONF].get(CONFIG_KEY_RUNTIME)
+
+        for kw, (runtime_key, default) in self.func_accepts.items():
+            # If the kwarg is already set, use the set value
+            if kw in kwargs:
+                continue
+
+            kw_value: Any = MISSING
+            if kw == "config":
+                kw_value = config
+            elif runtime:
+                if kw == "runtime":
+                    kw_value = runtime
+                else:
+                    try:
+                        kw_value = getattr(runtime, runtime_key)
+                    except AttributeError:
+                        pass
+
+            if kw_value is MISSING:
+                if default is inspect.Parameter.empty:
+                    raise ValueError(
+                        f"Missing required config key '{runtime_key}' for '{self.name}'."
+                    )
+                kw_value = default
+            kwargs[kw] = kw_value
+
+        if self.trace:
+            callback_manager = get_callback_manager_for_config(config, self.tags)
+            run_manager = callback_manager.on_chain_start(
+                None,
+                input,
+                name=config.get("run_name") or self.get_name(),
+                run_id=config.pop("run_id", None),
+            )
+            try:
+                child_config = patch_config(config, callbacks=run_manager.get_child())
+                # get the run
+                for h in run_manager.handlers:
+                    if isinstance(h, LangChainTracer):
+                        run = h.run_map.get(str(run_manager.run_id))
+                        break
+                else:
+                    run = None
+                # run in context
+                with set_config_context(child_config, run) as context:
+                    ret = context.run(self.func, *args, **kwargs)
+            except BaseException as e:
+                run_manager.on_chain_error(e)
+                raise
+            else:
+                run_manager.on_chain_end(ret)
+        else:
+            ret = self.func(*args, **kwargs)
+        if self.recurse and isinstance(ret, Runnable):
+            return ret.invoke(input, config)
+        return ret
+
+    async def ainvoke(
+        self, input: Any, config: RunnableConfig | None = None, **kwargs: Any
+    ) -> Any:
+        if not self.afunc:
+            return self.invoke(input, config)
+        if config is None:
+            config = ensure_config()
+        if self.explode_args:
+            args, _kwargs = input
+            kwargs = {**self.kwargs, **_kwargs, **kwargs}
+        else:
+            args = (input,)
+            kwargs = {**self.kwargs, **kwargs}
+
+        runtime = config[CONF].get(CONFIG_KEY_RUNTIME)
+
+        for kw, (runtime_key, default) in self.func_accepts.items():
+            # If the kwarg has already been set, use the set value
+            if kw in kwargs:
+                continue
+
+            kw_value: Any = MISSING
+            if kw == "config":
+                kw_value = config
+            elif runtime:
+                if kw == "runtime":
+                    kw_value = runtime
+                else:
+                    try:
+                        kw_value = getattr(runtime, runtime_key)
+                    except AttributeError:
+                        pass
+            if kw_value is MISSING:
+                if default is inspect.Parameter.empty:
+                    raise ValueError(
+                        f"Missing required config key '{runtime_key}' for '{self.name}'."
+                    )
+                kw_value = default
+            kwargs[kw] = kw_value
+
+        if self.trace:
+            callback_manager = get_async_callback_manager_for_config(config, self.tags)
+            run_manager = await callback_manager.on_chain_start(
+                None,
+                input,
+                name=config.get("run_name") or self.name,
+                run_id=config.pop("run_id", None),
+            )
+            try:
+                child_config = patch_config(config, callbacks=run_manager.get_child())
+                coro = cast(Coroutine[None, None, Any], self.afunc(*args, **kwargs))
+                if ASYNCIO_ACCEPTS_CONTEXT:
+                    for h in run_manager.handlers:
+                        if isinstance(h, LangChainTracer):
+                            run = h.run_map.get(str(run_manager.run_id))
+                            break
+                    else:
+                        run = None
+                    with set_config_context(child_config, run) as context:
+                        ret = await asyncio.create_task(coro, context=context)
+                else:
+                    ret = await coro
+            except BaseException as e:
+                await run_manager.on_chain_error(e)
+                raise
+            else:
+                await run_manager.on_chain_end(ret)
+        else:
+            ret = await self.afunc(*args, **kwargs)
+        if self.recurse and isinstance(ret, Runnable):
+            return await ret.ainvoke(input, config)
+        return ret
+
+
+def is_async_callable(
+    func: Any,
+) -> TypeGuard[Callable[..., Awaitable]]:
+    """Check if a function is async."""
+    return (
+        asyncio.iscoroutinefunction(func)
+        or hasattr(func, "__call__")
+        and asyncio.iscoroutinefunction(func.__call__)
+    )
+
+
+def is_async_generator(
+    func: Any,
+) -> TypeGuard[Callable[..., AsyncIterator]]:
+    """Check if a function is an async generator."""
+    return (
+        inspect.isasyncgenfunction(func)
+        or hasattr(func, "__call__")
+        and inspect.isasyncgenfunction(func.__call__)
+    )
+
+
+def coerce_to_runnable(
+    thing: RunnableLike, *, name: str | None, trace: bool
+) -> Runnable:
+    """Coerce a runnable-like object into a Runnable.
+
+    Args:
+        thing: A runnable-like object.
+
+    Returns:
+        A Runnable.
+    """
+    if isinstance(thing, Runnable):
+        return thing
+    elif is_async_generator(thing) or inspect.isgeneratorfunction(thing):
+        return RunnableLambda(thing, name=name)
+    elif callable(thing):
+        if is_async_callable(thing):
+            return RunnableCallable(None, thing, name=name, trace=trace)
+        else:
+            return RunnableCallable(
+                thing,
+                wraps(thing)(partial(run_in_executor, None, thing)),  # type: ignore[arg-type]
+                name=name,
+                trace=trace,
+            )
+    elif isinstance(thing, dict):
+        return RunnableParallel(thing)
+    else:
+        raise TypeError(
+            f"Expected a Runnable, callable or dict."
+            f"Instead got an unsupported type: {type(thing)}"
+        )
+
+
+class RunnableSeq(Runnable):
+    """Sequence of Runnables, where the output of each is the input of the next.
+
+    RunnableSeq is a simpler version of RunnableSequence that is internal to
+    LangGraph.
+    """
+
+    def __init__(
+        self,
+        *steps: RunnableLike,
+        name: str | None = None,
+        trace_inputs: Callable[[Any], Any] | None = None,
+    ) -> None:
+        """Create a new RunnableSeq.
+
+        Args:
+            steps: The steps to include in the sequence.
+            name: The name of the Runnable. Defaults to None.
+
+        Raises:
+            ValueError: If the sequence has less than 2 steps.
+        """
+        steps_flat: list[Runnable] = []
+        for step in steps:
+            if isinstance(step, RunnableSequence):
+                steps_flat.extend(step.steps)
+            elif isinstance(step, RunnableSeq):
+                steps_flat.extend(step.steps)
+            else:
+                steps_flat.append(coerce_to_runnable(step, name=None, trace=True))
+        if len(steps_flat) < 2:
+            raise ValueError(
+                f"RunnableSeq must have at least 2 steps, got {len(steps_flat)}"
+            )
+        self.steps = steps_flat
+        self.name = name
+        self.trace_inputs = trace_inputs
+
+    def __or__(
+        self,
+        other: Any,
+    ) -> Runnable:
+        if isinstance(other, RunnableSequence):
+            return RunnableSeq(
+                *self.steps,
+                other.first,
+                *other.middle,
+                other.last,
+                name=self.name or other.name,
+            )
+        elif isinstance(other, RunnableSeq):
+            return RunnableSeq(
+                *self.steps,
+                *other.steps,
+                name=self.name or other.name,
+            )
+        else:
+            return RunnableSeq(
+                *self.steps,
+                coerce_to_runnable(other, name=None, trace=True),
+                name=self.name,
+            )
+
+    def __ror__(
+        self,
+        other: Any,
+    ) -> Runnable:
+        if isinstance(other, RunnableSequence):
+            return RunnableSequence(
+                other.first,
+                *other.middle,
+                other.last,
+                *self.steps,
+                name=other.name or self.name,
+            )
+        elif isinstance(other, RunnableSeq):
+            return RunnableSeq(
+                *other.steps,
+                *self.steps,
+                name=other.name or self.name,
+            )
+        else:
+            return RunnableSequence(
+                coerce_to_runnable(other, name=None, trace=True),
+                *self.steps,
+                name=self.name,
+            )
+
+    def invoke(
+        self, input: Input, config: RunnableConfig | None = None, **kwargs: Any
+    ) -> Any:
+        if config is None:
+            config = ensure_config()
+        # setup callbacks and context
+        callback_manager = get_callback_manager_for_config(config)
+        # start the root run
+        run_manager = callback_manager.on_chain_start(
+            None,
+            self.trace_inputs(input) if self.trace_inputs is not None else input,
+            name=config.get("run_name") or self.get_name(),
+            run_id=config.pop("run_id", None),
+        )
+        # invoke all steps in sequence
+        try:
+            for i, step in enumerate(self.steps):
+                # mark each step as a child run
+                config = patch_config(
+                    config, callbacks=run_manager.get_child(f"seq:step:{i + 1}")
+                )
+                # 1st step is the actual node,
+                # others are writers which don't need to be run in context
+                if i == 0:
+                    # get the run object
+                    for h in run_manager.handlers:
+                        if isinstance(h, LangChainTracer):
+                            run = h.run_map.get(str(run_manager.run_id))
+                            break
+                    else:
+                        run = None
+                    # run in context
+                    with set_config_context(config, run) as context:
+                        input = context.run(step.invoke, input, config, **kwargs)
+                else:
+                    input = step.invoke(input, config)
+        # finish the root run
+        except BaseException as e:
+            run_manager.on_chain_error(e)
+            raise
+        else:
+            run_manager.on_chain_end(input)
+            return input
+
+    async def ainvoke(
+        self,
+        input: Input,
+        config: RunnableConfig | None = None,
+        **kwargs: Any | None,
+    ) -> Any:
+        if config is None:
+            config = ensure_config()
+        # setup callbacks
+        callback_manager = get_async_callback_manager_for_config(config)
+        # start the root run
+        run_manager = await callback_manager.on_chain_start(
+            None,
+            self.trace_inputs(input) if self.trace_inputs is not None else input,
+            name=config.get("run_name") or self.get_name(),
+            run_id=config.pop("run_id", None),
+        )
+
+        # invoke all steps in sequence
+        try:
+            for i, step in enumerate(self.steps):
+                # mark each step as a child run
+                config = patch_config(
+                    config, callbacks=run_manager.get_child(f"seq:step:{i + 1}")
+                )
+                # 1st step is the actual node,
+                # others are writers which don't need to be run in context
+                if i == 0:
+                    if ASYNCIO_ACCEPTS_CONTEXT:
+                        # get the run object
+                        for h in run_manager.handlers:
+                            if isinstance(h, LangChainTracer):
+                                run = h.run_map.get(str(run_manager.run_id))
+                                break
+                        else:
+                            run = None
+                        # run in context
+                        with set_config_context(config, run) as context:
+                            input = await asyncio.create_task(
+                                step.ainvoke(input, config, **kwargs), context=context
+                            )
+                    else:
+                        input = await step.ainvoke(input, config, **kwargs)
+                else:
+                    input = await step.ainvoke(input, config)
+        # finish the root run
+        except BaseException as e:
+            await run_manager.on_chain_error(e)
+            raise
+        else:
+            await run_manager.on_chain_end(input)
+            return input
+
+    def stream(
+        self,
+        input: Input,
+        config: RunnableConfig | None = None,
+        **kwargs: Any | None,
+    ) -> Iterator[Any]:
+        if config is None:
+            config = ensure_config()
+        # setup callbacks
+        callback_manager = get_callback_manager_for_config(config)
+        # start the root run
+        run_manager = callback_manager.on_chain_start(
+            None,
+            self.trace_inputs(input) if self.trace_inputs is not None else input,
+            name=config.get("run_name") or self.get_name(),
+            run_id=config.pop("run_id", None),
+        )
+        # get the run object
+        for h in run_manager.handlers:
+            if isinstance(h, LangChainTracer):
+                run = h.run_map.get(str(run_manager.run_id))
+                break
+        else:
+            run = None
+        # create first step config
+        config = patch_config(
+            config,
+            callbacks=run_manager.get_child(f"seq:step:{1}"),
+        )
+        # run all in context
+        with set_config_context(config, run) as context:
+            try:
+                # stream the last steps
+                # transform the input stream of each step with the next
+                # steps that don't natively support transforming an input stream will
+                # buffer input in memory until all available, and then start emitting output
+                for idx, step in enumerate(self.steps):
+                    if idx == 0:
+                        iterator = step.stream(input, config, **kwargs)
+                    else:
+                        config = patch_config(
+                            config,
+                            callbacks=run_manager.get_child(f"seq:step:{idx + 1}"),
+                        )
+                        iterator = step.transform(iterator, config)
+                # populates streamed_output in astream_log() output if needed
+                if _StreamingCallbackHandler is not None:
+                    for h in run_manager.handlers:
+                        if isinstance(h, _StreamingCallbackHandler):
+                            iterator = h.tap_output_iter(run_manager.run_id, iterator)
+                # consume into final output
+                output = context.run(_consume_iter, iterator)
+                # sequence doesn't emit output, yield to mark as generator
+                yield
+            except BaseException as e:
+                run_manager.on_chain_error(e)
+                raise
+            else:
+                run_manager.on_chain_end(output)
+
+    async def astream(
+        self,
+        input: Input,
+        config: RunnableConfig | None = None,
+        **kwargs: Any | None,
+    ) -> AsyncIterator[Any]:
+        if config is None:
+            config = ensure_config()
+        # setup callbacks
+        callback_manager = get_async_callback_manager_for_config(config)
+        # start the root run
+        run_manager = await callback_manager.on_chain_start(
+            None,
+            self.trace_inputs(input) if self.trace_inputs is not None else input,
+            name=config.get("run_name") or self.get_name(),
+            run_id=config.pop("run_id", None),
+        )
+        # stream the last steps
+        # transform the input stream of each step with the next
+        # steps that don't natively support transforming an input stream will
+        # buffer input in memory until all available, and then start emitting output
+        if ASYNCIO_ACCEPTS_CONTEXT:
+            # get the run object
+            for h in run_manager.handlers:
+                if isinstance(h, LangChainTracer):
+                    run = h.run_map.get(str(run_manager.run_id))
+                    break
+            else:
+                run = None
+            # create first step config
+            config = patch_config(
+                config,
+                callbacks=run_manager.get_child(f"seq:step:{1}"),
+            )
+            # run all in context
+            with set_config_context(config, run) as context:
+                try:
+                    async with AsyncExitStack() as stack:
+                        for idx, step in enumerate(self.steps):
+                            if idx == 0:
+                                aiterator = step.astream(input, config, **kwargs)
+                            else:
+                                config = patch_config(
+                                    config,
+                                    callbacks=run_manager.get_child(
+                                        f"seq:step:{idx + 1}"
+                                    ),
+                                )
+                                aiterator = step.atransform(aiterator, config)
+                            if hasattr(aiterator, "aclose"):
+                                stack.push_async_callback(aiterator.aclose)
+                        # populates streamed_output in astream_log() output if needed
+                        if _StreamingCallbackHandler is not None:
+                            for h in run_manager.handlers:
+                                if isinstance(h, _StreamingCallbackHandler):
+                                    aiterator = h.tap_output_aiter(
+                                        run_manager.run_id, aiterator
+                                    )
+                        # consume into final output
+                        output = await asyncio.create_task(
+                            _consume_aiter(aiterator), context=context
+                        )
+                        # sequence doesn't emit output, yield to mark as generator
+                        yield
+                except BaseException as e:
+                    await run_manager.on_chain_error(e)
+                    raise
+                else:
+                    await run_manager.on_chain_end(output)
+        else:
+            try:
+                async with AsyncExitStack() as stack:
+                    for idx, step in enumerate(self.steps):
+                        config = patch_config(
+                            config,
+                            callbacks=run_manager.get_child(f"seq:step:{idx + 1}"),
+                        )
+                        if idx == 0:
+                            aiterator = step.astream(input, config, **kwargs)
+                        else:
+                            aiterator = step.atransform(aiterator, config)
+                        if hasattr(aiterator, "aclose"):
+                            stack.push_async_callback(aiterator.aclose)
+                    # populates streamed_output in astream_log() output if needed
+                    if _StreamingCallbackHandler is not None:
+                        for h in run_manager.handlers:
+                            if isinstance(h, _StreamingCallbackHandler):
+                                aiterator = h.tap_output_aiter(
+                                    run_manager.run_id, aiterator
+                                )
+                    # consume into final output
+                    output = await _consume_aiter(aiterator)
+                    # sequence doesn't emit output, yield to mark as generator
+                    yield
+            except BaseException as e:
+                await run_manager.on_chain_error(e)
+                raise
+            else:
+                await run_manager.on_chain_end(output)
+
+
+def _consume_iter(it: Iterator[Any]) -> Any:
+    """Consume an iterator."""
+    output: Any = None
+    add_supported = False
+    for chunk in it:
+        # collect final output
+        if output is None:
+            output = chunk
+        elif add_supported:
+            try:
+                output = output + chunk
+            except TypeError:
+                output = chunk
+                add_supported = False
+        else:
+            output = chunk
+    return output
+
+
+async def _consume_aiter(it: AsyncIterator[Any]) -> Any:
+    """Consume an async iterator."""
+    output: Any = None
+    add_supported = False
+    async for chunk in it:
+        # collect final output
+        if add_supported:
+            try:
+                output = output + chunk
+            except TypeError:
+                output = chunk
+                add_supported = False
+        else:
+            output = chunk
+    return output

libs/langgraph/langgraph/_internal/_typing.py

@@ -42,13 +42,13 @@ It can either be a `TypedDict`, `dataclass`, or Pydantic `BaseModel`.
 Note: we cannot use either `TypedDict` or `dataclass` directly due to limitations in type checking.
 """
 
-
-class Unset:
-    """A sentinel value to represent an unset type."""
-
-
-UNSET: Unset = Unset()
+MISSING = object()
+"""Unset sentinel value."""
 
 
 class DeprecatedKwargs(TypedDict):
     """TypedDict to use for extra keyword arguments, enabling type checking warnings for deprecated arguments."""
+
+
+EMPTY_SEQ: tuple[str, ...] = tuple()
+"""An empty sequence of strings."""