Log queue size when future cancelled

.github/workflows/_test_langgraph.yml

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

.github/workflows/ci.yml

@@ -31,6 +31,7 @@ jobs:
             "libs/cli",
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
+            "libs/checkpoint-duckdb",
             "libs/checkpoint-postgres",
             "libs/scheduler-kafka",
           ]
@@ -43,12 +44,12 @@ jobs:
     name: cd ${{ matrix.working-directory }}
     strategy:
       matrix:
-        working-directory:
-          [
+        working-directory: [
             "libs/cli",
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
-            "libs/checkpoint-postgres",
+            "libs/checkpoint-duckdb",
+            "libs/checkpoint-postgres"
           ]
     uses: ./.github/workflows/_test.yml
     with:
@@ -75,7 +76,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: '3.11'
       - name: Run check_sdk_methods script
         run: python .github/scripts/check_sdk_methods.py
 
@@ -108,40 +109,9 @@ jobs:
       - name: Build
         run: yarn build
 
-  test-js:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        working-directory:
-          - "libs/sdk-js"
-    defaults:
-      run:
-        working-directory: ${{ matrix.working-directory }}
-    steps:
-      - uses: actions/checkout@v3
-      - name: Setup Node.js (LTS)
-        uses: actions/setup-node@v3
-        with:
-          node-version: "20"
-          cache: "yarn"
-          cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
-      - name: Install dependencies
-        run: yarn install
-      - name: Run tests
-        run: yarn test
-
   ci_success:
     name: "CI Success"
-    needs:
-      [
-        lint,
-        lint-js,
-        test,
-        test-langgraph,
-        test-scheduler-kafka,
-        integration-test,
-        test-js,
-      ]
+    needs: [lint, lint-js, test, test-langgraph, test-scheduler-kafka, integration-test]
     if: |
       always()
     runs-on: ubuntu-latest

.github/workflows/deploy_docs.yml

@@ -85,7 +85,7 @@ jobs:
           if [ "${{ github.event_name }}" == "schedule" ] || [ "${{ github.event_name }}" == "workflow_dispatch" ] || ([ "${{ github.event_name }}" == "push" ] && [ "${{ github.ref }}" == "refs/heads/main" ]); then
             echo "Running link check on all HTML files matching notebooks in docs directory..."
             poetry run pytest -v \
-              --check-links-ignore "https://(api|web|docs|academy)\.smith\.langchain\.com/.*" \
+              --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
               --check-links-ignore "https://x.com/.*" \
               --check-links-ignore "https://github\.com/.*" \
               --check-links-ignore "http://localhost:8123/.*" \
@@ -106,7 +106,7 @@ jobs:
             if [ -n "${CHANGED_FILES}" ]; then
               echo "Running link check on HTML files matching changed notebook files..."
               poetry run pytest -v \
-                --check-links-ignore "https://(api|web|docs|academy)\.smith\.langchain\.com/.*" \
+                --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
                 --check-links-ignore "http://localhost:8123/.*" \
                 --check-links-ignore "http://localhost:2024.*" \
                 --check-links-ignore "http://127.0.0.1:.*" \

docs/_scripts/prepare_notebooks_for_ci.py

@@ -42,13 +42,10 @@ NOTEBOOKS_NO_EXECUTION = [
     "docs/docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
     "docs/docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
     "docs/docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
-    "docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # flakiness only when running in GHA 
-    "docs/docs/tutorials/rag/langgraph_self_rag.ipynb",  # flakiness only when running in GHA
-    "docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb",  # flakiness only when running in GHA
+    "docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # Cannot create a consistent method resolution error from VCR
     "docs/docs/how-tos/map-reduce.ipynb",  # flakiness from structured output, only when running with VCR
     "docs/docs/tutorials/tot/tot.ipynb",
-    "docs/docs/how-tos/visualization.ipynb",
-    "docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb"
+    "docs/docs/how-tos/visualization.ipynb"
 ]
 
 
@@ -130,18 +127,7 @@ def add_vcr_to_notebook(
             uses_langsmith = True
 
     # Add import statement
-    vcr_import_lines = []
-    if uses_langsmith:
-        vcr_import_lines.extend([
-            # patch urllib3 to handle vcr errors, see more here:
-            # https://github.com/langchain-ai/langsmith-sdk/blob/main/python/langsmith/_internal/_patch.py
-            "import sys",
-            f"sys.path.insert(0, '{os.path.join(DOCS_PATH, '_scripts')}')",
-            "import _patch as patch_urllib3",
-            "patch_urllib3.patch_urllib3()",
-        ])
-
-    vcr_import_lines.extend([
+    vcr_import_lines = [
         "import nest_asyncio",
         "nest_asyncio.apply()",
         "import vcr",
@@ -171,8 +157,16 @@ def add_vcr_to_notebook(
         "",
         "custom_vcr.register_serializer('advanced_compressed', AdvancedCompressedSerializer())",
         "custom_vcr.serializer = 'advanced_compressed'",
-    ])
-
+    ]
+    if uses_langsmith:
+        vcr_import_lines.extend(
+            # patch urllib3 to handle vcr errors, see more here:
+            # https://github.com/langchain-ai/langsmith-sdk/blob/main/python/langsmith/_internal/_patch.py
+            "import sys",
+            f"sys.path.insert(0, '{os.path.join(DOCS_PATH, '_scripts')}')",
+            "import _patch as patch_urllib3",
+            "patch_urllib3.patch_urllib3()",
+        )
     import_cell = nbformat.v4.new_code_cell(source="\n".join(vcr_import_lines))
     import_cell.pop("id", None)
     notebook.cells.insert(0, import_cell)

docs/cassettes/LLMCompiler_15dd9639-691f-4906-9012-83fd6e9ac126.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/LLMCompiler_31854dfd-b82f-4c24-9b58-6bae66777909.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/LLMCompiler_730490c6-6e3a-4173-82a1-9eb9d5eeff20.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/langgraph_adaptive_rag_ded99680-437a-4c9d-b860-619c88949d84.msgpack.zlib

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docs/cassettes/langgraph_agentic_rag_278d1d83-dda6-4de4-bf8b-be9965c227fa.msgpack.zlib

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docs/cassettes/langgraph_self_rag_bd62276f-bf26-40d0-8cff-e07b10e00321.msgpack.zlib

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docs/cassettes/langgraph_self_rag_c6f4c70e-1660-4149-82c0-837f19fc9fb5.msgpack.zlib

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docs/cassettes/plan-and-execute_67ce37b7-e089-479b-bcb8-c3f5d9874613.msgpack.zlib

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docs/cassettes/plan-and-execute_72d233ca-1dbf-4b43-b680-b3bf39e3691f.msgpack.zlib

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docs/cassettes/run-id-langsmith_7.msgpack.zlib

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docs/cassettes/streaming-events-from-within-tools_4c9cdad3-3e9a-444f-9d9d-eae20b8d3486.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_11.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_12.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_13.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_14.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_15.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_16.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_30.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_31.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_33.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_34.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_40.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_41.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_42.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_43.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_48.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_49.msgpack.zlib

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docs/cassettes/update-state-from-tools_9d71af94-572a-4961-88a7-665e792cf96a.msgpack.zlib

@@ -1 +0,0 @@
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docs/cassettes/update-state-from-tools_de34a58b-1765-4b63-a232-d46790aff884.msgpack.zlib

@@ -1 +0,0 @@
-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

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

@@ -279,58 +279,6 @@
         }
       }
     },
-    "/v1/projects/{project_id}/revisions/{revision_id}/deploy": {
-      "post": {
-        "tags": ["Revisions (v1)"],
-        "summary": "Deploy Revision",
-        "description": "Deploy revision by ID.\n\nThis endpoint redeploys the deployment of a revision without rebuilding the image for the deployment. Redeploying the deployment of a revision may mitigate intermittent issues with a deployment.\n\nThe revision must be in the `DEPLOYED` status and must be the latest revision of the project.",
-        "operationId": "deploy_revision_projects__project_id__revisions__revision_id__deploy_post",
-        "parameters": [
-          {
-            "required": true,
-            "schema": {
-              "type": "string",
-              "format": "uuid",
-              "title": "Project ID"
-            },
-            "name": "project_id",
-            "in": "path"
-          },
-          {
-            "required": true,
-            "schema": {
-              "type": "string",
-              "format": "uuid",
-              "title": "Revision ID"
-            },
-            "name": "revision_id",
-            "in": "path"
-          }
-        ],
-        "responses": {
-          "400": {
-            "description": "Revision is not in DEPLOYED status or revision is not the latest revision for the project.",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          },
-          "404": {
-            "description": "Revision not found.",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          }
-        }
-      }
-    },
     "/v1/projects/{project_id}/revisions/{revision_id}/interrupt": {
       "post": {
         "tags": ["Revisions (v1)"],
@@ -371,6 +319,31 @@
       }
     },
     "schemas": {
+      "EnvVar": {
+        "type": "object",
+        "description": "An environment variable or secret.",
+        "properties": {
+          "name": {
+            "type": "string",
+            "description": "Environment variable or secret name.",
+            "required": true
+          },
+          "value": {
+            "type": "string",
+            "description": "Environment variable or secret value.",
+            "required": true
+          },
+          "type": {
+            "type": "string",
+            "enum": [
+              "default",
+              "secret"
+            ],
+            "description": "Field to designate type of the environment variable (default) or secret.",
+            "required": true
+          }
+        }
+      },
       "ContainerSpec": {
         "type": "object",
         "description": "Container specification for a revision's deployment.\n\nIf any field is omitted or set to `null`, the internal default value is used depending on the deployment type (`dev` or `prod`).",
@@ -431,42 +404,6 @@
           }
         }
       },
-      "EnvVar": {
-        "type": "object",
-        "description": "An environment variable or secret.",
-        "properties": {
-          "name": {
-            "type": "string",
-            "description": "Environment variable or secret name.",
-            "required": true
-          },
-          "value": {
-            "type": "string",
-            "description": "Environment variable or secret value.",
-            "required": true
-          },
-          "type": {
-            "type": "string",
-            "enum": [
-              "default",
-              "secret"
-            ],
-            "description": "Field to designate type of the environment variable (default) or secret.",
-            "required": true
-          }
-        }
-      },
-      "ErrorResponse": {
-        "type": "object",
-        "description": "Error response.",
-        "properties": {
-          "detail": {
-            "type": "string",
-            "description": "Error details.",
-            "required": true
-          }
-        }
-      },
       "Project": {
         "type": "object",
         "description": "A project corresponds to a LangGraph Server deployment and the associated LangSmith tracing project.",

docs/docs/cloud/reference/cli.md

@@ -289,7 +289,4 @@ RUN set -ex && \
 RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
 
 ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
-```
-
-???+ note "Updating your langgraph.json file"
-    The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
+```

docs/docs/concepts/deployment_options.md

@@ -28,10 +28,6 @@ The guide below will explain the differences between the deployment options.
 
     The Self-Hosted Enterprise version is only available for the **Enterprise** plan.
 
-!!! warning "Note"
-
-    The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Enterprise LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
-
 With a Self-Hosted Enterprise deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
 
 You’ll build a Docker image using the [LangGraph CLI](./langgraph_cli.md), which can then be deployed on your own infrastructure.
@@ -47,10 +43,6 @@ For more information, please see:
 
     The Self-Hosted Lite version is available for all plans.
 
-!!! warning "Note"
-
-    The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Lite LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
-
 The Self-Hosted Lite deployment option is a free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner.
 
 With a Self-Hosted Lite deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
@@ -69,11 +61,12 @@ For more information, please see:
 
     The Cloud SaaS version of LangGraph Platform is only available for **Plus** and **Enterprise** plans.
 
+
 The [Cloud SaaS](./langgraph_cloud.md) version of LangGraph Platform is hosted as part of [LangSmith](https://smith.langchain.com/).
 
 The Cloud SaaS version of LangGraph Platform provides a simple way to deploy and manage your LangGraph applications.
 
-This deployment option provides access to the LangGraph Platform UI (within LangSmith) and an integration with GitHub, allowing you to deploy code from any of your repositories on GitHub.
+This deployment option provides an integration with GitHub, allowing you to deploy code from any of your repositories on GitHub.
 
 For more information, please see:
 
@@ -88,7 +81,7 @@ For more information, please see:
     The Bring Your Own Cloud version of LangGraph Platform is only available for **Enterprise** plans.
 
 
-This combines the best of both worlds for Cloud and Self-Hosted. Create your deployments through the LangGraph Platform UI (within LangSmith) and we manage the infrastructure so you don't have to. The infrastructure all runs within your cloud. This is currently only available on AWS.
+This combines the best of both worlds for Cloud and Self-Hosted. We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud. This is currently only available on AWS.
 
 For more information please see:
 

docs/docs/concepts/index.md

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

docs/docs/concepts/langgraph_cli.md

@@ -62,9 +62,6 @@ The server includes all API endpoints for your graph's runs, threads, assistants
 
 The `langgraph dockerfile` command generates a [Dockerfile](https://docs.docker.com/reference/dockerfile/) that can be used to build images for and deploy instances of the [LangGraph API server](./langgraph_server.md). This is useful if you want to further customize the dockerfile or deploy in a more custom way.
 
-??? note "Updating your langgraph.json file"
-    The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
-
 ## Related
 
 - [LangGraph CLI API Reference](../cloud/reference/cli.md)

docs/docs/concepts/langgraph_cloud.md

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

docs/docs/concepts/langgraph_studio.md

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

docs/docs/concepts/multi_agent.md

@@ -112,7 +112,7 @@ In this architecture, agents are defined as graph nodes. Each agent can communic
 ```python
 from typing import Literal
 from langchain_openai import ChatOpenAI
-from langgraph.graph import StateGraph, MessagesState, START, END
+from langgraph.graph import StateGraph, MessagesState, START
 
 model = ChatOpenAI()
 

docs/docs/concepts/persistence.md

@@ -147,21 +147,24 @@ In our example, the output of `get_state_history` will look like this:
 
 ### Replay
 
-It's also possible to play-back a prior graph execution. If we `invoke` a graph with a `thread_id` and a `checkpoint_id`, then we will *re-play* the previously executed steps _before_ a checkpoint that corresponds to the `checkpoint_id`, and only execute the steps _after_ the checkpoint.
+It's also possible to play-back a prior graph execution. If we `invoking` a graph with a `thread_id` and a `checkpoint_id`, then we will *re-play* the graph from a checkpoint that corresponds to the `checkpoint_id`.
 
-* `thread_id` is the ID of a thread.
-* `checkpoint_id` is an identifier that refers to a specific checkpoint within a thread.
+* `thread_id` is simply the ID of a thread. This is always required.
+* `checkpoint_id` This identifier refers to a specific checkpoint within a thread. 
 
 You must pass these when invoking the graph as part of the `configurable` portion of the config:
 
 ```python
-config = {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}
+# {"configurable": {"thread_id": "1"}}  # valid config
+# {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}  # also valid config
+
+config = {"configurable": {"thread_id": "1"}}
 graph.invoke(None, config=config)
 ```
 
-Importantly, LangGraph knows whether a particular step has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step, but only for the steps _before_ the provided `checkpoint_id`. All of the steps _after_ `checkpoint_id` will be executed (i.e., a new fork), even if they have been executed previously. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.ipynb).
+Importantly, LangGraph knows whether a particular checkpoint has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.ipynb).
 
-![Replay](img/persistence/re_play.png)
+![Replay](img/persistence/re_play.jpg)
 
 ### Update state
 

docs/docs/concepts/self_hosted.md

@@ -32,10 +32,6 @@ To use the Self-Hosted Enterprise version, you must acquire a license key that y
 - Build the docker image for [LangGraph Server](./langgraph_server.md) using the [LangGraph CLI](./langgraph_cli.md).
 - Deploy a web server that will run the docker image and pass in the necessary environment variables.
 
-!!! warning "Note"
-
-    The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Lite or Self-Hosted Enterprise LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
-
 For step-by-step instructions, see [How to set up a self-hosted deployment of LangGraph](../how-tos/deploy-self-hosted.md).
 
 ## Helm Chart

docs/docs/concepts/time-travel.md

@@ -17,9 +17,17 @@ We call these debugging techniques **Time Travel**, composed of two key actions:
 
 ![](./img/human_in_the_loop/replay.png)
 
-Replaying allows us to revisit and reproduce an agent's past actions, up to and including a specific step (checkpoint).
+Replaying allows us to revisit and reproduce an agent's past actions. This can be done either from the current state (or checkpoint) of the graph or from a specific checkpoint.
 
-To replay actions before a specific checkpoint, start by retrieving all checkpoints for the thread:
+To replay from the current state, simply pass `None` as the input along with a `thread`:
+
+```python
+thread = {"configurable": {"thread_id": "1"}}
+for event in graph.stream(None, thread, stream_mode="values"):
+    print(event)
+```
+
+To replay actions from a specific checkpoint, start by retrieving all checkpoints for the thread:
 
 ```python
 all_checkpoints = []
@@ -35,7 +43,7 @@ for event in graph.stream(None, config, stream_mode="values"):
     print(event)
 ```
 
-The graph replays previously executed steps _before_ the provided `checkpoint_id` and executes the steps _after_ `checkpoint_id` (i.e., a new fork), even if they have been executed previously.
+The graph efficiently replays previously executed nodes instead of re-executing them, leveraging its awareness of prior checkpoint executions.
 
 ## Forking
 

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

@@ -29,7 +29,6 @@ You will eventually need to pass in the following environment variables to the L
 - `DATABASE_URI`: Postgres connection details. Postgres will be used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics.
 - `LANGSMITH_API_KEY`: (If using [Self-Hosted Lite](../concepts/deployment_options.md#self-hosted-lite)) LangSmith API key. This will be used to authenticate ONCE at server start up.
 - `LANGGRAPH_CLOUD_LICENSE_KEY`: (If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
-- `LANGCHAIN_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGCHAIN_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
 
 
 ## Build the Docker Image

docs/docs/how-tos/index.md

@@ -1,4 +1,6 @@
 ---
+hide:
+  - navigation
 title: How-to Guides
 description: How to accomplish common tasks in LangGraph
 ---

docs/docs/how-tos/memory/semantic-search.ipynb

@@ -208,7 +208,7 @@
     "from typing import Optional\n",
     "\n",
     "from langchain.chat_models import init_chat_model\n",
-    "from langgraph.prebuilt import InjectedStore\n",
+    "from langchain_core.tools import InjectedToolArg\n",
     "from langgraph.store.base import BaseStore\n",
     "from typing_extensions import Annotated\n",
     "\n",
@@ -232,7 +232,7 @@
     "    content: str,\n",
     "    *,\n",
     "    memory_id: Optional[uuid.UUID] = None,\n",
-    "    store: Annotated[BaseStore, InjectedStore],\n",
+    "    store: Annotated[BaseStore, InjectedToolArg],\n",
     "):\n",
     "    \"\"\"Upsert a memory in the database.\"\"\"\n",
     "    # The LLM can use this tool to store a new memory\n",

docs/docs/how-tos/subgraphs-manage-state.ipynb

@@ -123,7 +123,7 @@
     "    return f\"It's sunny in {city}!\"\n",
     "\n",
     "\n",
-    "raw_model = ChatOpenAI(model=\"gpt-4o\")\n",
+    "raw_model = ChatOpenAI()\n",
     "model = raw_model.with_structured_output(get_weather)\n",
     "\n",
     "\n",

docs/docs/index.md

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

docs/docs/troubleshooting/errors/MULTIPLE_SUBGRAPHS.md

@@ -1,6 +1,6 @@
 # MULTIPLE_SUBGRAPHS
 
-You are calling subgraphs multiple times within a single LangGraph node with checkpointing enabled for each subgraph.
+You are calling the same subgraph multiple times within a single LangGraph node with checkpointing enabled for each subgraph.
 
 This is currently not allowed due to internal restrictions on how checkpoint namespacing for subgraphs works.
 
@@ -9,4 +9,4 @@ This is currently not allowed due to internal restrictions on how checkpoint nam
 The following may help resolve this error:
 
 - If you don't need to interrupt/resume from a subgraph, pass `checkpointer=False` when compiling it like this: `.compile(checkpointer=False)`
-- Don't imperatively call graphs multiple times in the same node, and instead use the [`Send`](https://langchain-ai.github.io/langgraph/concepts/low_level/#send) API.
+- Don't imperatively call graphs multiple times in the same node, and instead use the [`Send`](https://langchain-ai.github.io/langgraph/concepts/low_level/#send) API.

docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb

@@ -582,7 +582,7 @@
     ")\n",
     "\n",
     "evaluator = prompt | ChatOpenAI(model=\"gpt-4-turbo-preview\").with_structured_output(\n",
-    "    RedTeamingResult, method=\"function_calling\"\n",
+    "    RedTeamingResult\n",
     ")\n",
     "\n",
     "\n",

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

@@ -246,7 +246,7 @@
     "\n",
     "Define the (`fetch_user_flight_information`) tool to let the agent see the current user's flight information.  Then define tools to search for flights and manage the passenger's bookings stored in the SQL database.\n",
     "\n",
-    "We then can [access the RunnableConfig](https://python.langchain.com/docs/how_to/tool_configure/#inferring-by-parameter-type) for a given run to check the `passenger_id` of the user accessing this application. The LLM never has to provide these explicitly, they are provided for a given invocation of the graph so that each user cannot access other passengers' booking information.\n",
+    "We the can [access the RunnableConfig](https://python.langchain.com/docs/how_to/tool_configure/#inferring-by-parameter-type) for a given run to check the `passenger_id` of the user accessing this application. The LLM never has to provide these explicitly, they are provided for a given invocation of the graph so that each user cannot access other passengers' booking information.\n",
     "\n",
     "<div class=\"admonition warning\">\n",
     "    <p class=\"admonition-title\">Compatibility</p>\n",

docs/docs/tutorials/deployment.md

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

docs/docs/tutorials/index.md

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

docs/docs/tutorials/introduction.ipynb

@@ -5,7 +5,7 @@
    "id": "4a1aae78-88a6-4133-b905-7e46c8e3772f",
    "metadata": {},
    "source": [
-    "# 🚀 LangGraph Quickstart\n",
+    "# 🚀 LangGraph Quick Start\n",
     "\n",
     "In this tutorial, we will build a support chatbot in LangGraph that can:\n",
     "\n",

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

@@ -1,4 +1,4 @@
-# Quickstart: Launch Local LangGraph Server
+# QuickStart: Launch Local LangGraph Server
 
 This is a quick start guide to help you get a LangGraph app up and running locally.
 

docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb

@@ -1032,9 +1032,7 @@
     ")  # You can optionally add examples\n",
     "llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n",
     "\n",
-    "runnable = joiner_prompt | llm.with_structured_output(\n",
-    "    JoinOutputs, method=\"function_calling\"\n",
-    ")"
+    "runnable = joiner_prompt | llm.with_structured_output(JoinOutputs)"
    ]
   },
   {

docs/docs/tutorials/llm-compiler/math_tools.py

@@ -114,9 +114,7 @@ def get_math_tool(llm: ChatOpenAI):
             MessagesPlaceholder(variable_name="context", optional=True),
         ]
     )
-    extractor = prompt | llm.with_structured_output(
-        ExecuteCode, method="function_calling"
-    )
+    extractor = prompt | llm.with_structured_output(ExecuteCode)
 
     def calculate_expression(
         problem: str,

docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb

@@ -42,13 +42,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "53d1a740-9fea-4a6e-8f95-fb9dbf1c80a1",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install -U langchain_community tiktoken langchain-openai langchain-cohere langchainhub chromadb langchain langgraph  tavily-python"
+    "! pip install -U langchain_community tiktoken langchain-openai langchain-cohere langchainhub chromadb langchain langgraph  tavily-python"
    ]
   },
   {
@@ -68,7 +68,7 @@
     "\n",
     "\n",
     "_set_env(\"OPENAI_API_KEY\")\n",
-    "# _set_env(\"COHERE_API_KEY\")\n",
+    "_set_env(\"COHERE_API_KEY\")\n",
     "_set_env(\"TAVILY_API_KEY\")"
    ]
   },
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "b224e5ba-50ca-495a-a7fa-0f75a080e03c",
    "metadata": {},
    "outputs": [],
@@ -161,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "4dec9d98-f3dc-4b7f-abc0-9d01c754f2be",
    "metadata": {},
    "outputs": [
@@ -196,7 +196,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "structured_llm_router = llm.with_structured_output(RouteQuery)\n",
     "\n",
     "# Prompt\n",
@@ -221,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "856801cb-f42a-44e7-956f-47845e3664ca",
    "metadata": {},
    "outputs": [
@@ -229,7 +229,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "binary_score='yes'\n"
+      "binary_score='no'\n"
      ]
     }
    ],
@@ -247,7 +247,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
     "\n",
     "# Prompt\n",
@@ -271,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "2272333e-50b2-42ab-b472-e1055a3b94a8",
    "metadata": {},
    "outputs": [
@@ -279,7 +279,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Agent memory in LLM-powered autonomous systems consists of short-term and long-term memory. Short-term memory utilizes in-context learning for immediate tasks, while long-term memory allows agents to retain and recall information over extended periods, often using external storage for efficient retrieval. This memory structure supports the agent's ability to reflect on past actions and improve future performance.\n"
+      "The design of generative agents combines LLM with memory, planning, and reflection mechanisms to enable agents to behave based on past experience and interact with other agents. Memory stream is a long-term memory module that records agents' experiences in natural language. The retrieval model surfaces context to inform the agent's behavior based on relevance, recency, and importance.\n"
      ]
     }
    ],
@@ -293,7 +293,7 @@
     "prompt = hub.pull(\"rlm/rag-prompt\")\n",
     "\n",
     "# LLM\n",
-    "llm = ChatOpenAI(model_name=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
     "\n",
     "\n",
     "# Post-processing\n",
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "f0c08d14-77a0-4eed-b882-2d636abb22a3",
    "metadata": {},
    "outputs": [
@@ -321,7 +321,7 @@
        "GradeHallucinations(binary_score='yes')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -340,7 +340,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
     "\n",
     "# Prompt\n",
@@ -359,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "ded99680-437a-4c9d-b860-619c88949d84",
    "metadata": {},
    "outputs": [
@@ -369,7 +369,7 @@
        "GradeAnswer(binary_score='yes')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -388,7 +388,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
     "\n",
     "# Prompt\n",
@@ -407,17 +407,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "9d75f1d7-a47a-4577-bb0d-84b504b0867e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'What are the key concepts and techniques related to agent memory in artificial intelligence?'"
+       "\"What is the role of memory in an agent's functioning?\""
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -426,7 +426,7 @@
     "### Question Re-writer\n",
     "\n",
     "# LLM\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "\n",
     "# Prompt\n",
     "system = \"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
@@ -455,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "01d829bb-1074-4976-b650-ead41dcb9788",
    "metadata": {},
    "outputs": [],
@@ -481,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "e723fcdb-06e6-402d-912e-899795b78408",
    "metadata": {},
    "outputs": [],
@@ -516,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "b76b5ec3-0720-443d-85b1-c0e79659ca0a",
    "metadata": {},
    "outputs": [],
@@ -736,7 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "67854e07-9293-4c3c-bf9a-bc9a605570ee",
    "metadata": {},
    "outputs": [],
@@ -796,7 +796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "id": "29acc541-d726-4b75-84d1-a215845fe88a",
    "metadata": {},
    "outputs": [
@@ -816,9 +816,11 @@
       "---DECISION: GENERATION ADDRESSES QUESTION---\n",
       "\"Node 'generate':\"\n",
       "'\\n---\\n'\n",
-      "('The Chicago Bears are expected to draft quarterback Caleb Williams first '\n",
-      " 'overall in the 2024 NFL Draft. They also have a second first-round pick, '\n",
-      " 'where they selected wide receiver Rome Odunze.')\n"
+      "('It is expected that the Chicago Bears could have the opportunity to draft '\n",
+      " 'the first defensive player in the 2024 NFL draft. The Bears have the first '\n",
+      " 'overall pick in the draft, giving them a prime position to select top '\n",
+      " 'talent. The top wide receiver Marvin Harrison Jr. from Ohio State is also '\n",
+      " 'mentioned as a potential pick for the Cardinals.')\n"
      ]
     }
    ],
@@ -841,9 +843,19 @@
     "pprint(value[\"generation\"])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "11fddd00-58bf-4910-bf36-be9e5bfba778",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/7e3aa7e5-c51f-45c2-bc66-b34f17ff2263/r"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "id": "69a985dd-03c6-45af-a67b-b15746a2cb5f",
    "metadata": {},
    "outputs": [
@@ -857,7 +869,7 @@
       "\"Node 'retrieve':\"\n",
       "'\\n---\\n'\n",
       "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
-      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
       "---GRADE: DOCUMENT NOT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
@@ -872,11 +884,11 @@
       "---DECISION: GENERATION ADDRESSES QUESTION---\n",
       "\"Node 'generate':\"\n",
       "'\\n---\\n'\n",
-      "('The types of agent memory include short-term memory, long-term memory, and '\n",
-      " 'sensory memory. Short-term memory is utilized for in-context learning, while '\n",
-      " 'long-term memory allows for the retention and recall of information over '\n",
-      " 'extended periods. Sensory memory involves learning embedding representations '\n",
-      " 'for various raw inputs, such as text and images.')\n"
+      "('The types of agent memory include Sensory Memory, Short-Term Memory (STM) or '\n",
+      " 'Working Memory, and Long-Term Memory (LTM) with subtypes of Explicit / '\n",
+      " 'declarative memory and Implicit / procedural memory. Sensory memory retains '\n",
+      " 'sensory information briefly, STM stores information for cognitive tasks, and '\n",
+      " 'LTM stores information for a long time with different types of memories.')\n"
      ]
     }
    ],
@@ -894,6 +906,16 @@
     "# Final generation\n",
     "pprint(value[\"generation\"])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebf41097-fc4c-4072-95b3-e7e07731ada1",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/fdf0a180-6d15-4d09-bb92-f84f2105ca51/r"
+   ]
   }
  ],
  "metadata": {
@@ -912,7 +934,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb

@@ -261,7 +261,7 @@
     "        binary_score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n",
     "\n",
     "    # LLM\n",
-    "    model = ChatOpenAI(temperature=0, model=\"gpt-4o\", streaming=True)\n",
+    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
     "\n",
     "    # LLM with tool and validation\n",
     "    llm_with_tool = model.with_structured_output(grade)\n",
@@ -376,7 +376,7 @@
     "    prompt = hub.pull(\"rlm/rag-prompt\")\n",
     "\n",
     "    # LLM\n",
-    "    llm = ChatOpenAI(model_name=\"gpt-4o-mini\", temperature=0, streaming=True)\n",
+    "    llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, streaming=True)\n",
     "\n",
     "    # Post-processing\n",
     "    def format_docs(docs):\n",
@@ -548,7 +548,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,