Apply patch [skip ci]

2026-09-27 12:04:58 +02:00 · 2025-08-22 15:24:37 +00:00 · 2025-08-22 15:23:02 +00:00 · 2025-08-22 15:22:51 +00:00 · 2025-08-22 15:22:40 +00:00 · 2025-08-22 15:22:11 +00:00
205 changed files with 5749 additions and 11799 deletions
@@ -1,9 +1,6 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
-  - name: Documentation
-    url: https://github.com/langchain-ai/docs/issues/new?template=langgraph.yml
-    about: Report an issue related to the LangGraph documentation
  - name: LangChain Forum
    url:  https://forum.langchain.com/
-    about: General community discussions and support
+    about: General community discussions, support, and feature requests
@@ -0,0 +1,19 @@
+name: Documentation
+description: Report an issue related to the LangGraph documentation.
+title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
+labels: [documentation]
+
+body:
+- type: textarea
+  attributes: 
+    label: "Issue with current documentation:"
+    description: >
+      Please make sure to leave a reference to the document/code you're
+      referring to.
+
+- type: textarea
+  attributes:
+    label: "Idea or request for content:"
+    description: >
+      Please describe as clearly as possible what topics you think are missing
+      from the current documentation.
@@ -14,25 +14,12 @@ jobs:
        python-version:
          - "3.10"
          - "3.11"
-        example:
-          - name: A
-            workdir: libs/cli/examples
-            tag: langgraph-test-a
-          - name: B
-            workdir: libs/cli/examples/graphs
-            tag: langgraph-test-b
-          - name: C
-            workdir: libs/cli/examples/graphs_reqs_a
-            tag: langgraph-test-c
-          - name: D
-            workdir: libs/cli/examples/graphs_reqs_b
-            tag: langgraph-test-d
    name: "CLI integration test"
    defaults:
      run:
        working-directory: libs/cli
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Get changed files
        id: changed-files
        uses: Ana06/get-changed-files@v2.3.0
@@ -46,65 +33,57 @@ jobs:
          enable-cache: true
          cache-suffix: "cli-integration-test"
          ignore-nothing-to-cache: true
+      - name: Setup env
+        if: steps.changed-files.outputs.all
+        working-directory: libs/cli/examples
+        run: cat .env.example > .env
      - name: Install cli globally
        if: steps.changed-files.outputs.all
        run: pip install -e .
-      - name: Build and test service ${{ matrix.example.name }}
+      - name: Build and test service A
        if: steps.changed-files.outputs.all
-        working-directory: ${{ matrix.example.workdir }}
+        working-directory: libs/cli/examples
        env:
          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
        run: |
-          # Build the image for this example
-          langgraph build -t ${{ matrix.example.tag }}
-          # Prepare environment file from local or parent example directory
-          if [ -f .env.example ]; then cp .env.example .env; elif [ -f ../.env.example ]; then cp ../.env.example .env && cp ../.env.example ../.env; fi
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi; fi
-          # Run the integration test using the built tag
-          # Compute repo root to reference the shared script robustly
-          REPO_ROOT=$(git rev-parse --show-toplevel)
-          timeout 60 python "$REPO_ROOT/.github/scripts/run_langgraph_cli_test.py" -t ${{ matrix.example.tag }}
+          # The build-arg isn't used; just testing that we accept other args
+          langgraph build -t langgraph-test-a
+          cp .env.example .env
+          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
+          timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -c langgraph.json -t langgraph-test-a
+      - name: Build and test service B
+        if: steps.changed-files.outputs.all
+        working-directory: libs/cli/examples/graphs
+        env:
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+        run: |
+          langgraph build -t langgraph-test-b
+          cp ../.env.example .env
+          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
+          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-b
+      - name: Build and test service C
+        if: steps.changed-files.outputs.all
+        working-directory: libs/cli/examples/graphs_reqs_a
+        env:
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+        run: |
+          langgraph build -t langgraph-test-c
+          cp ../.env.example .env
+          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
+          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-c
+      - name: Build and test service D
+        if: steps.changed-files.outputs.all
+        working-directory: libs/cli/examples/graphs_reqs_b
+        env:
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+        run: |
+          langgraph build -t langgraph-test-d
+          cp ../.env.example .env
+          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
+          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-d

      - name: Build JS service
        if: steps.changed-files.outputs.all
        working-directory: libs/cli/js-examples
        run: |
          langgraph build -t langgraph-test-e
-
-      - name: Build JS monorepo service
-        if: steps.changed-files.outputs.all
-        working-directory: libs/cli/js-monorepo-example
-        run: |
-          langgraph build -t langgraph-test-f -c apps/agent/langgraph.json --build-command "yarn run turbo build" --install-command "yarn install"
-
-      - name: Build Python monorepo service
-        if: steps.changed-files.outputs.all
-        working-directory: libs/cli/python-monorepo-example
-        run: |
-          langgraph build -t langgraph-test-g -c apps/agent/langgraph.json
-          cp apps/agent/.env.example apps/agent/.env
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env; fi
-          timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-g -c apps/agent/langgraph.json
-
-      - name: Build and test prerelease reqs service
-        if: steps.changed-files.outputs.all
-        working-directory: libs/cli/examples/graph_prerelease_reqs
-        run: |
-          langgraph build -t langgraph-test-h
-          cp ../.env.example .env
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
-          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-h
-          LANGGRAPH_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langgraph'); print(v);")
-          if [ "$LANGGRAPH_VERSION" != "1.0.0a2" ]; then
-            exit 1
-          fi
-          LANGCHAIN_OPENAI_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-openai'); print(v);")
-          if [ "$LANGCHAIN_OPENAI_VERSION" != "0.3.0" ]; then
-            exit 1
-          fi
-
-      - name: Build and test prerelease reqs fail service
-        if: steps.changed-files.outputs.all
-        working-directory: libs/cli/examples/graph_prerelease_reqs_fail
-        run: |
-          langgraph build -t langgraph-test-i || [ $? -eq 1 ]
@@ -31,7 +31,7 @@ jobs:
          - "3.12"
    name: "lint #${{ matrix.python-version }}"
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Get changed files
        id: changed-files
        uses: Ana06/get-changed-files@v2.3.0
@@ -25,7 +25,7 @@ jobs:

    name: "test #${{ matrix.python-version }}"
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Set up Python ${{ matrix.python-version }}
        uses: astral-sh/setup-uv@v6
        with:
@@ -23,7 +23,7 @@ jobs:
        working-directory: libs/langgraph
    name: "test #${{ matrix.python-version }}"
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Set up Python ${{ matrix.python-version }}
        uses: astral-sh/setup-uv@v6
        with:
@@ -24,7 +24,7 @@ jobs:
      version: ${{ steps.check-version.outputs.version }}

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4

      - name: Set up Python $${ env.PYTHON_VERSION }}
        uses: astral-sh/setup-uv@v6
@@ -75,9 +75,9 @@ jobs:
      id-token: write

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4

-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v4
        with:
          name: test-dist
          path: ${{ inputs.working-directory }}/dist/
@@ -17,7 +17,7 @@ jobs:
      run:
        working-directory: libs/langgraph
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - run: SHA=$(git rev-parse HEAD) && echo "SHA=$SHA" >> $GITHUB_ENV
      - name: Set up Python 3.11
        uses: astral-sh/setup-uv@v6
@@ -15,7 +15,7 @@ jobs:
      run:
        working-directory: libs/langgraph
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - id: files
        name: Get changed files
        uses: Ana06/get-changed-files@v2.3.0
@@ -57,7 +57,7 @@ jobs:
            echo EOF
          } >> "$GITHUB_OUTPUT"
      - name: Annotation
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
        with:
          script: |
            const file = JSON.parse(`${{ steps.files.outputs.added_modified_renamed }}`)[0]
@@ -27,7 +27,7 @@ jobs:
      python: ${{ steps.filter.outputs.python }}
      deps: ${{ steps.filter.outputs.deps }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - uses: dorny/paths-filter@v3
        id: filter
        with:
@@ -100,9 +100,9 @@ jobs:
    name: "Check SDK methods matching"
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - name: Run check_sdk_methods script
@@ -118,7 +118,7 @@ jobs:
        python-version:
          - "3.11"
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Set up Python ${{ matrix.python-version }}
        uses: astral-sh/setup-uv@v6
        with:
@@ -21,7 +21,7 @@
    
        steps:
          - name: Checkout
-            uses: actions/checkout@v5
+            uses: actions/checkout@v4
    
          - name: Install Dependencies
            run: |
@@ -28,7 +28,7 @@ jobs:
    outputs:
      changed-files: ${{ steps.changed-files.outputs.added_modified }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Get changed files
        id: changed-files
        uses: Ana06/get-changed-files@v2.3.0
@@ -41,7 +41,7 @@ jobs:
    env:
      GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
        with:
          fetch-depth: 0

@@ -140,7 +140,7 @@ jobs:

      - name: Upload Pages Artifact
        # if: github.ref == 'refs/heads/main'
-        uses: actions/upload-pages-artifact@v4
+        uses: actions/upload-pages-artifact@v3
        with:
          path: ./docs/site/

@@ -19,7 +19,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
        with:
          fetch-depth: 0

@@ -36,7 +36,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
        with:
          fetch-depth: 1

@@ -12,7 +12,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Validate PR Title
-        uses: amannn/action-semantic-pull-request@v6
+        uses: amannn/action-semantic-pull-request@v5
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        with:
@@ -40,7 +40,6 @@ jobs:
            sdk-py
            docs
            ci
-            deps
          requireScope: false
          ignoreLabels: |
            ignore-lint-pr-title
@@ -26,7 +26,7 @@ jobs:
      tag: ${{ steps.check-version.outputs.tag }}

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4

      - name: Set up Python
        uses: astral-sh/setup-uv@v6
@@ -87,7 +87,7 @@ jobs:
    outputs:
      release-body: ${{ steps.generate-release-body.outputs.release-body }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
        with:
          repository: langchain-ai/langgraph
          path: langgraph
@@ -158,7 +158,7 @@ jobs:
      - test-pypi-publish
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4

      # We explicitly *don't* set up caching here. This ensures our tests are
      # maximally sensitive to catching breakage.
@@ -261,7 +261,7 @@ jobs:
        working-directory: ${{ inputs.working-directory }}

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4

      - name: Set up Python
        uses: astral-sh/setup-uv@v6
@@ -270,7 +270,7 @@ jobs:
          enable-cache: true
          cache-suffix: "release"

-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v4
        with:
          name: dist
          path: ${{ inputs.working-directory }}/dist/
@@ -302,7 +302,7 @@ jobs:
        working-directory: ${{ inputs.working-directory }}

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4

      - name: Set up Python
        uses: astral-sh/setup-uv@v6
@@ -311,7 +311,7 @@ jobs:
          enable-cache: true
          cache-suffix: "release"

-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v4
        with:
          name: dist
          path: ${{ inputs.working-directory }}/dist/
@@ -28,7 +28,7 @@ jobs:
          - "latest"

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Set up Python + Poetry
        uses: astral-sh/setup-uv@v6
        with:
@@ -16,7 +16,7 @@ jobs:
    runs-on: ubuntu-latest
    
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      
      - name: Set up uv
        uses: astral-sh/setup-uv@v6
@@ -33,8 +33,8 @@ jobs:
        uses: peter-evans/create-pull-request@v7
        with:
          token: ${{ secrets.GITHUB_TOKEN }}
-          commit-message: "chore(deps): upgrade dependencies with `uv lock --upgrade`"
-          title: "chore(deps): upgrade dependencies with `uv lock --upgrade`"
+          commit-message: "chore[deps]: upgrade dependencies with `uv lock --upgrade`"
+          title: "chore[deps]: upgrade dependencies with `uv lock --upgrade`"
          body: |
            This PR updates the dependencies in all Python packages using `uv lock --upgrade`.
            
@@ -277,9 +277,9 @@ def my_function(arg1: int, arg2: str) -> float:
    Examples:
        This is a section for examples of how to use the function.

-        ```python
-        my_function(1, "hello")
-        \```
+        .. code-block:: python
+
+            my_function(1, "hello")

    Args:
        arg1: This is a description of arg1. We do not need to specify the type since
@@ -71,7 +71,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L

 ## Additional resources

- [Guides](https://langchain-ai.github.io/langgraph/guides/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
+- [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
 - [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
 - [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
 - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
@@ -0,0 +1,47 @@
+#!/usr/bin/env python
+"""Debug why InjectedState deprecation warning is not being emitted."""
+
+from typing import Annotated
+from langgraph.prebuilt import ToolNode, InjectedState, InjectedStore
+from langgraph.prebuilt.tool_node import _get_state_args, _get_store_arg, _get_reserved_keyword_args
+from langgraph.store.base import BaseStore
+from langchain_core.tools.base import create_schema_from_function
+from langchain_core.tools import StructuredTool
+
+
+def tool_with_injected_state(x: int, state: Annotated[dict, InjectedState]) -> str:
+    """Tool using deprecated InjectedState annotation."""
+    return f"state: {state.get('foo', 'none')}"
+
+def tool_with_injected_store(x: int, store: Annotated[BaseStore, InjectedStore()]) -> str:
+    """Tool using deprecated InjectedStore annotation."""
+    return "has store"
+
+# Convert to tools
+tool1 = create_tool(tool_with_injected_state)
+tool2 = create_tool(tool_with_injected_store)
+
+print("Debugging annotation detection:")
+print(f"\nTool 1 (InjectedState):")
+print(f"  _get_state_args: {_get_state_args(tool1)}")
+print(f"  _get_reserved_keyword_args: {_get_reserved_keyword_args(tool1)}")
+
+print(f"\nTool 2 (InjectedStore):")
+print(f"  _get_store_arg: {_get_store_arg(tool2)}")
+print(f"  _get_reserved_keyword_args: {_get_reserved_keyword_args(tool2)}")
+
+# Check the condition for warnings
+state_args = _get_state_args(tool1)
+reserved_args = _get_reserved_keyword_args(tool1)
+print(f"\nTool 1 warning condition:")
+print(f"  state_args: {state_args}")
+print(f"  reserved_args.get('state'): {reserved_args.get('state')}")
+print(f"  Should warn: {bool(state_args and not reserved_args.get('state'))}")
+
+store_arg = _get_store_arg(tool2)
+reserved_args2 = _get_reserved_keyword_args(tool2)
+print(f"\nTool 2 warning condition:")
+print(f"  store_arg: {store_arg}")
+print(f"  reserved_args.get('runtime'): {reserved_args2.get('runtime')}")
+print(f"  Should warn: {bool(store_arg and not reserved_args2.get('runtime'))}")
+
@@ -0,0 +1,47 @@
+#!/usr/bin/env python
+"""Debug why InjectedState warning is not being emitted."""
+
+from typing import Annotated, get_args
+from langgraph.prebuilt import InjectedState, InjectedStore
+from langgraph.prebuilt.tool_node import get_all_basemodel_annotations, _is_injection
+from langchain_core.tools import StructuredTool
+from langgraph.store.base import BaseStore
+
+
+def tool_with_injected_state(x: int, state: Annotated[dict, InjectedState]) -> str:
+    """Tool using deprecated InjectedState annotation."""
+    return f"state: {state.get('foo', 'none')}"
+
+def tool_with_injected_store(x: int, store: Annotated[BaseStore, InjectedStore()]) -> str:
+    """Tool using deprecated InjectedStore annotation."""
+    return "has store"
+
+# Convert to tools
+tool1 = StructuredTool.from_function(tool_with_injected_state)
+tool2 = StructuredTool.from_function(tool_with_injected_store)
+
+print("Debugging annotation detection:")
+
+print(f"\nTool 1 (InjectedState):")
+schema1 = tool1.get_input_schema()
+print(f"  Schema fields: {list(schema1.__fields__.keys()) if hasattr(schema1, '__fields__') else list(schema1.model_fields.keys())}")
+
+for name, type_ in get_all_basemodel_annotations(schema1).items():
+    print(f"  Field '{name}': type={type_}")
+    args = get_args(type_)
+    print(f"    Type args: {args}")
+    for arg in args:
+        print(f"      Is InjectedState? {_is_injection(arg, InjectedState)}")
+        print(f"      Type of arg: {type(arg)}")
+
+print(f"\nTool 2 (InjectedStore):")
+schema2 = tool2.get_input_schema()
+print(f"  Schema fields: {list(schema2.__fields__.keys()) if hasattr(schema2, '__fields__') else list(schema2.model_fields.keys())}")
+
+for name, type_ in get_all_basemodel_annotations(schema2).items():
+    print(f"  Field '{name}': type={type_}")
+    args = get_args(type_)
+    print(f"    Type args: {args}")
+    for arg in args:
+        print(f"      Is InjectedStore? {_is_injection(arg, InjectedStore)}")
+        print(f"      Type of arg: {type(arg)}")
@@ -0,0 +1,49 @@
+#!/usr/bin/env python
+"""Debug reserved keyword detection for annotated tools."""
+
+from typing import Annotated
+from langgraph.prebuilt import InjectedState, InjectedStore
+from langgraph.prebuilt.tool_node import _get_reserved_keyword_args
+from langchain_core.tools import StructuredTool
+from langgraph.store.base import BaseStore
+
+
+def tool_with_injected_state(x: int, state: Annotated[dict, InjectedState]) -> str:
+    """Tool using deprecated InjectedState annotation."""
+    return f"state: {state.get('foo', 'none')}"
+
+def tool_with_injected_store(x: int, store: Annotated[BaseStore, InjectedStore()]) -> str:
+    """Tool using deprecated InjectedStore annotation."""
+    return "has store"
+
+def tool_with_reserved_state(x: int, state) -> str:
+    """Tool using reserved keyword 'state'."""
+    return f"state: {state.get('foo', 'none')}"
+
+# Convert to tools
+tool1 = StructuredTool.from_function(tool_with_injected_state)
+tool2 = StructuredTool.from_function(tool_with_injected_store)
+tool3 = StructuredTool.from_function(tool_with_reserved_state)
+
+print("Debugging reserved keyword detection:")
+
+print(f"\nTool 1 (Annotated[dict, InjectedState]):")
+reserved1 = _get_reserved_keyword_args(tool1)
+print(f"  Reserved args: {reserved1}")
+print(f"  Has 'state' as reserved? {'state' in reserved1}")
+
+print(f"\nTool 2 (Annotated[BaseStore, InjectedStore()]):")
+reserved2 = _get_reserved_keyword_args(tool2)
+print(f"  Reserved args: {reserved2}")
+print(f"  Has 'runtime' as reserved? {'runtime' in reserved2}")
+
+print(f"\nTool 3 (plain 'state' parameter):")
+reserved3 = _get_reserved_keyword_args(tool3)
+print(f"  Reserved args: {reserved3}")
+print(f"  Has 'state' as reserved? {'state' in reserved3}")
+
+# The issue might be that 'state' is being detected as a reserved keyword
+# even when it has an annotation
+print("\nConclusion:")
+print("If tool1 shows 'state' as reserved, that's the bug - it shouldn't be")
+print("considered reserved when it has an InjectedState annotation.")
@@ -1,126 +1,24 @@
-# LangGraph Documentation
+# Setup

-For more information on contributing to our documentation, see the [Contributing Guide](../CONTRIBUTING.md).
-
-## Structure
-
-The primary documentation is located in the `docs/` directory. This directory contains both the source files for the main documentation as well as the API reference doc build process.
-
-### Main Documentation
-
-Main documentation files are located in `docs/docs/` and are written in Markdown format. The site uses [**MkDocs**](https://www.mkdocs.org/) with the [Material theme](https://squidfunk.github.io/mkdocs-material/) and includes:
-
- **Concepts**: Core LangGraph concepts and explanations
- **Tutorials**: Step-by-step learning guides
- **How-tos**: Task-focused guides for specific use cases
- **Examples**: Real-world applications and use cases
- **Jupyter Notebooks**: Interactive tutorials that are automatically converted to markdown
-
-### API Reference
-
-API reference documentation is defined in `docs/docs/reference/`. Each `.md` file outlines the "template" that each page is built from. Reference content is automatically generated from docstrings in the codebase using the **mkdocstrings** plugin. Once generated, the content is plugged into the corresponding markdown file where it is referenced by using manual directives to specify which classes and/or functions are documented:
-
-```markdown
-::: langgraph.graph.state.StateGraph
-    options:
-      show_if_no_docstring: true
-      show_root_heading: true
-      show_root_full_path: false
-      members:
-        - add_node
-        - add_edge
-        - add_conditional_edges
-        - add_sequence
-        - compile
-```
-
-## Build Process
-
-Docs are built following these steps:
-
-1. **Content Processing:**
-   - `_scripts/notebook_hooks.py` - Main processing pipeline that:
-     - Converts how-tos/tutorial Jupyter notebooks to markdown using `notebook_convert.py`
-     - Adds automatic API reference links to code blocks using `generate_api_reference_links.py`
-     - Handles conditional rendering for Python/JS versions
-     - Processes highlight comments and custom syntax
-
-2. **API Reference Generation:**
-   - **mkdocstrings** plugin extracts docstrings from Python source code
-   - Manual `::: module.Class` directives in reference pages (`/docs/docs/*`) specify what to document
-   - Cross-references are automatically generated between docs and API
-
-3. **Site Generation:**
-   - **MkDocs** processes all markdown files and generates static HTML
-   - Custom hooks handle redirects and inject additional functionality
-
-4. **Deployment:**
-   - Site is deployed with Vercel
-   - `make build-docs` generates production build (also usable for local testing)
-   - Automatic redirects handle URL changes between versions
-
-### Local Development
-
-For local development, use the Makefile targets:
+To setup requirements for building docs you can run:
+
+```bash
+uv sync --group test
+```
+
+## Serving documentation locally
+
+To run the documentation server locally you can run:

 ```bash
-# Serve docs locally with hot reloading
 make serve-docs
-
-# Clean build for production testing
-make build-docs
-
-# Serve with clean build
-make serve-clean-docs
 ```

-The `serve-docs` command:
-
- Watches source files for changes
- Includes dirty builds for faster iteration
- Serves on [http://127.0.0.1:8000/langgraph/](http://127.0.0.1:8000/langgraph/)
-
-## Standards
-
-**Docstring Format:**
-The API reference uses **Google-style docstrings** with Markdown markup. The `mkdocstrings` plugin processes these to generate documentation.
-
-**Required format:**
-
-```python
-def example_function(param1: str, param2: int = 5) -> bool:
-    """Brief description of the function.
-
-    Longer description can go here. Use Markdown syntax for
-    rich formatting like **bold** and *italic*.
-
-    Args:
-        param1: Description of the first parameter.
-        param2: Description of the second parameter with default value.
-
-    Returns:
-        Description of the return value.
-
-    Raises:
-        ValueError: When param1 is empty.
-        TypeError: When param2 is not an integer.
-
-    !!! warning
-        This function is experimental and may change.
-
-    !!! version-added "Added in version 0.2.0"
-    """
-```
-
-**Special Markers:**
-
- **MkDocs admonitions**: `!!! warning`, `!!! note`, `!!! version-added`
- **Code blocks**: Standard markdown ``` syntax
- **Cross-references**: Automatic linking via `generate_api_reference_links.py`
+This will start the documentation server on [http://127.0.0.1:8000/langgraph/](http://127.0.0.1:8000/langgraph/).

 ## Execute notebooks

-If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GitHub action, you can run:
+If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:

 ```bash
 python _scripts/prepare_notebooks_for_ci.py
@@ -135,9 +33,8 @@ python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
 ```

 `prepare_notebooks_for_ci.py` script will add VCR cassette context manager for each cell in the notebook, so that:
-
- when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
- when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
+* when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
+* when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes

 ## Adding new notebooks

@@ -1,5 +1,3 @@
-"""Generate API reference links for imports in Python code blocks within markdown files."""
-
 import ast
 import importlib
 import logging
@@ -72,18 +70,8 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
    ([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
    ([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
    # other prebuilts
-    (
-        ["langgraph_supervisor"],
-        "langgraph_supervisor.supervisor",
-        "create_supervisor",
-        "supervisor",
-    ),
-    (
-        ["langgraph_supervisor"],
-        "langgraph_supervisor.handoff",
-        "create_handoff_tool",
-        "supervisor",
-    ),
+    (["langgraph_supervisor"], "langgraph_supervisor.supervisor", "create_supervisor", "supervisor"),
+    (["langgraph_supervisor"], "langgraph_supervisor.handoff", "create_handoff_tool", "supervisor"),
    ([], "langgraph_supervisor.handoff", "create_forward_message_tool", "supervisor"),
    (["langgraph_swarm"], "langgraph_swarm.swarm", "create_swarm", "swarm"),
    (["langgraph_swarm"], "langgraph_swarm.swarm", "add_active_agent_router", "swarm"),
@@ -2108,9 +2108,9 @@ __metadata:
  linkType: hard

 "hono@npm:^4.5.4":
-  version: 4.9.7
-  resolution: "hono@npm:4.9.7"
-  checksum: 10c0/089184660a9211ea216ab95bafa45260e371651cb019db49828064b7982b0ae61cc3c4715324bfeb9037aa2460c39ffa2c91d84ad0c8d500fa77cbcc7fc07a8f
+  version: 4.8.9
+  resolution: "hono@npm:4.8.9"
+  checksum: 10c0/385539d1787fdc747bc869ef0e5ccc9f39cbe40289b94f23eecfc82c6ca440f059704647cd6381a5066d2cf7baa43ab25184c78d44af4c5c98a5c5b07670059e
  languageName: node
  linkType: hard

@@ -1,5 +1,3 @@
-"""Convert Jupyter notebooks to markdown with custom processing."""
-
 import ast
 import os
 import re
@@ -144,13 +144,13 @@ REDIRECT_MAP = {
    "concepts/langgraph_cli.md": "https://docs.langchain.com/langgraph-platform/langgraph-cli",
    "concepts/langgraph_studio.md": "https://docs.langchain.com/langgraph-platform/langgraph-studio",
    "cloud/how-tos/studio/quick_start.md": "https://docs.langchain.com/langgraph-platform/quick-start-studio",
-    "cloud/how-tos/invoke_studio.md": "https://docs.langchain.com/langgraph-platform/use-studio#run-application",
-    "cloud/how-tos/studio/manage_assistants.md": "https://docs.langchain.com/langgraph-platform/use-studio#manage-assistants",
-    "cloud/how-tos/threads_studio.md": "https://docs.langchain.com/langgraph-platform/use-studio#manage-threads",
-    "cloud/how-tos/iterate_graph_studio.md": "https://docs.langchain.com/langgraph-platform/observability-studio#iterate-on-prompts",
-    "cloud/how-tos/studio/run_evals.md": "https://docs.langchain.com/langgraph-platform/observability-studio#run-experiments-over-a-dataset",
-    "cloud/how-tos/clone_traces_studio.md": "https://docs.langchain.com/langgraph-platform/observability-studio#debug-langsmith-traces",
-    "cloud/how-tos/datasets_studio.md": "https://docs.langchain.com/langgraph-platform/observability-studio#add-node-to-dataset",
+    "cloud/how-tos/invoke_studio.md": "https://docs.langchain.com/langgraph-platform/invoke-studio",
+    "cloud/how-tos/studio/manage_assistants.md": "https://docs.langchain.com/langgraph-platform/manage-assistants-studio",
+    "cloud/how-tos/threads_studio.md": "https://docs.langchain.com/langgraph-platform/threads-studio",
+    "cloud/how-tos/iterate_graph_studio.md": "https://docs.langchain.com/langgraph-platform/iterate-graph-studio",
+    "cloud/how-tos/studio/run_evals.md": "https://docs.langchain.com/langgraph-platform/run-evals-studio",
+    "cloud/how-tos/clone_traces_studio.md": "https://docs.langchain.com/langgraph-platform/clone-traces-studio",
+    "cloud/how-tos/datasets_studio.md": "https://docs.langchain.com/langgraph-platform/datasets-studio",
    "concepts/sdk.md": "https://docs.langchain.com/langgraph-platform/sdk",
    "concepts/plans.md": "https://docs.langchain.com/langgraph-platform/plans",
    "concepts/application_structure.md": "https://docs.langchain.com/langgraph-platform/application-structure",
@@ -33,7 +33,7 @@ LangGraph provides three ways to manage context, which combines the mutability a

 **Static runtime context** represents immutable data like user metadata, tools, and database connections that are passed to an application at the start of a run via the `context` argument to `invoke`/`stream`. This data does not change during execution.

-!!! version-added "Added in version 0.6.0: `context` replaces `config['configurable']`"
+!!! version-added "New in LangGraph v0.6: `context` replaces `config['configurable']`"

    Runtime context is now passed to the `context` argument of `invoke`/`stream`,
    which replaces the previous pattern of passing application configuration to `config['configurable']`.
@@ -90,7 +90,7 @@ graph.invoke( # (1)!
    from langgraph.runtime import Runtime

    # highlight-next-line
-    def node(state: State, runtime: Runtime[ContextSchema]):
+    def node(state: State, config: Runtime[ContextSchema]):
        user_name = runtime.context.user_name
        ...
    ```
@@ -211,7 +211,7 @@ output = agent.invoke(
 print(output["messages"][-1].text())
 ```

-!!! version-added "Added in version 0.6.0"
+!!! version-added "New in LangGraph v0.6"

 :::

@@ -351,13 +351,11 @@ If your desired LLM isn't officially supported by LangChain, consider these opti
 :::python

 1. **Implement a custom LangChain chat model**: Create a model conforming to the [LangChain chat model interface](https://python.langchain.com/docs/how_to/custom_chat_model/). This enables full compatibility with LangGraph's agents and workflows but requires understanding of the LangChain framework.
-
   :::

 :::js

 1. **Implement a custom LangChain chat model**: Create a model conforming to the [LangChain chat model interface](https://js.langchain.com/docs/how_to/custom_chat/). This enables full compatibility with LangGraph's agents and workflows but requires understanding of the LangChain framework.
-
   :::

 2. **Direct invocation with custom streaming**: Use your model directly by [adding custom streaming logic](../how-tos/streaming.md#use-with-any-llm) with `StreamWriter`.
@@ -373,7 +371,6 @@ If your desired LLM isn't officially supported by LangChain, consider these opti
 - [Force model to call a specific tool](https://python.langchain.com/docs/how_to/tool_choice/)
 - [All chat model how-to guides](https://python.langchain.com/docs/how_to/#chat-models)
 - [Chat model integrations](https://python.langchain.com/docs/integrations/chat/)
-
  :::

 :::js
@@ -384,5 +381,4 @@ If your desired LLM isn't officially supported by LangChain, consider these opti
 - [Force model to call a specific tool](https://js.langchain.com/docs/how_to/tool_choice/)
 - [All chat model how-to guides](https://js.langchain.com/docs/how_to/#chat-models)
 - [Chat model integrations](https://js.langchain.com/docs/integrations/chat/)
-
  :::
@@ -384,7 +384,7 @@ Putting this together, here is how you can implement a simple multi-agent system
 ```python
 from typing import Annotated
 from langchain_core.tools import tool, InjectedToolCallId
-from langgraph.prebuilt import create_react_agent, InjectedState
+from langgraph.prebuilt import create_react_agent
 from langgraph.graph import StateGraph, START, MessagesState
 from langgraph.types import Command

@@ -395,7 +395,7 @@ def create_handoff_tool(*, agent_name: str, description: str | None = None):
    @tool(name, description=description)
    def handoff_tool(
        # highlight-next-line
-        state: Annotated[MessagesState, InjectedState], # (1)!
+        state,  # (1)! Reserved keyword - automatically injected
        # highlight-next-line
        tool_call_id: Annotated[str, InjectedToolCallId],
    ) -> Command:
@@ -636,3 +636,5 @@ Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraph-super
 :::js
 Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-supervisor#customizing-handoff-tools) and [swarm](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-swarm#customizing-handoff-tools) documentation to learn how to customize handoffs.
 :::
+
+
@@ -99,8 +99,8 @@ Starting from the `LangGraph Platform` view...
 1. In the top-right corner, select the gear icon (`Deployment Settings`).
 1. Update the `Git Branch` to the desired branch.
 1. Check/uncheck checkbox to `Automatically update deployment on push to branch`.
-1. Branch creation/deletion and tag creation/deletion events will not trigger an update. Only pushes to an existing branch will trigger an update.
-1. Pushes in quick succession to a branch will queue subsequent updates. Once a build completes, the most recent commit will begin building and the other queued builds will be skipped.
+    1. Branch creation/deletion and tag creation/deletion events will not trigger an update. Only pushes to an existing branch will trigger an update.
+    1. Pushes in quick succession to a branch will not trigger subsequent updates. In the future, this functionality may be changed/improved.

 ## Add or Remove GitHub Repositories

@@ -29,10 +29,6 @@
      "name": "Store",
      "description": "Store is an API for managing persistent key-value store (long-term memory) that is available from any thread."
    },
-    {
-      "name": "A2A",
-      "description": "Agent-to-Agent Protocol related endpoints for exposing assistants as A2A-compliant agents."
-    },
    {
      "name": "MCP",
      "description": "Model Context Protocol related endpoints for exposing an agent as an MCP server."
@@ -1524,96 +1520,6 @@
        }
      }
    },
-    "/threads/{thread_id}/stream": {
-      "get": {
-        "tags": [
-          "Threads"
-        ],
-        "summary": "Join Thread Stream",
-        "description": "This endpoint streams output in real-time from a thread. The stream will include the output of each run executed sequentially on the thread and will remain open indefinitely. It is the responsibility of the calling client to close the connection.",
-        "operationId": "join_thread_stream_threads__thread_id__stream_get",
-        "parameters": [
-          {
-            "description": "The ID of the thread.",
-            "required": true,
-            "schema": {
-              "type": "string",
-              "format": "uuid",
-              "title": "Thread Id",
-              "description": "The ID of the thread."
-            },
-            "name": "thread_id",
-            "in": "path"
-          },
-          {
-            "required": false,
-            "schema": {
-              "type": "string",
-              "title": "Last Event ID",
-              "description": "The ID of the last event received. Used to resume streaming from a specific point. Pass '-' to resume from the beginning."
-            },
-            "name": "Last-Event-ID",
-            "in": "header"
-          },
-          {
-            "required": false,
-            "schema": {
-              "anyOf": [
-                {
-                  "type": "string",
-                  "enum": ["lifecycle", "run_modes", "state_update"]
-                },
-                {
-                  "type": "array",
-                  "items": {
-                    "type": "string",
-                    "enum": ["lifecycle", "run_modes", "state_update"]
-                  }
-                }
-              ],
-              "default": ["run_modes"],
-              "title": "Stream Modes",
-              "description": "Stream modes to control which events are returned. 'lifecycle' returns only run start/end events, 'run_modes' returns all run events (default behavior), 'state_update' returns only state update events."
-            },
-            "name": "stream_modes",
-            "in": "query"
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Success",
-            "content": {
-              "text/event-stream": {
-                "schema": {
-                  "type": "string",
-                  "description": "The server will send a stream of events in SSE format.\n\n**Example event**:\n\nid: 1\n\nevent: message\n\ndata: {}"
-                }
-              }
-            }
-          },
-          "404": {
-            "description": "Not Found",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          }
-        }
-      }
-    },
    "/threads/{thread_id}/runs": {
      "get": {
        "tags": [
@@ -3186,195 +3092,6 @@
        }
      }
    },
-    "/a2a/{assistant_id}": {
-      "post": {
-        "operationId": "post_a2a",
-        "summary": "A2A Post",
-        "description": "Communicate with an assistant using the Agent-to-Agent Protocol.\nSends a JSON-RPC 2.0 message to the assistant.\n\n- **Request**: Provide an object with `jsonrpc`, `id`, `method`, and optional `params`.\n- **Response**: Returns a JSON-RPC response with task information or error.\n\n**Supported Methods:**\n- `message/send`: Send a message to the assistant\n- `tasks/get`: Get the status and result of a task\n\n**Notes:**\n- Supports threaded conversations via thread context\n- Messages can contain text and data parts\n- Tasks run asynchronously and return completion status\n",
-        "parameters": [
-          {
-            "name": "assistant_id",
-            "in": "path",
-            "required": true,
-            "schema": {
-              "type": "string",
-              "format": "uuid"
-            },
-            "description": "The ID of the assistant to communicate with"
-          },
-          {
-            "name": "Accept",
-            "in": "header",
-            "required": true,
-            "schema": {
-              "type": "string",
-              "enum": ["application/json"]
-            },
-            "description": "Must be application/json"
-          }
-        ],
-        "requestBody": {
-          "required": true,
-          "content": {
-            "application/json": {
-              "schema": {
-                "type": "object",
-                "properties": {
-                  "jsonrpc": {
-                    "type": "string",
-                    "enum": ["2.0"],
-                    "description": "JSON-RPC version"
-                  },
-                  "id": {
-                    "type": "string",
-                    "description": "Request identifier"
-                  },
-                  "method": {
-                    "type": "string",
-                    "enum": ["message/send", "tasks/get"],
-                    "description": "The method to invoke"
-                  },
-                  "params": {
-                    "type": "object",
-                    "description": "Method parameters",
-                    "oneOf": [
-                      {
-                        "title": "Message Send Parameters",
-                        "properties": {
-                          "message": {
-                            "type": "object",
-                            "properties": {
-                              "role": {
-                                "type": "string",
-                                "enum": ["user", "assistant"],
-                                "description": "Message role"
-                              },
-                              "parts": {
-                                "type": "array",
-                                "items": {
-                                  "oneOf": [
-                                    {
-                                      "title": "Text Part",
-                                      "type": "object",
-                                      "properties": {
-                                        "kind": {
-                                          "type": "string",
-                                          "enum": ["text"]
-                                        },
-                                        "text": {
-                                          "type": "string"
-                                        }
-                                      },
-                                      "required": ["kind", "text"]
-                                    },
-                                    {
-                                      "title": "Data Part", 
-                                      "type": "object",
-                                      "properties": {
-                                        "kind": {
-                                          "type": "string",
-                                          "enum": ["data"]
-                                        },
-                                        "data": {
-                                          "type": "object"
-                                        }
-                                      },
-                                      "required": ["kind", "data"]
-                                    }
-                                  ]
-                                },
-                                "description": "Message parts"
-                              },
-                              "messageId": {
-                                "type": "string",
-                                "description": "Unique message identifier"
-                              }
-                            },
-                            "required": ["role", "parts", "messageId"]
-                          },
-                          "thread": {
-                            "type": "object",
-                            "properties": {
-                              "threadId": {
-                                "type": "string",
-                                "description": "Thread identifier for conversation context"
-                              }
-                            },
-                            "description": "Optional thread context"
-                          }
-                        },
-                        "required": ["message"]
-                      },
-                      {
-                        "title": "Task Get Parameters",
-                        "properties": {
-                          "taskId": {
-                            "type": "string",
-                            "description": "Task identifier to retrieve"
-                          }
-                        },
-                        "required": ["taskId"]
-                      }
-                    ]
-                  }
-                },
-                "required": ["jsonrpc", "id", "method"]
-              }
-            }
-          }
-        },
-        "responses": {
-          "200": {
-            "description": "JSON-RPC response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "type": "object",
-                  "properties": {
-                    "jsonrpc": {
-                      "type": "string",
-                      "enum": ["2.0"]
-                    },
-                    "id": {
-                      "type": "string"
-                    },
-                    "result": {
-                      "type": "object",
-                      "description": "Success result containing task information or task details"
-                    },
-                    "error": {
-                      "type": "object",
-                      "properties": {
-                        "code": {
-                          "type": "integer"
-                        },
-                        "message": {
-                          "type": "string"
-                        }
-                      },
-                      "description": "Error information if request failed"
-                    }
-                  },
-                  "required": ["jsonrpc", "id"]
-                }
-              }
-            }
-          },
-          "400": {
-            "description": "Bad request - invalid JSON-RPC or missing Accept header"
-          },
-          "404": {
-            "description": "Assistant not found"
-          },
-          "500": {
-            "description": "Internal server error"
-          }
-        },
-        "tags": [
-          "A2A"
-        ]
-      }
-    },
    "/mcp/": {
      "post": {
        "operationId": "post_mcp",
@@ -4629,17 +4346,6 @@
            "title": "Checkpoint During",
            "description": "Whether to checkpoint during the run.",
            "default": false
-          },
-          "durability": {
-            "type": "string",
-            "enum": [
-              "sync",
-              "async",
-              "exit"
-            ],
-            "title": "Durability",
-            "description": "Durability level for the run. Must be one of 'sync', 'async', or 'exit'.",
-            "default": "async"
          }
        },
        "type": "object",
@@ -4876,17 +4582,6 @@
            "title": "Checkpoint During",
            "description": "Whether to checkpoint during the run.",
            "default": false
-          },
-          "durability": {
-            "type": "string",
-            "enum": [
-              "sync",
-              "async",
-              "exit"
-            ],
-            "title": "Durability",
-            "description": "Durability level for the run. Must be one of 'sync', 'async', or 'exit'.",
-            "default": "async"
          }
        },
        "type": "object",
@@ -5015,12 +4710,6 @@
      },
      "ThreadSearchRequest": {
        "properties": {
-          "ids": {
-            "type": "array",
-            "items": {"type": "string", "format": "uuid"},
-            "title": "Ids",
-            "description": "List of thread IDs to include. Others are excluded."
-          },
          "metadata": {
            "type": "object",
            "title": "Metadata",
@@ -5261,30 +4950,11 @@
            "type": "object",
            "title": "Metadata",
            "description": "Metadata to merge with existing thread metadata."
-          },
-          "ttl": {
-            "type": "object",
-            "title": "TTL",
-            "description": "The time-to-live for the thread.",
-            "properties": {
-              "strategy": {
-                "type": "string",
-                "enum": [
-                  "delete"
-                ],
-                "description": "The TTL strategy. 'delete' removes the entire thread.",
-                "default": "delete"
-              },
-              "ttl": {
-                "type": "number",
-                "description": "The time-to-live in minutes from now until thread should be swept."
-              }
-            }
          }
        },
        "type": "object",
        "title": "ThreadPatch",
-        "description": "Payload for updating a thread."
+        "description": "Payload for creating a thread."
      },
      "ThreadStateCheckpointRequest": {
        "properties": {
@@ -483,19 +483,19 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](

    RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn

-    ADD ./graphs /deps/outer-graphs/src
+    ADD ./graphs /deps/__outer_graphs/src
    RUN set -ex && \
        for line in '[project]' \
                    'name = "graphs"' \
                    'version = "0.1"' \
                    '[tool.setuptools.package-data]' \
                    '"*" = ["**/*"]'; do \
-            echo "$line" >> /deps/outer-graphs/pyproject.toml; \
+            echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
        done

    RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*

-    ENV LANGSERVE_GRAPHS='{"agent": "/deps/outer-graphs/src/agent.py:graph", "storm": "/deps/outer-graphs/src/storm.py:graph"}'
+    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"
@@ -1040,7 +1040,7 @@ def node_a(state: State, runtime: Runtime[ContextSchema]):
    ...
 ```

-See [this guide](../how-tos/graph-api.md#add-runtime-configuration) for a full breakdown on configuration.
+See [this guide](../how-tos/graph-api.ipynb#add-runtime-configuration) for a full breakdown on configuration.
 :::

 :::js
@@ -134,7 +134,7 @@ def update_instructions(state: State, store: BaseStore):
    namespace = ("instructions",)
    current_instructions = store.search(namespace)[0]
    # Memory logic
-    prompt = prompt_template.format(instructions=current_instructions.value["instructions"], conversation=state["messages"])
+    prompt = prompt_template.format(instructions=instructions.value["instructions"], conversation=state["messages"])
    output = llm.invoke(prompt)
    new_instructions = output['new_instructions']
    store.put(("agent_instructions",), "agent_a", {"instructions": new_instructions})
@@ -278,4 +278,4 @@ const items = await store.search(
 ```
 :::

-For more information about the memory store, see the [Persistence](persistence.md#memory-store) guide.
+For more information about the memory store, see the [Persistence](persistence.md#memory-store) guide.
@@ -498,15 +498,14 @@ In this variant of the [supervisor](#supervisor) architecture, we define a super
 :::python

 ```python
-from typing import Annotated
 from langchain_openai import ChatOpenAI
-from langgraph.prebuilt import InjectedState, create_react_agent
+from langgraph.prebuilt import create_react_agent

 model = ChatOpenAI()

 # this is the agent function that will be called as tool
-# notice that you can pass the state to the tool via InjectedState annotation
-def agent_1(state: Annotated[dict, InjectedState]):
+# notice that you can pass the state to the tool via the reserved keyword 'state'
+def agent_1(state):  # 'state' is a reserved keyword - automatically injected
    # you can pass relevant parts of the state to the LLM (e.g., state["messages"])
    # and add any additional logic (different models, custom prompts, structured output, etc.)
    response = model.invoke(...)
@@ -515,7 +514,7 @@ def agent_1(state: Annotated[dict, InjectedState]):
    # by the prebuilt create_react_agent (supervisor)
    return response.content

-def agent_2(state: Annotated[dict, InjectedState]):
+def agent_2(state):  # 'state' is a reserved keyword - automatically injected
    response = model.invoke(...)
    return response.content

@@ -897,5 +896,6 @@ There are two high-level approaches to achieve that:

 An agent might need to have a different state schema from the rest of the agents. For example, a search agent might only need to keep track of queries and retrieved documents. There are two ways to achieve this in LangGraph:

- Define [subgraph](./subgraphs.md) agents with a separate state schema. If there are no shared state keys (channels) between the subgraph and the parent graph, it's important to [add input / output transformations](../how-tos/subgraph.md#different-state-schemas) so that the parent graph knows how to communicate with the subgraphs.
- Define agent node functions with a [private input state schema](../how-tos/graph-api.md#pass-private-state-between-nodes) that is distinct from the overall graph state schema. This allows passing information that is only needed for executing that particular agent.
+- Define [subgraph](./subgraphs.md) agents with a separate state schema. If there are no shared state keys (channels) between the subgraph and the parent graph, it's important to [add input / output transformations](../how-tos/subgraph.ipynb#different-state-schemas) so that the parent graph knows how to communicate with the subgraphs.
+- Define agent node functions with a [private input state schema](../how-tos/graph-api.ipynb#pass-private-state-between-nodes) that is distinct from the overall graph state schema. This allows passing information that is only needed for executing that particular agent.
+
@@ -1019,7 +1019,7 @@ console.log(await graph.invoke({}, { configurable: { myRuntimeValue: "b" } }));
    # Usage
    input_message = {"role": "user", "content": "hi"}
    # With no configuration, uses default (Anthropic)
-    response_1 = graph.invoke({"messages": [input_message]}, context=ContextSchema())["messages"][-1]
+    response_1 = graph.invoke({"messages": [input_message]})["messages"][-1]
    # Or, can set OpenAI
    response_2 = graph.invoke({"messages": [input_message]}, context={"model_provider": "openai"})["messages"][-1]

@@ -1205,7 +1205,7 @@ There are many use cases where you may wish for your node to have a custom retry
 To configure a retry policy, pass the `retry_policy` parameter to the [add_node](../reference/graphs.md#langgraph.graph.state.StateGraph.add_node). The `retry_policy` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters and associate it with a node:

 ```python
-from langgraph.types import RetryPolicy
+from langgraph.pregel import RetryPolicy

 builder.add_node(
    "node_name",
@@ -1260,7 +1260,7 @@ By default, the retry policy retries on any exception except for the following:
    from typing_extensions import TypedDict
    from langchain.chat_models import init_chat_model
    from langgraph.graph import END, MessagesState, StateGraph, START
-    from langgraph.types import RetryPolicy
+    from langgraph.pregel import RetryPolicy
    from langchain_community.utilities import SQLDatabase
    from langchain_core.messages import AIMessage

@@ -1422,15 +1422,15 @@ const builder = new StateGraph(State)
 :::

 ??? info "Why split application steps into a sequence with LangGraph?"
-    LangGraph makes it easy to add an underlying persistence layer to your application.
-    This allows state to be checkpointed in between the execution of nodes, so your LangGraph nodes govern:
+LangGraph makes it easy to add an underlying persistence layer to your application.
+This allows state to be checkpointed in between the execution of nodes, so your LangGraph nodes govern:

- How state updates are [checkpointed](../concepts/persistence.md)
- How interruptions are resumed in [human-in-the-loop](../concepts/human_in_the_loop.md) workflows
- How we can "rewind" and branch-off executions using LangGraph's [time travel](../concepts/time-travel.md) features
+    - How state updates are [checkpointed](../concepts/persistence.md)
+    - How interruptions are resumed in [human-in-the-loop](../concepts/human_in_the_loop.md) workflows
+    - How we can "rewind" and branch-off executions using LangGraph's [time travel](../concepts/time-travel.md) features

-They also determine how execution steps are [streamed](../concepts/streaming.md), and how your application is visualized
-and debugged using [LangGraph Studio](../concepts/langgraph_studio.md).
+    They also determine how execution steps are [streamed](../concepts/streaming.md), and how your application is visualized
+    and debugged using [LangGraph Studio](../concepts/langgraph_studio.md).

 Let's demonstrate an end-to-end example. We will create a sequence of three steps:

@@ -2110,6 +2110,7 @@ builder.add_edge(START, "generate_topics")
 builder.add_conditional_edges("generate_topics", continue_to_jokes, ["generate_joke"])
 builder.add_edge("generate_joke", "best_joke")
 builder.add_edge("best_joke", END)
+builder.add_edge("generate_topics", END)
 graph = builder.compile()
 ```

@@ -2332,7 +2333,7 @@ from IPython.display import Image, display
 display(Image(graph.get_graph().draw_mermaid_png()))
 ```

-![Simple loop graph](assets/graph_api_image_7.png)
+![Simple loop graph](assets/graph_api_image_3.png)
 :::

 :::js
@@ -3271,7 +3272,7 @@ from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeSt
 display(Image(app.get_graph().draw_mermaid_png()))
 ```

-![Fractal graph visualization](assets/graph_api_image_10.png)
+![Fractal graph visualization](assets/graph_api_image_5.png)

 **Using Mermaid + Pyppeteer**

@@ -3319,4 +3320,4 @@ const imageBuffer = new Uint8Array(await image.arrayBuffer());

 await fs.writeFile("graph.png", imageBuffer);
 ```
-:::
+:::
@@ -366,8 +366,8 @@ result = graph.invoke(

 # Resume with mapping of interrupt IDs to values
 resume_map = {
-    i.id: f"edited text for {i.value['text_to_revise']}"
-    for i in graph.get_state(config).interrupts
+    i.interrupt_id: f"human input for prompt {i.value}"
+    for i in parent.get_state(thread_config).interrupts
 }
 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'}
@@ -26,7 +26,7 @@ To implement handoffs, you can return `Command` objects from your agent nodes or
 ```python
 from typing import Annotated
 from langchain_core.tools import tool, InjectedToolCallId
-from langgraph.prebuilt import create_react_agent, InjectedState
+from langgraph.prebuilt import create_react_agent
 from langgraph.graph import StateGraph, START, MessagesState
 from langgraph.types import Command

@@ -37,7 +37,7 @@ def create_handoff_tool(*, agent_name: str, description: str | None = None):
    @tool(name, description=description)
    def handoff_tool(
        # highlight-next-line
-        state: Annotated[MessagesState, InjectedState], # (1)!
+        state,  # (1)! Reserved keyword - automatically injected
        # highlight-next-line
        tool_call_id: Annotated[str, InjectedToolCallId],
    ) -> Command:
@@ -58,7 +58,7 @@ def create_handoff_tool(*, agent_name: str, description: str | None = None):
    return handoff_tool
 ```

-1. Access the [state](../concepts/low_level.md#state) of the agent that is calling the handoff tool using the @[InjectedState] annotation. 
+1. Access the [state](../concepts/low_level.md#state) of the agent using the reserved keyword `state`. No annotation needed - LangGraph automatically injects the state when it sees this parameter name. 
 2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.
 3. Name of the agent or node to hand off to.
 4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
@@ -183,7 +183,6 @@ You can use the @[`Send()`][Send] primitive to directly send data to the worker

 from typing import Annotated
 from langchain_core.tools import tool, InjectedToolCallId
-from langgraph.prebuilt import InjectedState
 from langgraph.graph import StateGraph, START, MessagesState
 # highlight-next-line
 from langgraph.types import Command, Send
@@ -202,7 +201,7 @@ def create_task_description_handoff_tool(
            "Description of what the next agent should do, including all of the relevant context.",
        ],
        # these parameters are ignored by the LLM
-        state: Annotated[MessagesState, InjectedState],
+        state,  # Reserved keyword - automatically injected
    ) -> Command:
        task_description_message = {"role": "user", "content": task_description}
        agent_input = {**state, "messages": [task_description_message]}
@@ -382,7 +381,7 @@ const multiAgentGraph = new StateGraph(MessagesZodState)
    from typing import Annotated
    from langchain_core.messages import convert_to_messages
    from langchain_core.tools import tool, InjectedToolCallId
-    from langgraph.prebuilt import create_react_agent, InjectedState
+    from langgraph.prebuilt import create_react_agent
    from langgraph.graph import StateGraph, START, MessagesState
    from langgraph.types import Command
    
@@ -435,7 +434,7 @@ const multiAgentGraph = new StateGraph(MessagesZodState)
        @tool(name, description=description)
        def handoff_tool(
            # highlight-next-line
-            state: Annotated[MessagesState, InjectedState], # (1)!
+            state,  # (1)! Reserved keyword - automatically injected
            # highlight-next-line
            tool_call_id: Annotated[str, InjectedToolCallId],
        ) -> Command:
@@ -789,7 +788,7 @@ function agent(state: MessagesState): Command {
    ```python
    from langchain_anthropic import ChatAnthropic
    from langgraph.graph import MessagesState, StateGraph, START
-    from langgraph.prebuilt import create_react_agent, InjectedState
+    from langgraph.prebuilt import create_react_agent
    from langgraph.types import Command, interrupt
    from langgraph.checkpoint.memory import InMemorySaver
    
@@ -1250,4 +1249,9 @@ LangGraph comes with prebuilt implementations of two of the most popular multi-a
 :::js
 - [supervisor](../agents/multi-agent.md#supervisor) — individual agents are coordinated by a central supervisor agent. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. You can use [`langgraph-supervisor`](https://github.com/langchain-ai/langgraph-supervisor-js) library to create a supervisor multi-agent systems.
 - [swarm](../agents/multi-agent.md#supervisor) — agents dynamically hand off control to one another based on their specializations. The system remembers which agent was last active, ensuring that on subsequent interactions, the conversation resumes with that agent. You can use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-js) library to create a swarm multi-agent systems.
-:::
+:::
+
+
+
+
+
@@ -244,7 +244,7 @@ output = agent.invoke(
 print(output["messages"][-1].text())
 ```

-!!! version-added "Added in version 0.6.0"
+!!! version-added "New in langgraph>=0.6"

 :::

@@ -1141,12 +1141,12 @@ await agent.invoke(
 Short-term memory maintains **dynamic** state that changes during a single execution.

 :::python
-To **access** (read) the graph state inside the tools, you can use a special parameter **annotation** — @[`InjectedState`][InjectedState]:
+To **access** (read) the graph state inside the tools, you can use the reserved keyword parameter `state`:

 ```python
-from typing import Annotated, NotRequired
+from typing import NotRequired
 from langchain_core.tools import tool
-from langgraph.prebuilt import InjectedState, create_react_agent
+from langgraph.prebuilt import create_react_agent
 from langgraph.prebuilt.chat_agent_executor import AgentState

 class CustomState(AgentState):
@@ -1156,7 +1156,7 @@ class CustomState(AgentState):
@tool
 def get_user_name(
    # highlight-next-line
-    state: Annotated[CustomState, InjectedState]
+    state  # Reserved keyword - automatically injected
 ) -> str:
    """Retrieve the current user-name from state."""
    # Return stored name or a default if not set
@@ -1173,6 +1173,9 @@ agent = create_react_agent(
 agent.invoke({"messages": "what's my name?"})
 ```

+!!! note "Migration from Annotations"
+    The old annotation-based approach using `Annotated[CustomState, InjectedState]` is deprecated but still supported for backward compatibility. Simply use the reserved keyword `state` without any type annotation for cleaner code.
+
 :::

 :::js
@@ -2387,3 +2390,4 @@ Some commonly used tool categories include:

 These integrations can be configured and added to your agents using the same `tools` parameter shown in the examples above.
 :::
+
@@ -383,7 +383,6 @@ We will implement handoffs via **handoff tools** and give these tools to the sup
 ```python
 from typing import Annotated
 from langchain_core.tools import tool, InjectedToolCallId
-from langgraph.prebuilt import InjectedState
 from langgraph.graph import StateGraph, START, MessagesState
 from langgraph.types import Command

@@ -394,7 +393,7 @@ def create_handoff_tool(*, agent_name: str, description: str | None = None):

    @tool(name, description=description)
    def handoff_tool(
-        state: Annotated[MessagesState, InjectedState],
+        state,  # Reserved keyword - automatically injected
        tool_call_id: Annotated[str, InjectedToolCallId],
    ) -> Command:
        tool_message = {
@@ -644,7 +643,7 @@ def create_task_description_handoff_tool(
            "Description of what the next agent should do, including all of the relevant context.",
        ],
        # these parameters are ignored by the LLM
-        state: Annotated[MessagesState, InjectedState],
+        state,  # Reserved keyword - automatically injected
    ) -> Command:
        task_description_message = {"role": "user", "content": task_description}
        agent_input = {**state, "messages": [task_description_message]}
@@ -766,3 +765,5 @@ Update from subgraph research_agent:
 	
 	{"query": "2024 United States GDP value from a reputable source", "follow_up_questions": null, "answer": null, "images": [], "results": [{"url": "https://www.focus-economics.com/countries/united-states/", "title": "United States Economy Overview - Focus Economics", "content": "The United States' Macroeconomic Analysis:\n------------------------------------------\n\n**Nominal GDP of USD 29,185 billion in 2024.**\n\n**Nominal GDP of USD 29,179 billion in 2024.**\n\n**GDP per capita of USD 86,635 compared to the global average of USD 10,589.**\n\n**GDP per capita of USD 86,652 compared to the global average of USD 10,589.**\n\n**Average real GDP growth of 2.5% over the last decade.**\n\n**Average real GDP growth of ```
 ``` 
+
+
@@ -1948,7 +1948,7 @@ const llmWithTools = llm.bindTools(tools);


    # Conditional edge function to route to the tool node or end based upon whether the LLM made a tool call
-    def should_continue(state: MessagesState) -> Literal["Action", END]:
+    def should_continue(state: MessagesState) -> Literal["environment", END]:
        """Decide if we should continue the loop or stop based upon whether the LLM made a tool call"""

        messages = state["messages"]
@@ -291,7 +291,7 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
    }

    .md-banner {
-      background-color: #FFAE42;
+      background-color: #CFC9FA;
      color: #000000;
    }

@@ -360,5 +360,5 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
 {% endblock %}

 {% block announce %}
-These docs will be deprecated and removed with the release of LangGraph v1.0 in October 2025. <a href="https://docs.langchain.com/oss/python/langgraph/overview" target="_blank">Visit the v1.0 alpha docs</a>
+Our new LangChain Academy Course Deep Research with LangGraph is now live! <a href="https://academy.langchain.com/courses/deep-research-with-langgraph/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2025_deep-research-course_co" target="_blank">Enroll for free</a>.
 {% endblock %}
@@ -7,14 +7,14 @@ name = "langgraph-docs"
 version = "0.0.1"
 description = "LangGraph docs"
 authors = []
-requires-python = ">=3.11.0,<4.0.0"
+requires-python = "~=3.11"
 readme = "README.md"
 license = "MIT"
 dependencies = [
    "aiohappyeyeballs==2.4.3",
-    "hub>=3.0.1,<4.0.0",
-    "xxhash>=3.5.0,<4.0.0",
-    "black>=25.1.0,<26.0.0",
+    "hub>=3.0.1,<4",
+    "xxhash>=3.5.0,<4",
+    "black>=25.1.0,<26",
 ]

 [dependency-groups]
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.11, <4"
 resolution-markers = [
    "python_full_version >= '3.13' and platform_python_implementation != 'PyPy'",
@@ -2337,7 +2337,7 @@ wheels = [

 [[package]]
 name = "langgraph"
-version = "0.6.7"
+version = "0.6.2"
 source = { editable = "../libs/langgraph" }
 dependencies = [
    { name = "langchain-core" },
@@ -2380,7 +2380,6 @@ dev = [
    { name = "pytest-repeat" },
    { name = "pytest-watcher" },
    { name = "pytest-xdist", extras = ["psutil"] },
-    { name = "redis" },
    { name = "ruff" },
    { name = "syrupy" },
    { name = "types-requests" },
@@ -2414,7 +2413,6 @@ dev = [
    { name = "pytest-asyncio" },
    { name = "pytest-mock" },
    { name = "pytest-watcher" },
-    { name = "redis" },
    { name = "ruff" },
 ]

@@ -2645,7 +2643,7 @@ test = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "0.6.4"
+version = "0.6.2"
 source = { editable = "../libs/prebuilt" }
 dependencies = [
    { name = "langchain-core" },
@@ -2676,6 +2674,7 @@ dev = [

 [[package]]
 name = "langgraph-sdk"
+version = "0.2.0"
 source = { editable = "../libs/sdk-py" }
 dependencies = [
    { name = "httpx" },
@@ -5,7 +5,7 @@
   "id": "18526f23",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/add-memory.md"
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/persistence_postgres.ipynb"
   ]
  }
 ],
@@ -707,9 +707,7 @@
    "    \"\"\"\n",
    "    Find all tool calls in the messages returned\n",
    "    \"\"\"\n",
-    "    tool_calls = [\n",
-    "        tc[\"name\"] for m in messages[\"messages\"] for tc in getattr(m, \"tool_calls\", [])\n",
-    "    ]\n",
+    "    tool_calls = [tc['name'] for m in messages['messages'] for tc in getattr(m, 'tool_calls', [])]\n",
    "    return tool_calls\n",
    "\n",
    "\n",
@@ -7,6 +7,11 @@ from contextlib import contextmanager
 from typing import Any

 from langchain_core.runnables import RunnableConfig
+from psycopg import Capabilities, Connection, Cursor, Pipeline
+from psycopg.rows import DictRow, dict_row
+from psycopg.types.json import Jsonb
+from psycopg_pool import ConnectionPool
+
 from langgraph.checkpoint.base import (
    WRITES_IDX_MAP,
    ChannelVersions,
@@ -14,17 +19,12 @@ from langgraph.checkpoint.base import (
    CheckpointMetadata,
    CheckpointTuple,
    get_checkpoint_id,
-    get_serializable_checkpoint_metadata,
+    get_checkpoint_metadata,
 )
-from langgraph.checkpoint.serde.base import SerializerProtocol
-from psycopg import Capabilities, Connection, Cursor, Pipeline
-from psycopg.rows import DictRow, dict_row
-from psycopg.types.json import Jsonb
-from psycopg_pool import ConnectionPool
-
 from langgraph.checkpoint.postgres import _internal
 from langgraph.checkpoint.postgres.base import BasePostgresSaver
 from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver
+from langgraph.checkpoint.serde.base import SerializerProtocol

 Conn = _internal.Conn  # For backward compatibility

@@ -325,7 +325,7 @@ class PostgresSaver(BasePostgresSaver):
                    checkpoint["id"],
                    checkpoint_id,
                    Jsonb(copy),
-                    Jsonb(get_serializable_checkpoint_metadata(config, metadata)),
+                    Jsonb(get_checkpoint_metadata(config, metadata)),
                ),
            )
        return next_config
@@ -450,7 +450,7 @@ class PostgresSaver(BasePostgresSaver):
            {
                **value["checkpoint"],
                "channel_values": {
-                    **(value["checkpoint"].get("channel_values") or {}),
+                    **value["checkpoint"].get("channel_values"),
                    **self._load_blobs(value["channel_values"]),
                },
            },
@@ -7,6 +7,11 @@ from contextlib import asynccontextmanager
 from typing import Any

 from langchain_core.runnables import RunnableConfig
+from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
+from psycopg.rows import DictRow, dict_row
+from psycopg.types.json import Jsonb
+from psycopg_pool import AsyncConnectionPool
+
 from langgraph.checkpoint.base import (
    WRITES_IDX_MAP,
    ChannelVersions,
@@ -14,17 +19,12 @@ from langgraph.checkpoint.base import (
    CheckpointMetadata,
    CheckpointTuple,
    get_checkpoint_id,
-    get_serializable_checkpoint_metadata,
+    get_checkpoint_metadata,
 )
-from langgraph.checkpoint.serde.base import SerializerProtocol
-from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
-from psycopg.rows import DictRow, dict_row
-from psycopg.types.json import Jsonb
-from psycopg_pool import AsyncConnectionPool
-
 from langgraph.checkpoint.postgres import _ainternal
 from langgraph.checkpoint.postgres.base import BasePostgresSaver
 from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver
+from langgraph.checkpoint.serde.base import SerializerProtocol

 Conn = _ainternal.Conn  # For backward compatibility

@@ -283,7 +283,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
                    checkpoint["id"],
                    checkpoint_id,
                    Jsonb(copy),
-                    Jsonb(get_serializable_checkpoint_metadata(config, metadata)),
+                    Jsonb(get_checkpoint_metadata(config, metadata)),
                ),
            )
        return next_config
@@ -409,7 +409,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
            {
                **value["checkpoint"],
                "channel_values": {
-                    **(value["checkpoint"].get("channel_values") or {}),
+                    **value["checkpoint"].get("channel_values"),
                    **self._load_blobs(value["channel_values"]),
                },
            },
@@ -1,12 +1,12 @@
 from __future__ import annotations

 import random
-import warnings
 from collections.abc import Sequence
-from importlib.metadata import version as get_version
 from typing import Any, Optional, cast

 from langchain_core.runnables import RunnableConfig
+from psycopg.types.json import Jsonb
+
 from langgraph.checkpoint.base import (
    WRITES_IDX_MAP,
    BaseCheckpointSaver,
@@ -14,22 +14,9 @@ from langgraph.checkpoint.base import (
    get_checkpoint_id,
 )
 from langgraph.checkpoint.serde.types import TASKS
-from psycopg.types.json import Jsonb

 MetadataInput = Optional[dict[str, Any]]

-try:
-    major, minor = get_version("langgraph").split(".")[:2]
-    if int(major) == 0 and int(minor) < 5:
-        warnings.warn(
-            "You're using incompatible versions of langgraph and checkpoint-postgres. Please upgrade langgraph to avoid unexpected behavior.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-except Exception:
-    # skip version check if running from source
-    pass
-
 """
 To add a new migration, add a new string to the MIGRATIONS list.
 The position of the migration in the list is the version number.
@@ -6,16 +6,6 @@ from contextlib import asynccontextmanager, contextmanager
 from typing import Any, Optional

 from langchain_core.runnables import RunnableConfig
-from langgraph.checkpoint.base import (
-    WRITES_IDX_MAP,
-    ChannelVersions,
-    Checkpoint,
-    CheckpointMetadata,
-    CheckpointTuple,
-    get_serializable_checkpoint_metadata,
-)
-from langgraph.checkpoint.serde.base import SerializerProtocol
-from langgraph.checkpoint.serde.types import TASKS
 from psycopg import (
    AsyncConnection,
    AsyncCursor,
@@ -29,8 +19,18 @@ from psycopg.rows import DictRow, dict_row
 from psycopg.types.json import Jsonb
 from psycopg_pool import AsyncConnectionPool, ConnectionPool

+from langgraph.checkpoint.base import (
+    WRITES_IDX_MAP,
+    ChannelVersions,
+    Checkpoint,
+    CheckpointMetadata,
+    CheckpointTuple,
+    get_checkpoint_metadata,
+)
 from langgraph.checkpoint.postgres import _ainternal, _internal
 from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.serde.base import SerializerProtocol
+from langgraph.checkpoint.serde.types import TASKS

 """
 To add a new migration, add a new string to the MIGRATIONS list.
@@ -441,7 +441,7 @@ class ShallowPostgresSaver(BasePostgresSaver):
                    thread_id,
                    checkpoint_ns,
                    Jsonb(copy),
-                    Jsonb(get_serializable_checkpoint_metadata(config, metadata)),
+                    Jsonb(get_checkpoint_metadata(config, metadata)),
                ),
            )
        return next_config
@@ -774,7 +774,7 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
                    thread_id,
                    checkpoint_ns,
                    Jsonb(copy),
-                    Jsonb(get_serializable_checkpoint_metadata(config, metadata)),
+                    Jsonb(get_checkpoint_metadata(config, metadata)),
                ),
            )
        return next_config
@@ -1,4 +1,4 @@
 from langgraph.store.postgres.aio import AsyncPostgresStore
-from langgraph.store.postgres.base import PoolConfig, PostgresStore
+from langgraph.store.postgres.base import PostgresStore

-__all__ = ["AsyncPostgresStore", "PoolConfig", "PostgresStore"]
+__all__ = ["AsyncPostgresStore", "PostgresStore"]
@@ -8,6 +8,11 @@ from types import TracebackType
 from typing import Any, Callable, cast

 import orjson
+from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
+from psycopg.rows import DictRow, dict_row
+from psycopg_pool import AsyncConnectionPool
+
+from langgraph.checkpoint.postgres import _ainternal
 from langgraph.store.base import (
    GetOp,
    ListNamespacesOp,
@@ -17,11 +22,6 @@ from langgraph.store.base import (
    SearchOp,
 )
 from langgraph.store.base.batch import AsyncBatchedBaseStore
-from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
-from psycopg.rows import DictRow, dict_row
-from psycopg_pool import AsyncConnectionPool
-
-from langgraph.checkpoint.postgres import _ainternal
 from langgraph.store.postgres.base import (
    PLACEHOLDER,
    BasePostgresStore,
@@ -22,6 +22,14 @@ from typing import (
 )

 import orjson
+from psycopg import Capabilities, Connection, Cursor, Pipeline
+from psycopg.rows import DictRow, dict_row
+from psycopg.types.json import Jsonb
+from psycopg_pool import ConnectionPool
+from typing_extensions import TypedDict
+
+from langgraph.checkpoint.postgres import _ainternal as _ainternal
+from langgraph.checkpoint.postgres import _internal as _pg_internal
 from langgraph.store.base import (
    BaseStore,
    GetOp,
@@ -38,14 +46,6 @@ from langgraph.store.base import (
    get_text_at_path,
    tokenize_path,
 )
-from psycopg import Capabilities, Connection, Cursor, Pipeline
-from psycopg.rows import DictRow, dict_row
-from psycopg.types.json import Jsonb
-from psycopg_pool import ConnectionPool
-from typing_extensions import TypedDict
-
-from langgraph.checkpoint.postgres import _ainternal as _ainternal
-from langgraph.checkpoint.postgres import _internal as _pg_internal

 if TYPE_CHECKING:
    from langchain_core.embeddings import Embeddings
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.25"
+version = "2.0.23"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.9"
@@ -12,7 +12,7 @@ readme = "README.md"
 license = "MIT"
 license-files = ['LICENSE']
 dependencies = [
-  "langgraph-checkpoint>=2.1.2,<3.0.0",
+  "langgraph-checkpoint>=2.0.21,<3.0.0",
  "orjson>=3.10.1",
  "psycopg>=3.2.0",
  "psycopg-pool>=3.2.0",
@@ -6,6 +6,10 @@ from uuid import uuid4

 import pytest
 from langchain_core.runnables import RunnableConfig
+from psycopg import AsyncConnection
+from psycopg.rows import dict_row
+from psycopg_pool import AsyncConnectionPool
+
 from langgraph.checkpoint.base import (
    EXCLUDED_METADATA_KEYS,
    Checkpoint,
@@ -13,15 +17,11 @@ from langgraph.checkpoint.base import (
    create_checkpoint,
    empty_checkpoint,
 )
-from langgraph.checkpoint.serde.types import TASKS
-from psycopg import AsyncConnection
-from psycopg.rows import dict_row
-from psycopg_pool import AsyncConnectionPool
-
 from langgraph.checkpoint.postgres.aio import (
    AsyncPostgresSaver,
    AsyncShallowPostgresSaver,
 )
+from langgraph.checkpoint.serde.types import TASKS
 from tests.conftest import DEFAULT_POSTGRES_URI


@@ -187,11 +187,13 @@ def test_data():
    metadata_1: CheckpointMetadata = {
        "source": "input",
        "step": 2,
+        "writes": {},
        "score": 1,
    }
    metadata_2: CheckpointMetadata = {
        "source": "loop",
        "step": 1,
+        "writes": {"foo": "bar"},
        "score": None,
    }
    metadata_3: CheckpointMetadata = {}
@@ -218,6 +220,7 @@ async def test_combined_metadata(saver_name: str, test_data) -> None:
        metadata: CheckpointMetadata = {
            "source": "loop",
            "step": 1,
+            "writes": {"foo": "bar"},
            "score": None,
        }
        await saver.aput(config, chkpnt, metadata, {})
@@ -243,6 +246,7 @@ async def test_asearch(saver_name: str, test_data) -> None:
        query_1 = {"source": "input"}  # search by 1 key
        query_2 = {
            "step": 1,
+            "writes": {"foo": "bar"},
        }  # search by multiple keys
        query_3: dict[str, Any] = {}  # search by no keys, return all checkpoints
        query_4 = {"source": "update", "step": 1}  # no match
@@ -340,34 +344,3 @@ async def test_pending_sends_migration(saver_name: str) -> None:
            TASKS: ["send-1", "send-2", "send-3"]
        }
        assert TASKS in search_results[0].checkpoint["channel_versions"]
-
-
-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
-async def test_get_checkpoint_no_channel_values(
-    monkeypatch, saver_name: str, test_data
-) -> None:
-    """Backwards compatibility test that verifies a checkpoint with no channel_values key can be retrieved without throwing an error."""
-    async with _saver(saver_name) as saver:
-        config = {
-            "configurable": {
-                "thread_id": "thread-2",
-                "checkpoint_ns": "",
-                "__super_private_key": "super_private_value",
-            },
-            "metadata": {"run_id": "my_run_id"},
-        }
-        chkpnt: Checkpoint = create_checkpoint(empty_checkpoint(), {}, 1)
-        await saver.aput(config, chkpnt, {}, {})
-
-        load_checkpoint_tuple = saver._load_checkpoint_tuple
-
-        def patched_load_checkpoint_tuple(value):
-            value["checkpoint"].pop("channel_values", None)
-            return load_checkpoint_tuple(value)
-
-        monkeypatch.setattr(
-            saver, "_load_checkpoint_tuple", patched_load_checkpoint_tuple
-        )
-
-        checkpoint = await saver.aget_tuple(config)
-        assert checkpoint.checkpoint["channel_values"] == {}
@@ -12,6 +12,8 @@ from typing import Any

 import pytest
 from langchain_core.embeddings import Embeddings
+from psycopg import AsyncConnection
+
 from langgraph.store.base import (
    GetOp,
    Item,
@@ -19,8 +21,6 @@ from langgraph.store.base import (
    PutOp,
    SearchOp,
 )
-from psycopg import AsyncConnection
-
 from langgraph.store.postgres import AsyncPostgresStore
 from tests.conftest import (
    DEFAULT_URI,
@@ -9,6 +9,8 @@ from uuid import uuid4

 import pytest
 from langchain_core.embeddings import Embeddings
+from psycopg import Connection
+
 from langgraph.store.base import (
    GetOp,
    Item,
@@ -17,8 +19,6 @@ from langgraph.store.base import (
    PutOp,
    SearchOp,
 )
-from psycopg import Connection
-
 from langgraph.store.postgres import PostgresStore
 from tests.conftest import (
    DEFAULT_URI,
@@ -861,41 +861,3 @@ def test_store_ttl(store):
    # Now has been (TTL_SECONDS-2)*2 > TTL_SECONDS + TTL_SECONDS/2
    res = store.search(ns, query="bar", refresh_ttl=False)
    assert len(res) == 0
-
-
-@pytest.mark.parametrize(
-    "vector_type,distance_type",
-    [
-        ("vector", "cosine"),
-        ("vector", "inner_product"),
-        ("halfvec", "cosine"),
-        ("halfvec", "inner_product"),
-    ],
-)
-def test_non_ascii(
-    request: Any,
-    fake_embeddings: CharacterEmbeddings,
-    vector_type: str,
-    distance_type: str,
-) -> None:
-    """Test support for non-ascii characters"""
-    with _create_vector_store(vector_type, distance_type, fake_embeddings) as store:
-        store.put(("user_123", "memories"), "1", {"text": "这是中文"})  # Chinese
-        store.put(
-            ("user_123", "memories"), "2", {"text": "これは日本語です"}
-        )  # Japanese
-        store.put(("user_123", "memories"), "3", {"text": "이건 한국어야"})  # Korean
-        store.put(("user_123", "memories"), "4", {"text": "Это русский"})  # Russian
-        store.put(("user_123", "memories"), "5", {"text": "यह रूसी है"})  # Hindi
-
-        result1 = store.search(("user_123", "memories"), query="这是中文")
-        result2 = store.search(("user_123", "memories"), query="これは日本語です")
-        result3 = store.search(("user_123", "memories"), query="이건 한국어야")
-        result4 = store.search(("user_123", "memories"), query="Это русский")
-        result5 = store.search(("user_123", "memories"), query="यह रूसी है")
-
-        assert result1[0].key == "1"
-        assert result2[0].key == "2"
-        assert result3[0].key == "3"
-        assert result4[0].key == "4"
-        assert result5[0].key == "5"
@@ -7,6 +7,10 @@ from uuid import uuid4

 import pytest
 from langchain_core.runnables import RunnableConfig
+from psycopg import Connection
+from psycopg.rows import dict_row
+from psycopg_pool import ConnectionPool
+
 from langgraph.checkpoint.base import (
    EXCLUDED_METADATA_KEYS,
    Checkpoint,
@@ -14,12 +18,8 @@ from langgraph.checkpoint.base import (
    create_checkpoint,
    empty_checkpoint,
 )
-from langgraph.checkpoint.serde.types import TASKS
-from psycopg import Connection
-from psycopg.rows import dict_row
-from psycopg_pool import ConnectionPool
-
 from langgraph.checkpoint.postgres import PostgresSaver, ShallowPostgresSaver
+from langgraph.checkpoint.serde.types import TASKS
 from tests.conftest import DEFAULT_POSTGRES_URI


@@ -169,11 +169,13 @@ def test_data():
    metadata_1: CheckpointMetadata = {
        "source": "input",
        "step": 2,
+        "writes": {},
        "score": 1,
    }
    metadata_2: CheckpointMetadata = {
        "source": "loop",
        "step": 1,
+        "writes": {"foo": "bar"},
        "score": None,
    }
    metadata_3: CheckpointMetadata = {}
@@ -200,6 +202,7 @@ def test_combined_metadata(saver_name: str, test_data) -> None:
        metadata: CheckpointMetadata = {
            "source": "loop",
            "step": 1,
+            "writes": {"foo": "bar"},
            "score": None,
        }
        saver.put(config, chkpnt, metadata, {})
@@ -225,6 +228,7 @@ def test_search(saver_name: str, test_data) -> None:
        query_1 = {"source": "input"}  # search by 1 key
        query_2 = {
            "step": 1,
+            "writes": {"foo": "bar"},
        }  # search by multiple keys
        query_3: dict[str, Any] = {}  # search by no keys, return all checkpoints
        query_4 = {"source": "update", "step": 1}  # no match
@@ -328,33 +332,3 @@ def test_pending_sends_migration(saver_name: str) -> None:
            TASKS: ["send-1", "send-2", "send-3"]
        }
        assert TASKS in search_results[0].checkpoint["channel_versions"]
-
-
-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
-def test_get_checkpoint_no_channel_values(
-    monkeypatch, saver_name: str, test_data
-) -> None:
-    """Backwards compatibility test that verifies a checkpoint with no channel_values key can be retrieved without throwing an error."""
-    with _saver(saver_name) as saver:
-        config = {
-            "configurable": {
-                "thread_id": "thread-2",
-                "checkpoint_ns": "",
-                "__super_private_key": "super_private_value",
-            },
-        }
-        chkpnt: Checkpoint = create_checkpoint(empty_checkpoint(), {}, 1)
-        saver.put(config, chkpnt, {}, {})
-
-        load_checkpoint_tuple = saver._load_checkpoint_tuple
-
-        def patched_load_checkpoint_tuple(value):
-            value["checkpoint"].pop("channel_values", None)
-            return load_checkpoint_tuple(value)
-
-        monkeypatch.setattr(
-            saver, "_load_checkpoint_tuple", patched_load_checkpoint_tuple
-        )
-
-        checkpoint = saver.get_tuple(config)
-        assert checkpoint.checkpoint["channel_values"] == {}
@@ -8,6 +8,7 @@ from contextlib import closing, contextmanager
 from typing import Any, cast

 from langchain_core.runnables import RunnableConfig
+
 from langgraph.checkpoint.base import (
    WRITES_IDX_MAP,
    BaseCheckpointSaver,
@@ -20,7 +21,6 @@ from langgraph.checkpoint.base import (
    get_checkpoint_metadata,
 )
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
-
 from langgraph.checkpoint.sqlite.utils import search_where

 _AIO_ERROR_MSG = (
@@ -8,6 +8,7 @@ from typing import Any, Callable, TypeVar, cast

 import aiosqlite
 from langchain_core.runnables import RunnableConfig
+
 from langgraph.checkpoint.base import (
    WRITES_IDX_MAP,
    BaseCheckpointSaver,
@@ -20,7 +21,6 @@ from langgraph.checkpoint.base import (
    get_checkpoint_metadata,
 )
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
-
 from langgraph.checkpoint.sqlite.utils import search_where

 T = TypeVar("T", bound=Callable)
@@ -5,6 +5,7 @@ from collections.abc import Sequence
 from typing import Any

 from langchain_core.runnables import RunnableConfig
+
 from langgraph.checkpoint.base import get_checkpoint_id


@@ -11,6 +11,7 @@ from typing import Any, Callable, cast
 import aiosqlite
 import orjson
 import sqlite_vec  # type: ignore[import-untyped]
+
 from langgraph.store.base import (
    GetOp,
    ListNamespacesOp,
@@ -21,7 +22,6 @@ from langgraph.store.base import (
    TTLConfig,
 )
 from langgraph.store.base.batch import AsyncBatchedBaseStore
-
 from langgraph.store.sqlite.base import (
    _PLACEHOLDER,
    BaseSqliteStore,
@@ -507,9 +507,7 @@ class AsyncSqliteStore(AsyncBatchedBaseStore, BaseSqliteStore):
            results: List to store results in.
            cur: Database cursor.
        """
-        prepared_queries, embedding_requests = self._prepare_batch_search_queries(
-            search_ops
-        )
+        queries, embedding_requests = self._prepare_batch_search_queries(search_ops)

        # Setup dot_product function if it doesn't exist
        if embedding_requests and self.embeddings:
@@ -517,60 +515,23 @@ class AsyncSqliteStore(AsyncBatchedBaseStore, BaseSqliteStore):
                [query for _, query in embedding_requests]
            )

-            for (embed_req_idx, _), embedding in zip(embedding_requests, vectors):
-                # Find the corresponding query in prepared_queries
-                # The embed_req_idx is the original index in search_ops, which should map to prepared_queries
-                if embed_req_idx < len(prepared_queries):
-                    _params_list: list = prepared_queries[embed_req_idx][1]
-                    for i, param in enumerate(_params_list):
-                        if param is _PLACEHOLDER:
-                            _params_list[i] = sqlite_vec.serialize_float32(embedding)
-                else:
-                    logger.warning(
-                        f"Embedding request index {embed_req_idx} out of bounds for prepared_queries."
-                    )
+            for (idx, _), embedding in zip(embedding_requests, vectors):
+                _params_list: list = queries[idx][1]
+                for i, param in enumerate(_params_list):
+                    if param is _PLACEHOLDER:
+                        _params_list[i] = sqlite_vec.serialize_float32(embedding)

-        for (original_op_idx, _), (query, params, needs_refresh) in zip(
-            search_ops, prepared_queries
-        ):
+        for (idx, _), (query, params) in zip(search_ops, queries):
            await cur.execute(query, params)
            rows = await cur.fetchall()

-            if needs_refresh and rows and self.ttl_config:
-                keys_to_refresh = []
-                for row_data in rows:
-                    # Assuming row_data[0] is prefix (text), row_data[1] is key (text)
-                    # These are raw text values directly from the DB.
-                    keys_to_refresh.append((row_data[0], row_data[1]))
-
-                if keys_to_refresh:
-                    updates_by_prefix = defaultdict(list)
-                    for prefix_text, key_text in keys_to_refresh:
-                        updates_by_prefix[prefix_text].append(key_text)
-
-                    for prefix_text, key_list in updates_by_prefix.items():
-                        placeholders = ",".join(["?"] * len(key_list))
-                        update_query = f"""
-                            UPDATE store
-                            SET expires_at = DATETIME(CURRENT_TIMESTAMP, '+' || ttl_minutes || ' minutes')
-                            WHERE prefix = ? AND key IN ({placeholders}) AND ttl_minutes IS NOT NULL
-                        """
-                        update_params = (prefix_text, *key_list)
-                        try:
-                            await cur.execute(update_query, update_params)
-                        except Exception as e:
-                            logger.error(
-                                f"Error during TTL refresh update for search: {e}"
-                            )
-
-            # Process rows into items
-            if "score" in query:  # Vector search query
+            if "score" in query:
                items = [
                    _row_to_search_item(
-                        _decode_ns_text(row[0]),  # prefix
+                        _decode_ns_text(row[0]),
                        {
-                            "key": row[1],  # key
-                            "value": row[2],  # value
+                            "key": row[1],
+                            "value": row[2],
                            "created_at": row[3],
                            "updated_at": row[4],
                            "expires_at": row[5] if len(row) > 5 else None,
@@ -584,10 +545,10 @@ class AsyncSqliteStore(AsyncBatchedBaseStore, BaseSqliteStore):
            else:  # Regular search query
                items = [
                    _row_to_search_item(
-                        _decode_ns_text(row[0]),  # prefix
+                        _decode_ns_text(row[0]),
                        {
-                            "key": row[1],  # key
-                            "value": row[2],  # value
+                            "key": row[1],
+                            "value": row[2],
                            "created_at": row[3],
                            "updated_at": row[4],
                            "expires_at": row[5] if len(row) > 5 else None,
@@ -598,7 +559,7 @@ class AsyncSqliteStore(AsyncBatchedBaseStore, BaseSqliteStore):
                    for row in rows
                ]

-            results[original_op_idx] = items
+            results[idx] = items

    async def _batch_list_namespaces_ops(
        self,
@@ -13,6 +13,7 @@ from typing import Any, Callable, Literal, NamedTuple, cast

 import orjson
 import sqlite_vec  # type: ignore[import-untyped]
+
 from langgraph.store.base import (
    BaseStore,
    GetOp,
@@ -371,15 +372,13 @@ class BaseSqliteStore:
    def _prepare_batch_search_queries(
        self, search_ops: Sequence[tuple[int, SearchOp]]
    ) -> tuple[
-        list[
-            tuple[str, list[None | str | list[float]], bool]
-        ],  # queries, params, needs_refresh
+        list[tuple[str, list[None | str | list[float]]]],  # queries, params
        list[tuple[int, str]],  # idx, query_text pairs to embed
    ]:
        """
-        Build per-SearchOp SQL queries (with optional TTL refresh flag) plus embedding requests.
+        Build per-SearchOp SQL queries (with optional TTL refresh) plus embedding requests.
        Returns:
-        - queries: list of (SQL, param_list, needs_ttl_refresh_flag)
+        - queries: list of (SQL, param_list)
        - embedding_requests: list of (original_index_in_search_ops, text_query)
        """
        queries = []
@@ -520,18 +519,30 @@ class BaseSqliteStore:
                logger.debug(f"Search query: {base_query}")
                logger.debug(f"Search params: {params}")

-            # Determine if TTL refresh is needed
-            needs_ttl_refresh = bool(
+            # Handle TTL refresh if requested
+            if (
                op.refresh_ttl
                and self.ttl_config
                and self.ttl_config.get("refresh_on_read", False)
-            )
+            ):
+                final_sql = f"""
+                    WITH search_results AS (
+                        {base_query}
+                    ),
+                    updated AS (
+                        UPDATE store
+                        SET expires_at = DATETIME(CURRENT_TIMESTAMP, '+' || ttl_minutes || ' minutes')
+                        WHERE (prefix, key) IN (SELECT prefix, key FROM search_results)
+                        AND ttl_minutes IS NOT NULL
+                    )
+                    SELECT * FROM search_results
+                """
+                final_params = params[:]  # copy params
+            else:
+                final_sql = base_query
+                final_params = params

-            # The base_query is now the final_sql, and we pass the refresh flag
-            final_sql = base_query
-            final_params = params
-
-            queries.append((final_sql, final_params, needs_ttl_refresh))
+            queries.append((final_sql, final_params))

        return queries, embedding_requests

@@ -1320,9 +1331,7 @@ class SqliteStore(BaseSqliteStore, BaseStore):
        results: list[Result],
        cur: sqlite3.Cursor,
    ) -> None:
-        prepared_queries, embedding_requests = self._prepare_batch_search_queries(
-            search_ops
-        )
+        queries, embedding_requests = self._prepare_batch_search_queries(search_ops)

        # Setup similarity functions if they don't exist
        if embedding_requests and self.embeddings:
@@ -1332,48 +1341,16 @@ class SqliteStore(BaseSqliteStore, BaseStore):
            )

            # Replace placeholders with actual embeddings
-            for (embed_req_idx, _), embedding in zip(embedding_requests, embeddings):
-                if embed_req_idx < len(prepared_queries):
-                    _params_list: list = prepared_queries[embed_req_idx][1]
-                    for i, param in enumerate(_params_list):
-                        if param is _PLACEHOLDER:
-                            _params_list[i] = sqlite_vec.serialize_float32(embedding)
-                else:
-                    logger.warning(
-                        f"Embedding request index {embed_req_idx} out of bounds for prepared_queries."
-                    )
+            for (idx, _), embedding in zip(embedding_requests, embeddings):
+                _params_list: list = queries[idx][1]
+                for i, param in enumerate(_params_list):
+                    if param is _PLACEHOLDER:
+                        _params_list[i] = sqlite_vec.serialize_float32(embedding)

-        for (original_op_idx, _), (query, params, needs_refresh) in zip(
-            search_ops, prepared_queries
-        ):
+        for (idx, _), (query, params) in zip(search_ops, queries):
            cur.execute(query, params)
            rows = cur.fetchall()

-            if needs_refresh and rows and self.ttl_config:
-                keys_to_refresh = []
-                for row_data in rows:
-                    keys_to_refresh.append((row_data[0], row_data[1]))
-
-                if keys_to_refresh:
-                    updates_by_prefix = defaultdict(list)
-                    for prefix_text, key_text in keys_to_refresh:
-                        updates_by_prefix[prefix_text].append(key_text)
-
-                    for prefix_text, key_list in updates_by_prefix.items():
-                        placeholders = ",".join(["?"] * len(key_list))
-                        update_query = f"""
-                            UPDATE store
-                            SET expires_at = DATETIME(CURRENT_TIMESTAMP, '+' || ttl_minutes || ' minutes')
-                            WHERE prefix = ? AND key IN ({placeholders}) AND ttl_minutes IS NOT NULL
-                        """
-                        update_params = (prefix_text, *key_list)
-                        try:
-                            cur.execute(update_query, update_params)
-                        except Exception as e:
-                            logger.error(
-                                f"Error during TTL refresh update for search: {e}"
-                            )
-
            if "score" in query:  # Vector search query
                items = [
                    _row_to_search_item(
@@ -1408,7 +1385,7 @@ class SqliteStore(BaseSqliteStore, BaseStore):
                    for row in rows
                ]

-            results[original_op_idx] = items
+            results[idx] = items

    def _batch_list_namespaces_ops(
        self,
@@ -2,13 +2,13 @@ from typing import Any

 import pytest
 from langchain_core.runnables import RunnableConfig
+
 from langgraph.checkpoint.base import (
    Checkpoint,
    CheckpointMetadata,
    create_checkpoint,
    empty_checkpoint,
 )
-
 from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver


@@ -8,6 +8,7 @@ from contextlib import asynccontextmanager
 from typing import Optional, Union, cast

 import pytest
+
 from langgraph.store.base import (
    GetOp,
    Item,
@@ -15,7 +16,6 @@ from langgraph.store.base import (
    PutOp,
    SearchOp,
 )
-
 from langgraph.store.sqlite import AsyncSqliteStore
 from langgraph.store.sqlite.base import SqliteIndexConfig
 from tests.test_store import CharacterEmbeddings
@@ -2,13 +2,13 @@ from typing import Any, cast

 import pytest
 from langchain_core.runnables import RunnableConfig
+
 from langgraph.checkpoint.base import (
    Checkpoint,
    CheckpointMetadata,
    create_checkpoint,
    empty_checkpoint,
 )
-
 from langgraph.checkpoint.sqlite import SqliteSaver
 from langgraph.checkpoint.sqlite.utils import _metadata_predicate, search_where

@@ -116,17 +116,7 @@ class TestSqliteSaver:
                search_results_5[1].config["configurable"]["checkpoint_ns"],
            } == {"", "inner"}

-            # search with before param
-            search_results_6 = list(saver.list(None, before=search_results_5[1].config))
-            assert len(search_results_6) == 1
-            assert search_results_6[0].config["configurable"]["thread_id"] == "thread-1"
-
-            # search with limit param
-            search_results_7 = list(
-                saver.list({"configurable": {"thread_id": "thread-2"}}, limit=1)
-            )
-            assert len(search_results_7) == 1
-            assert search_results_7[0].config["configurable"]["thread_id"] == "thread-2"
+            # TODO: test before and limit params

    def test_search_where(self) -> None:
        # call method / assertions
@@ -9,6 +9,7 @@ from typing import Any, Literal, Optional, Union, cast

 import pytest
 from langchain_core.embeddings import Embeddings
+
 from langgraph.store.base import (
    GetOp,
    Item,
@@ -17,7 +18,6 @@ from langgraph.store.base import (
    PutOp,
    SearchOp,
 )
-
 from langgraph.store.sqlite import SqliteStore
 from langgraph.store.sqlite.base import SqliteIndexConfig

@@ -1067,31 +1067,3 @@ def test_sql_injection_vulnerability(store: SqliteStore) -> None:

    with pytest.raises(ValueError, match="Invalid filter key"):
        store.search(("docs",), filter={malicious_key: "dummy"})
-
-
-@pytest.mark.parametrize("distance_type", VECTOR_TYPES)
-def test_non_ascii(
-    fake_embeddings: CharacterEmbeddings,
-    distance_type: str,
-) -> None:
-    """Test support for non-ascii characters"""
-    with create_vector_store(fake_embeddings, distance_type=distance_type) as store:
-        store.put(("user_123", "memories"), "1", {"text": "这是中文"})  # Chinese
-        store.put(
-            ("user_123", "memories"), "2", {"text": "これは日本語です"}
-        )  # Japanese
-        store.put(("user_123", "memories"), "3", {"text": "이건 한국어야"})  # Korean
-        store.put(("user_123", "memories"), "4", {"text": "Это русский"})  # Russian
-        store.put(("user_123", "memories"), "5", {"text": "यह रूसी है"})  # Hindi
-
-        result1 = store.search(("user_123", "memories"), query="这是中文")
-        result2 = store.search(("user_123", "memories"), query="これは日本語です")
-        result3 = store.search(("user_123", "memories"), query="이건 한국어야")
-        result4 = store.search(("user_123", "memories"), query="Это русский")
-        result5 = store.search(("user_123", "memories"), query="यह रूसी है")
-
-        assert result1[0].key == "1"
-        assert result2[0].key == "2"
-        assert result3[0].key == "3"
-        assert result4[0].key == "4"
-        assert result5[0].key == "5"
@@ -7,7 +7,6 @@ import time
 from collections.abc import Generator

 import pytest
-from langgraph.store.base import TTLConfig

 from langgraph.store.sqlite import SqliteStore
 from langgraph.store.sqlite.aio import AsyncSqliteStore
@@ -94,13 +93,9 @@ def test_ttl_sweeper(temp_db_file: str) -> None:
    ttl_seconds = 2
    ttl_minutes = ttl_seconds / 60

-    ttl_config: TTLConfig = {
-        "default_ttl": ttl_minutes,
-        "sweep_interval_minutes": ttl_minutes / 2,
-    }
    with SqliteStore.from_conn_string(
        temp_db_file,
-        ttl=ttl_config,
+        ttl={"default_ttl": ttl_minutes, "sweep_interval_minutes": ttl_minutes / 2},
    ) as store:
        store.setup()

@@ -303,14 +298,9 @@ async def test_async_ttl_sweeper(temp_db_file: str) -> None:
    ttl_seconds = 2
    ttl_minutes = ttl_seconds / 60

-    ttl_config: TTLConfig = {
-        "default_ttl": ttl_minutes,
-        "sweep_interval_minutes": ttl_minutes / 2,
-    }
-
    async with AsyncSqliteStore.from_conn_string(
        temp_db_file,
-        ttl=ttl_config,
+        ttl={"default_ttl": ttl_minutes, "sweep_interval_minutes": ttl_minutes / 2},
    ) as store:
        await store.setup()

@@ -363,67 +353,3 @@ async def test_async_search_with_ttl(temp_db_file: str) -> None:
        # Search after expiration
        results = await store.asearch(("test",), filter={"value": "apple"})
        assert len(results) == 0
-
-
-@pytest.mark.asyncio
-@pytest.mark.flaky(retries=3)
-async def test_async_asearch_refresh_ttl(temp_db_file: str) -> None:
-    """Test TTL refresh on asearch with async API."""
-    ttl_seconds = 4.0  # Increased TTL for less sensitivity to timing
-    ttl_minutes = ttl_seconds / 60.0
-
-    async with AsyncSqliteStore.from_conn_string(
-        temp_db_file, ttl={"default_ttl": ttl_minutes, "refresh_on_read": True}
-    ) as store:
-        await store.setup()
-
-        namespace = ("docs", "user1")
-        # t=0: items put, expire at t=4.0s
-        await store.aput(namespace, "item1", {"text": "content1", "id": 1})
-        await store.aput(namespace, "item2", {"text": "content2", "id": 2})
-
-        # t=3.0s: (after sleep ttl_seconds * 0.75 = 3s)
-        await asyncio.sleep(ttl_seconds * 0.75)
-
-        # Perform asearch with refresh_ttl=True for item1.
-        # item1's TTL should be refreshed. New expiry: t=3.0s + 4.0s = t=7.0s.
-        # item2's TTL is not affected. Expires at t=4.0s.
-        searched_items = await store.asearch(
-            namespace, filter={"id": 1}, refresh_ttl=True
-        )
-        assert len(searched_items) == 1
-        assert searched_items[0].key == "item1"
-
-        # t=5.0s: (after sleep ttl_seconds * 0.5 = 2s more. Total elapsed: 3s + 2s = 5s)
-        await asyncio.sleep(ttl_seconds * 0.5)
-        # At this point:
-        # - item1 (refreshed by asearch) should expire at t=7.0s. Should be ALIVE.
-        # - item2 (original TTL) should have expired at t=4.0s. Should be GONE after sweep.
-
-        await store.sweep_ttl()
-
-        # Check item1 (should exist due to asearch refresh)
-        item1_check1 = await store.aget(namespace, "item1", refresh_ttl=False)
-        assert item1_check1 is not None, (
-            "Item1 should exist after asearch refresh and first sweep"
-        )
-        assert item1_check1.value["text"] == "content1"
-
-        # Check item2 (should be gone)
-        item2_check1 = await store.aget(namespace, "item2", refresh_ttl=False)
-        assert item2_check1 is None, (
-            "Item2 should be gone after its original TTL expired"
-        )
-
-        # t=7.5s: (after sleep ttl_seconds * 0.625 = 2.5s more. Total elapsed: 5s + 2.5s = 7.5s)
-        await asyncio.sleep(ttl_seconds * 0.625)
-        # At this point:
-        # - item1 (refreshed by asearch, expired at t=7.0s) should be GONE after sweep.
-
-        await store.sweep_ttl()
-
-        # Check item1 again (should be gone now)
-        item1_final_check = await store.aget(namespace, "item1", refresh_ttl=False)
-        assert item1_final_check is None, (
-            "Item1 should be gone after its refreshed TTL expired"
-        )
@@ -404,16 +404,6 @@ def get_checkpoint_metadata(
    return metadata


-def get_serializable_checkpoint_metadata(
-    config: RunnableConfig, metadata: CheckpointMetadata
-) -> CheckpointMetadata:
-    """Get checkpoint metadata in a backwards-compatible manner."""
-    checkpoint_metadata = get_checkpoint_metadata(config, metadata)
-    if "writes" in checkpoint_metadata:
-        checkpoint_metadata.pop("writes")
-    return checkpoint_metadata
-
-
 """
 Mapping from error type to error index.
 Regular writes just map to their index in the list of writes being saved.
@@ -238,7 +238,7 @@ def get_text_at_path(obj: Any, path: str | list[str]) -> list[str]:
        - Nested paths in multi-field: "{field1,nested.field2}"
    """
    if not path or path == "$":
-        return [json.dumps(obj, sort_keys=True, ensure_ascii=False)]
+        return [json.dumps(obj, sort_keys=True)]

    tokens = tokenize_path(path) if isinstance(path, str) else path

@@ -249,7 +249,7 @@ def get_text_at_path(obj: Any, path: str | list[str]) -> list[str]:
            elif obj is None:
                return []
            elif isinstance(obj, (list, dict)):
-                return [json.dumps(obj, sort_keys=True, ensure_ascii=False)]
+                return [json.dumps(obj, sort_keys=True)]
            return []

        token = tokens[pos]
@@ -295,11 +295,7 @@ def get_text_at_path(obj: Any, path: str | list[str]) -> list[str]:
                        if isinstance(current_obj, (str, int, float, bool)):
                            results.append(str(current_obj))
                        elif isinstance(current_obj, (list, dict)):
-                            results.append(
-                                json.dumps(
-                                    current_obj, sort_keys=True, ensure_ascii=False
-                                )
-                            )
+                            results.append(json.dumps(current_obj, sort_keys=True))

        # Handle wildcard
        elif token == "*":
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-checkpoint"
-version = "2.1.2"
+version = "2.1.1"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 requires-python = ">=3.9"
@@ -5,13 +5,12 @@ import time
 import pytest
 import redis

-from langgraph.cache.base import FullKey
 from langgraph.cache.redis import RedisCache


 class TestRedisCache:
    @pytest.fixture(autouse=True)
-    def setup(self) -> None:
+    def setup(self):
        """Set up test Redis client and cache."""
        self.client = redis.Redis(
            host="localhost", port=6379, db=0, decode_responses=False
@@ -21,21 +20,21 @@ class TestRedisCache:
        except redis.ConnectionError:
            pytest.skip("Redis server not available")

-        self.cache: RedisCache = RedisCache(self.client, prefix="test:cache:")
+        self.cache = RedisCache(self.client, prefix="test:cache:")

        # Clean up before each test
        self.client.flushdb()

-    def teardown_method(self) -> None:
+    def teardown_method(self):
        """Clean up after each test."""
        try:
            self.client.flushdb()
        except Exception:
            pass

-    def test_basic_set_and_get(self) -> None:
+    def test_basic_set_and_get(self):
        """Test basic set and get operations."""
-        keys: list[FullKey] = [(("graph", "node"), "key1")]
+        keys = [(("graph", "node"), "key1")]
        values = {keys[0]: ({"result": 42}, None)}

        # Set value
@@ -46,9 +45,9 @@ class TestRedisCache:
        assert len(result) == 1
        assert result[keys[0]] == {"result": 42}

-    def test_batch_operations(self) -> None:
+    def test_batch_operations(self):
        """Test batch set and get operations."""
-        keys: list[FullKey] = [
+        keys = [
            (("graph", "node1"), "key1"),
            (("graph", "node2"), "key2"),
            (("other", "node"), "key3"),
@@ -69,9 +68,9 @@ class TestRedisCache:
        assert result[keys[1]] == {"result": 2}
        assert result[keys[2]] == {"result": 3}

-    def test_ttl_behavior(self) -> None:
+    def test_ttl_behavior(self):
        """Test TTL (time-to-live) functionality."""
-        key: FullKey = (("graph", "node"), "ttl_key")
+        key = (("graph", "node"), "ttl_key")
        values = {key: ({"data": "expires_soon"}, 1)}  # 1 second TTL

        # Set with TTL
@@ -89,10 +88,10 @@ class TestRedisCache:
        result = self.cache.get([key])
        assert len(result) == 0

-    def test_namespace_isolation(self) -> None:
+    def test_namespace_isolation(self):
        """Test that different namespaces are isolated."""
-        key1: FullKey = (("graph1", "node"), "same_key")
-        key2: FullKey = (("graph2", "node"), "same_key")
+        key1 = (("graph1", "node"), "same_key")
+        key2 = (("graph2", "node"), "same_key")

        values = {key1: ({"graph": 1}, None), key2: ({"graph": 2}, None)}

@@ -102,12 +101,9 @@ class TestRedisCache:
        assert result[key1] == {"graph": 1}
        assert result[key2] == {"graph": 2}

-    def test_clear_all(self) -> None:
+    def test_clear_all(self):
        """Test clearing all cached values."""
-        keys: list[FullKey] = [
-            (("graph", "node1"), "key1"),
-            (("graph", "node2"), "key2"),
-        ]
+        keys = [(("graph", "node1"), "key1"), (("graph", "node2"), "key2")]
        values = {keys[0]: ({"result": 1}, None), keys[1]: ({"result": 2}, None)}

        self.cache.set(values)
@@ -123,9 +119,9 @@ class TestRedisCache:
        result = self.cache.get(keys)
        assert len(result) == 0

-    def test_clear_by_namespace(self) -> None:
+    def test_clear_by_namespace(self):
        """Test clearing cached values by namespace."""
-        keys: list[FullKey] = [
+        keys = [
            (("graph1", "node"), "key1"),
            (("graph2", "node"), "key2"),
            (("graph1", "other"), "key3"),
@@ -146,7 +142,7 @@ class TestRedisCache:
        assert len(result) == 1
        assert result[keys[1]] == {"result": 2}

-    def test_empty_operations(self) -> None:
+    def test_empty_operations(self):
        """Test behavior with empty keys/values."""
        # Empty get
        result = self.cache.get([])
@@ -155,14 +151,14 @@ class TestRedisCache:
        # Empty set
        self.cache.set({})  # Should not raise error

-    def test_nonexistent_keys(self) -> None:
+    def test_nonexistent_keys(self):
        """Test getting keys that don't exist."""
-        keys: list[FullKey] = [(("graph", "node"), "nonexistent")]
+        keys = [(("graph", "node"), "nonexistent")]
        result = self.cache.get(keys)
        assert len(result) == 0

    @pytest.mark.asyncio
-    async def test_async_operations(self) -> None:
+    async def test_async_operations(self):
        """Test async set and get operations with sync Redis client."""
        # Create sync Redis client and cache (like main integration tests)
        client = redis.Redis(host="localhost", port=6379, db=1, decode_responses=False)
@@ -171,9 +167,9 @@ class TestRedisCache:
        except Exception:
            pytest.skip("Redis not available")

-        cache: RedisCache = RedisCache(client, prefix="test:async:")
+        cache = RedisCache(client, prefix="test:async:")

-        keys: list[FullKey] = [(("graph", "node"), "async_key")]
+        keys = [(("graph", "node"), "async_key")]
        values = {keys[0]: ({"async": True}, None)}

        # Async set (delegates to sync)
@@ -188,7 +184,7 @@ class TestRedisCache:
        client.flushdb()

    @pytest.mark.asyncio
-    async def test_async_clear(self) -> None:
+    async def test_async_clear(self):
        """Test async clear operations with sync Redis client."""
        # Create sync Redis client and cache (like main integration tests)
        client = redis.Redis(host="localhost", port=6379, db=1, decode_responses=False)
@@ -197,9 +193,9 @@ class TestRedisCache:
        except Exception:
            pytest.skip("Redis not available")

-        cache: RedisCache = RedisCache(client, prefix="test:async:")
+        cache = RedisCache(client, prefix="test:async:")

-        keys: list[FullKey] = [(("graph", "node"), "key")]
+        keys = [(("graph", "node"), "key")]
        values = {keys[0]: ({"data": "test"}, None)}

        await cache.aset(values)
@@ -218,44 +214,44 @@ class TestRedisCache:
        # Cleanup
        client.flushdb()

-    def test_redis_unavailable_get(self) -> None:
+    def test_redis_unavailable_get(self):
        """Test behavior when Redis is unavailable during get operations."""
        # Create cache with non-existent Redis server
        bad_client = redis.Redis(
            host="nonexistent", port=9999, socket_connect_timeout=0.1
        )
-        cache: RedisCache = RedisCache(bad_client, prefix="test:cache:")
+        cache = RedisCache(bad_client, prefix="test:cache:")

-        keys: list[FullKey] = [(("graph", "node"), "key")]
+        keys = [(("graph", "node"), "key")]
        result = cache.get(keys)

        # Should return empty dict when Redis unavailable
        assert result == {}

-    def test_redis_unavailable_set(self) -> None:
+    def test_redis_unavailable_set(self):
        """Test behavior when Redis is unavailable during set operations."""
        # Create cache with non-existent Redis server
        bad_client = redis.Redis(
            host="nonexistent", port=9999, socket_connect_timeout=0.1
        )
-        cache: RedisCache = RedisCache(bad_client, prefix="test:cache:")
+        cache = RedisCache(bad_client, prefix="test:cache:")

-        keys: list[FullKey] = [(("graph", "node"), "key")]
+        keys = [(("graph", "node"), "key")]
        values = {keys[0]: ({"data": "test"}, None)}

        # Should not raise exception when Redis unavailable
        cache.set(values)  # Should silently fail

    @pytest.mark.asyncio
-    async def test_redis_unavailable_async(self) -> None:
+    async def test_redis_unavailable_async(self):
        """Test async behavior when Redis is unavailable."""
        # Create sync cache with non-existent Redis server (like main integration tests)
        bad_client = redis.Redis(
            host="nonexistent", port=9999, socket_connect_timeout=0.1
        )
-        cache: RedisCache = RedisCache(bad_client, prefix="test:cache:")
+        cache = RedisCache(bad_client, prefix="test:cache:")

-        keys: list[FullKey] = [(("graph", "node"), "key")]
+        keys = [(("graph", "node"), "key")]
        values = {keys[0]: ({"data": "test"}, None)}

        # Should return empty dict for get (delegates to sync)
@@ -265,10 +261,10 @@ class TestRedisCache:
        # Should not raise exception for set (delegates to sync)
        await cache.aset(values)  # Should silently fail

-    def test_corrupted_data_handling(self) -> None:
+    def test_corrupted_data_handling(self):
        """Test handling of corrupted data in Redis."""
        # Set some valid data first
-        keys: list[FullKey] = [(("graph", "node"), "valid_key")]
+        keys = [(("graph", "node"), "valid_key")]
        values = {keys[0]: ({"data": "valid"}, None)}
        self.cache.set(values)

@@ -277,36 +273,33 @@ class TestRedisCache:
        self.client.set(corrupted_key, b"invalid:data:format:too:many:colons")

        # Should skip corrupted entry and return only valid ones
-        all_keys: list[FullKey] = [keys[0], (("graph", "node"), "corrupted_key")]
+        all_keys = [keys[0], (("graph", "node"), "corrupted_key")]
        result = self.cache.get(all_keys)

        assert len(result) == 1
        assert result[keys[0]] == {"data": "valid"}

-    def test_key_parsing_edge_cases(self) -> None:
+    def test_key_parsing_edge_cases(self):
        """Test key parsing with edge cases."""
        # Test empty namespace
-        key1: FullKey = ((), "empty_ns")
+        key1 = ((), "empty_ns")
        values = {key1: ({"data": "empty_ns"}, None)}
        self.cache.set(values)
        result = self.cache.get([key1])
        assert result[key1] == {"data": "empty_ns"}

        # Test namespace with special characters
-        key2: FullKey = (
-            ("graph:with:colons", "node-with-dashes"),
-            "key_with_underscores",
-        )
+        key2 = (("graph:with:colons", "node-with-dashes"), "key_with_underscores")
        values = {key2: ({"data": "special_chars"}, None)}
        self.cache.set(values)
        result = self.cache.get([key2])
        assert result[key2] == {"data": "special_chars"}

-    def test_large_data_serialization(self) -> None:
+    def test_large_data_serialization(self):
        """Test handling of large data objects."""
        # Create a large data structure
        large_data = {"large_list": list(range(1000)), "nested": {"data": "x" * 1000}}
-        key: FullKey = (("graph", "node"), "large_key")
+        key = (("graph", "node"), "large_key")
        values = {key: (large_data, None)}

        self.cache.set(values)
@@ -950,8 +950,8 @@ async def test_embed_with_path(fake_embeddings: CharacterEmbeddings) -> None:
    assert results[0].key != results[1].key
    ascore = results[0].score
    bscore = results[1].score
+    assert ascore == bscore
    assert ascore is not None and bscore is not None
-    assert ascore == pytest.approx(bscore, abs=1e-5)

    results = await store.asearch(("test",), query="uuu")
    assert len(results) == 2
@@ -1021,27 +1021,3 @@ async def test_embed_with_path(fake_embeddings: CharacterEmbeddings) -> None:
    assert len(results) == 3
    doc5_result = next(r for r in results if r.key == "doc5")
    assert doc5_result.score is None
-
-
-def test_non_ascii(fake_embeddings: CharacterEmbeddings) -> None:
-    """Test support for non-ascii characters"""
-    store = InMemoryStore(
-        index={"dims": fake_embeddings.dims, "embed": fake_embeddings}
-    )
-    store.put(("user_123", "memories"), "1", {"text": "这是中文"})  # Chinese
-    store.put(("user_123", "memories"), "2", {"text": "これは日本語です"})  # Japanese
-    store.put(("user_123", "memories"), "3", {"text": "이건 한국어야"})  # Korean
-    store.put(("user_123", "memories"), "4", {"text": "Это русский"})  # Russian
-    store.put(("user_123", "memories"), "5", {"text": "यह रूसी है"})  # Hindi
-
-    result1 = store.search(("user_123", "memories"), query="这是中文")
-    result2 = store.search(("user_123", "memories"), query="これは日本語です")
-    result3 = store.search(("user_123", "memories"), query="이건 한국어야")
-    result4 = store.search(("user_123", "memories"), query="Это русский")
-    result5 = store.search(("user_123", "memories"), query="यह रूसी है")
-
-    assert result1[0].key == "1"
-    assert result2[0].key == "2"
-    assert result3[0].key == "3"
-    assert result4[0].key == "4"
-    assert result5[0].key == "5"
@@ -1,89 +0,0 @@
-from collections.abc import Sequence
-from typing import Annotated, Literal, TypedDict
-
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_core.messages import BaseMessage
-from langchain_openai import ChatOpenAI
-from langgraph.graph import END, StateGraph, add_messages
-from langgraph.prebuilt import ToolNode
-
-tools = [TavilySearchResults(max_results=1)]
-
-model_oai = ChatOpenAI(temperature=0)
-
-model_oai = model_oai.bind_tools(tools)
-
-
-class AgentState(TypedDict):
-    messages: Annotated[Sequence[BaseMessage], add_messages]
-
-
-# Define the function that determines whether to continue or not
-def should_continue(state):
-    messages = state["messages"]
-    last_message = messages[-1]
-    # If there are no tool calls, then we finish
-    if not last_message.tool_calls:
-        return "end"
-    # Otherwise if there is, we continue
-    else:
-        return "continue"
-
-
-# Define the function that calls the model
-def call_model(state, config):
-    model = model_oai
-    messages = state["messages"]
-    response = model.invoke(messages)
-    # We return a list, because this will get added to the existing list
-    return {"messages": [response]}
-
-
-# Define the function to execute tools
-tool_node = ToolNode(tools)
-
-
-class ContextSchema(TypedDict):
-    model: Literal["anthropic", "openai"]
-
-
-# Define a new graph
-workflow = StateGraph(AgentState, context_schema=ContextSchema)
-
-# Define the two nodes we will cycle between
-workflow.add_node("agent", call_model)
-workflow.add_node("action", tool_node)
-
-# Set the entrypoint as `agent`
-# This means that this node is the first one called
-workflow.set_entry_point("agent")
-
-# We now add a conditional edge
-workflow.add_conditional_edges(
-    # First, we define the start node. We use `agent`.
-    # This means these are the edges taken after the `agent` node is called.
-    "agent",
-    # Next, we pass in the function that will determine which node is called next.
-    should_continue,
-    # Finally we pass in a mapping.
-    # The keys are strings, and the values are other nodes.
-    # END is a special node marking that the graph should finish.
-    # What will happen is we will call `should_continue`, and then the output of that
-    # will be matched against the keys in this mapping.
-    # Based on which one it matches, that node will then be called.
-    {
-        # If `tools`, then we call the tool node.
-        "continue": "action",
-        # Otherwise we finish.
-        "end": END,
-    },
-)
-
-# We now add a normal edge from `tools` to `agent`.
-# This means that after `tools` is called, `agent` node is called next.
-workflow.add_edge("action", "agent")
-
-# Finally, we compile it!
-# This compiles it into a LangChain Runnable,
-# meaning you can use it as you would any other runnable
-graph = workflow.compile()
@@ -1,9 +0,0 @@
-[project]
-name = "graph-prerelease-reqs-additional-deps"
-version = "0.1.0"
-description = "Test for prerelease stuff"
-readme = "README.md"
-requires-python = ">=3.10"
-dependencies = [
-  "langgraph==0.6.0"
-]
@@ -1,9 +0,0 @@
-[project]
-name = "graph-prerelease-reqs-zuper-deps"
-version = "0.1.0"
-description = "Test for prerelease stuff"
-readme = "README.md"
-requires-python = ">=3.10"
-dependencies = [
-  "langchain-openai==0.3.0"
-]
@@ -1,13 +0,0 @@
-{
-    "python_version": "3.12",
-    "dependencies": [
-      ".",
-      "./deps/additional_deps",
-      "./deps/zuper_deps"
-    ],
-    "graphs": {
-      "agent": "./agent.py:graph"
-    },
-    "env": "../.env"
-  }
-  
@@ -1,14 +0,0 @@
-[project]
-name = "graph-prerelease-reqs"
-version = "0.1.0"
-description = "Test for prerelease stuff"
-readme = "README.md"
-requires-python = ">=3.10"
-dependencies = [
-  "langchain-openai==1.0.0a2",
-  "langgraph==1.0.0a2",
-  "langchain_community>=0.3.0",
-]
-
-[tool.uv]
-prerelease = "allow"
@@ -1,89 +0,0 @@
-from collections.abc import Sequence
-from typing import Annotated, Literal, TypedDict
-
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_core.messages import BaseMessage
-from langchain_openai import ChatOpenAI
-from langgraph.graph import END, StateGraph, add_messages
-from langgraph.prebuilt import ToolNode
-
-tools = [TavilySearchResults(max_results=1)]
-
-model_oai = ChatOpenAI(temperature=0)
-
-model_oai = model_oai.bind_tools(tools)
-
-
-class AgentState(TypedDict):
-    messages: Annotated[Sequence[BaseMessage], add_messages]
-
-
-# Define the function that determines whether to continue or not
-def should_continue(state):
-    messages = state["messages"]
-    last_message = messages[-1]
-    # If there are no tool calls, then we finish
-    if not last_message.tool_calls:
-        return "end"
-    # Otherwise if there is, we continue
-    else:
-        return "continue"
-
-
-# Define the function that calls the model
-def call_model(state, config):
-    model = model_oai
-    messages = state["messages"]
-    response = model.invoke(messages)
-    # We return a list, because this will get added to the existing list
-    return {"messages": [response]}
-
-
-# Define the function to execute tools
-tool_node = ToolNode(tools)
-
-
-class ContextSchema(TypedDict):
-    model: Literal["anthropic", "openai"]
-
-
-# Define a new graph
-workflow = StateGraph(AgentState, context_schema=ContextSchema)
-
-# Define the two nodes we will cycle between
-workflow.add_node("agent", call_model)
-workflow.add_node("action", tool_node)
-
-# Set the entrypoint as `agent`
-# This means that this node is the first one called
-workflow.set_entry_point("agent")
-
-# We now add a conditional edge
-workflow.add_conditional_edges(
-    # First, we define the start node. We use `agent`.
-    # This means these are the edges taken after the `agent` node is called.
-    "agent",
-    # Next, we pass in the function that will determine which node is called next.
-    should_continue,
-    # Finally we pass in a mapping.
-    # The keys are strings, and the values are other nodes.
-    # END is a special node marking that the graph should finish.
-    # What will happen is we will call `should_continue`, and then the output of that
-    # will be matched against the keys in this mapping.
-    # Based on which one it matches, that node will then be called.
-    {
-        # If `tools`, then we call the tool node.
-        "continue": "action",
-        # Otherwise we finish.
-        "end": END,
-    },
-)
-
-# We now add a normal edge from `tools` to `agent`.
-# This means that after `tools` is called, `agent` node is called next.
-workflow.add_edge("action", "agent")
-
-# Finally, we compile it!
-# This compiles it into a LangChain Runnable,
-# meaning you can use it as you would any other runnable
-graph = workflow.compile()
@@ -1,11 +0,0 @@
-{
-    "python_version": "3.12",
-    "dependencies": [
-      "."
-    ],
-    "graphs": {
-      "agent": "./agent.py:graph"
-    },
-    "env": "../.env"
-  }
-  
@@ -1,11 +0,0 @@
-[project]
-name = "graph-prerelease-reqs"
-version = "0.1.0"
-description = "Test for prerelease stuff"
-readme = "README.md"
-requires-python = ">=3.10"
-dependencies = [
-  "langchain-openai==1.0.0a2",
-  "langgraph==1.0.0a2",
-  "langchain_community>=0.3.0",
-]
@@ -7,7 +7,6 @@ from langchain_core.messages import BaseMessage
 from langchain_openai import ChatOpenAI
 from langgraph.graph import END, StateGraph, add_messages
 from langgraph.prebuilt import ToolNode
-from langgraph.runtime import Runtime

 tools = [TavilySearchResults(max_results=1)]

@@ -18,10 +17,6 @@ model_anth = model_anth.bind_tools(tools)
 model_oai = model_oai.bind_tools(tools)


-class AgentContext(TypedDict):
-    model: Literal["anthropic", "openai"]
-
-
 class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]

@@ -39,8 +34,8 @@ def should_continue(state):


 # Define the function that calls the model
-def call_model(state, runtime: Runtime[AgentContext]):
-    if runtime.context.get("model", "anthropic") == "anthropic":
+def call_model(state, config):
+    if config["configurable"].get("model", "anthropic") == "anthropic":
        model = model_anth
    else:
        model = model_oai
@@ -54,8 +49,12 @@ def call_model(state, runtime: Runtime[AgentContext]):
 tool_node = ToolNode(tools)


+class ContextSchema(TypedDict):
+    model: Literal["anthropic", "openai"]
+
+
 # Define a new graph
-workflow = StateGraph(AgentState, context_schema=AgentContext)
+workflow = StateGraph(AgentState, context_schema=ContextSchema)

 # Define the two nodes we will cycle between
 workflow.add_node("agent", call_model)
@@ -1,5 +1,4 @@
 {
-  "$schema": "https://langgra.ph/schema.json",
  "python_version": "3.12",
  "dependencies": [
    "langchain_community",
@@ -1,6 +1,6 @@
 from collections.abc import Sequence
 from pathlib import Path
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, TypedDict

 from langchain_anthropic import ChatAnthropic
 from langchain_community.tools.tavily_search import TavilySearchResults
@@ -8,7 +8,6 @@ from langchain_core.messages import BaseMessage
 from langchain_openai import ChatOpenAI
 from langgraph.graph import END, StateGraph, add_messages
 from langgraph.prebuilt import ToolNode
-from langgraph.runtime import Runtime

 tools = [TavilySearchResults(max_results=1)]

@@ -22,10 +21,6 @@ prompt = open(Path(__file__).parent.parent / "prompt.txt").read()
 subprompt = open(Path(__file__).parent / "subprompt.txt").read()


-class AgentContext(TypedDict):
-    model: Literal["anthropic", "openai"]
-
-
 class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]

@@ -43,8 +38,8 @@ def should_continue(state):


 # Define the function that calls the model
-def call_model(state, runtime: Runtime[AgentContext]):
-    if runtime.context.get("model", "anthropic") == "anthropic":
+def call_model(state, config):
+    if config["configurable"].get("model", "anthropic") == "anthropic":
        model = model_anth
    else:
        model = model_oai
@@ -57,8 +52,9 @@ def call_model(state, runtime: Runtime[AgentContext]):
 # Define the function to execute tools
 tool_node = ToolNode(tools)

+
 # Define a new graph
-workflow = StateGraph(AgentState, context_schema=AgentContext)
+workflow = StateGraph(AgentState)

 # Define the two nodes we will cycle between
 workflow.add_node("agent", call_model)
@@ -1,5 +1,4 @@
 {
-  "$schema": "https://langgra.ph/schema.json",
  "dependencies": [
    "."
  ],
@@ -1,6 +1,6 @@
 from collections.abc import Sequence
 from pathlib import Path
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, TypedDict

 from langchain_anthropic import ChatAnthropic
 from langchain_community.tools.tavily_search import TavilySearchResults
@@ -8,7 +8,6 @@ from langchain_core.messages import BaseMessage
 from langchain_openai import ChatOpenAI
 from langgraph.graph import END, StateGraph, add_messages
 from langgraph.prebuilt import ToolNode
-from langgraph.runtime import Runtime

 tools = [TavilySearchResults(max_results=1)]

@@ -22,10 +21,6 @@ prompt = open(Path(__file__).parent.parent / "prompt.txt").read()
 subprompt = open(Path(__file__).parent / "subprompt.txt").read()


-class AgentContext(TypedDict):
-    model: Literal["anthropic", "openai"]
-
-
 class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]

@@ -43,8 +38,8 @@ def should_continue(state):


 # Define the function that calls the model
-def call_model(state, runtime: Runtime[AgentContext]):
-    if runtime.context.get("model", "anthropic") == "anthropic":
+def call_model(state, config):
+    if config["configurable"].get("model", "anthropic") == "anthropic":
        model = model_anth
    else:
        model = model_oai
@@ -59,7 +54,7 @@ tool_node = ToolNode(tools)


 # Define a new graph
-workflow = StateGraph(AgentState, context_schema=AgentContext)
+workflow = StateGraph(AgentState)

 # Define the two nodes we will cycle between
 workflow.add_node("agent", call_model)
@@ -1,5 +1,4 @@
 {
-  "$schema": "https://langgra.ph/schema.json",
  "dependencies": [
    "."
  ],
@@ -1,5 +1,4 @@
 {
-  "$schema": "https://langgra.ph/schema.json",
  "node_version": "20",
  "graphs": {
    "agent": "./src/agent/graph.ts:graph"
@@ -1,62 +0,0 @@
-module.exports = {
-  extends: [
-    "eslint:recommended",
-    "prettier",
-    "plugin:@typescript-eslint/recommended",
-  ],
-  parserOptions: {
-    ecmaVersion: 12,
-    parser: "@typescript-eslint/parser",
-    project: "./tsconfig.json",
-    sourceType: "module",
-  },
-  plugins: ["import", "@typescript-eslint", "no-instanceof"],
-  ignorePatterns: [
-    ".eslintrc.cjs",
-    "scripts",
-    "src/utils/lodash/*",
-    "node_modules",
-    "dist",
-    "dist-cjs",
-    "*.js",
-    "*.cjs",
-    "*.d.ts",
-  ],
-  rules: {
-    "no-process-env": 2,
-    "no-instanceof/no-instanceof": 2,
-    "@typescript-eslint/explicit-module-boundary-types": 0,
-    "@typescript-eslint/no-empty-function": 0,
-    "@typescript-eslint/no-shadow": 0,
-    "@typescript-eslint/no-empty-interface": 0,
-    "@typescript-eslint/no-use-before-define": ["error", "nofunc"],
-    "@typescript-eslint/no-unused-vars": ["warn", { args: "none" }],
-    "@typescript-eslint/no-floating-promises": "error",
-    "@typescript-eslint/no-misused-promises": "error",
-    camelcase: 0,
-    "class-methods-use-this": 0,
-    "import/extensions": [2, "ignorePackages"],
-    "import/no-extraneous-dependencies": [
-      "error",
-      { devDependencies: ["**/*.test.ts"] },
-    ],
-    "import/no-unresolved": 0,
-    "import/prefer-default-export": 0,
-    "keyword-spacing": "error",
-    "max-classes-per-file": 0,
-    "max-len": 0,
-    "no-await-in-loop": 0,
-    "no-bitwise": 0,
-    "no-console": 0,
-    "no-restricted-syntax": 0,
-    "no-shadow": 0,
-    "no-continue": 0,
-    "no-underscore-dangle": 0,
-    "no-use-before-define": 0,
-    "no-useless-constructor": 0,
-    "no-return-await": 0,
-    "consistent-return": 0,
-    "no-else-return": 0,
-    "new-cap": ["error", { properties: false, capIsNew: false }],
-  },
-};
@@ -1,7 +0,0 @@
-{
-  "node_version": "20",
-  "graphs": {
-    "agent": "./src/graph.ts:graph"
-  },
-  "env": "../../.env"
-}
@@ -1,18 +0,0 @@
-{
-  "name": "@js-monorepo-example/agent",
-  "version": "0.0.1",
-  "type": "module",
-  "main": "src/graph.ts",
-  "scripts": {
-    "build": "tsc",
-    "clean": "rm -rf dist"
-  },
-  "dependencies": {
-    "@js-monorepo-example/shared": "*",
-    "@langchain/core": "^0.3.2",
-    "@langchain/langgraph": "^0.2.5"
-  },
-  "devDependencies": {
-    "typescript": "^5.3.3"
-  }
-}
@@ -1,47 +0,0 @@
-/**
- * Simple LangGraph.js example for monorepo testing
- */
-import { StateGraph } from "@langchain/langgraph";
-import { RunnableConfig } from "@langchain/core/runnables";
-import { StateAnnotation } from "./state.js";
-import { getGreeting } from "@js-monorepo-example/shared";
-
-/**
- * Simple node that uses the shared library
- */
-const callModel = async (
-  state: typeof StateAnnotation.State,
-  _config: RunnableConfig,
-): Promise<typeof StateAnnotation.Update> => {
-  // Use functions from the shared library
-  const greeting = getGreeting();
-
-  return {
-    messages: [
-      {
-        role: "assistant",
-        content: `${greeting}`,
-      },
-    ],
-  };
-};
-
-/**
- * Simple routing function
- */
-export const route = (
-  state: typeof StateAnnotation.State,
-): "__end__" | "callModel" => {
-  if (state.messages.length > 0) {
-    return "__end__";
-  }
-  return "callModel";
-};
-
-// Create the graph
-const builder = new StateGraph(StateAnnotation)
-  .addNode("callModel", callModel)
-  .addEdge("__start__", "callModel")
-  .addConditionalEdges("callModel", route);
-
-export const graph = builder.compile();
@@ -1,15 +0,0 @@
-import { BaseMessage, BaseMessageLike } from "@langchain/core/messages";
-import { Annotation, messagesStateReducer } from "@langchain/langgraph";
-
-/**
- * Simple state annotation for the agent
- */
-export const StateAnnotation = Annotation.Root({
-  /**
-   * Messages track the primary execution state of the agent.
-   */
-  messages: Annotation<BaseMessage[], BaseMessageLike[]>({
-    reducer: messagesStateReducer,
-    default: () => [],
-  }),
-});
@@ -1,9 +0,0 @@
-{
-  "extends": "../../tsconfig.json",
-  "compilerOptions": {
-    "outDir": "./dist",
-    "rootDir": "./src"
-  },
-  "include": ["src/**/*"],
-  "exclude": ["node_modules", "dist"]
-}
@@ -1,14 +0,0 @@
-{
-  "name": "@js-monorepo-example/shared",
-  "version": "0.0.1",
-  "type": "module",
-  "main": "dist/index.js",
-  "types": "dist/index.d.ts",
-  "scripts": {
-    "build": "tsc",
-    "clean": "rm -rf dist"
-  },
-  "devDependencies": {
-    "typescript": "^5.3.3"
-  }
-}
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
open-swe[bot]	7c09532a51	Apply patch [skip ci]	2025-08-22 15:24:37 +00:00
open-swe[bot]	98201fa98d	Apply patch [skip ci]	2025-08-22 15:23:02 +00:00
open-swe[bot]	0334c81eb2	Apply patch [skip ci]	2025-08-22 15:22:51 +00:00
open-swe[bot]	0302621c24	Apply patch [skip ci]	2025-08-22 15:22:40 +00:00
open-swe[bot]	921a8edf66	Apply patch [skip ci]	2025-08-22 15:22:11 +00:00
open-swe[bot]	dfee69f4b2	Apply patch [skip ci]	2025-08-22 15:22:00 +00:00
open-swe[bot]	fc83746a29	Apply patch [skip ci]	2025-08-22 15:21:48 +00:00
open-swe[bot]	20befcb86a	Apply patch [skip ci]	2025-08-22 15:20:42 +00:00
open-swe[bot]	d97d18ad35	Apply patch [skip ci]	2025-08-22 15:20:31 +00:00
open-swe[bot]	2f096a3c2e	Apply patch [skip ci]	2025-08-22 15:20:20 +00:00
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