x

.github/ISSUE_TEMPLATE/config.yml

@@ -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

.github/ISSUE_TEMPLATE/documentation.yml

@@ -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.

.github/scripts/check_sdk_methods.py

@@ -1,15 +1,10 @@
 import ast
 import os
 from itertools import filterfalse
-from typing import Dict, List, Tuple
+from typing import List, Tuple
 
 ROOT_PATH = os.path.abspath(os.path.join(__file__, "..", "..", ".."))
 CLIENT_PATH = os.path.join(ROOT_PATH, "libs", "sdk-py", "langgraph_sdk", "client.py")
-ASYNC_TO_SYNC_METHOD_MAP: Dict[str, str] = {
-    "aclose": "close",
-    "__aenter__": "__enter__",
-    "__aexit__": "__exit__",
-}
 
 
 def get_class_methods(node: ast.ClassDef) -> List[str]:
@@ -27,7 +22,7 @@ def find_classes(tree: ast.AST) -> List[Tuple[str, List[str]]]:
 
 def compare_sync_async_methods(sync_methods: List[str], async_methods: List[str]) -> List[str]:
     sync_set = set(sync_methods)
-    async_set = {ASYNC_TO_SYNC_METHOD_MAP.get(async_method, async_method) for async_method in async_methods}
+    async_set = set(async_methods)
     missing_in_sync = list(async_set - sync_set)
     missing_in_async = list(sync_set - async_set)
     return missing_in_sync + missing_in_async

.github/scripts/run_langgraph_cli_test.py

@@ -1,145 +1,107 @@
+import asyncio
+import json
+import os
 import pathlib
 import sys
-import time
-from urllib import request, error
-
 import langgraph_cli
-import langgraph_cli.config
 import langgraph_cli.docker
-from langgraph_cli.cli import prepare_args_and_stdin
-from langgraph_cli.constants import DEFAULT_PORT
+import langgraph_cli.config
+
 from langgraph_cli.exec import Runner, subp_exec
 from langgraph_cli.progress import Progress
+from langgraph_cli.constants import DEFAULT_PORT
 
 
-def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
-    """Spin up API with Postgres/Redis via docker compose and wait until ready."""
+def test(
+    config: pathlib.Path,
+    port: int,
+    tag: str,
+    verbose: bool,
+):
     with Runner() as runner, Progress(message="Pulling...") as set:
-        # Detect docker/compose capabilities
+        # check docker available
         capabilities = langgraph_cli.docker.check_capabilities(runner)
-
-        # Validate config and prepare compose stdin/args using built image
+        # open config
         config_json = langgraph_cli.config.validate_config_file(config)
-        args, stdin = prepare_args_and_stdin(
-            capabilities=capabilities,
-            config_path=config,
-            config=config_json,
-            docker_compose=None,
-            port=port,
-            watch=False,
-            debugger_port=None,
-            debugger_base_url=f"http://127.0.0.1:{port}",
-            postgres_uri=None,
-            api_version=None,
-            image=tag,
-            base_image=None,
-        )
 
-        # Compose up with wait (implies detach), similar to `langgraph up --wait`
-        args_up = [*args, "up", "--remove-orphans", "--wait"]
+        set("Running...")
+        args = [
+            "run",
+            "--rm",
+            "-p",
+            f"{port}:8000",
+        ]
+        if isinstance(config_json["env"], str):
+            args.extend(
+                [
+                    "--env-file",
+                    str(config.parent / config_json["env"]),
+                ]
+            )
+        else:
+            for k, v in config_json["env"].items():
+                args.extend(
+                    [
+                        "-e",
+                        f"{k}={v}",
+                    ]
+                )
+        if capabilities.healthcheck_start_interval:
+            args.extend(
+                [
+                    "--health-interval",
+                    "5s",
+                    "--health-retries",
+                    "1",
+                    "--health-start-period",
+                    "10s",
+                    "--health-start-interval",
+                    "1s",
+                ]
+            )
+        else:
+            args.extend(
+                [
+                    "--health-interval",
+                    "5s",
+                    "--health-retries",
+                    "2",
+                ]
+            )
 
-        compose_cmd = ["docker", "compose"]
-        if capabilities.compose_type == "standalone":
-            compose_cmd = ["docker-compose"]
+        _task = None
+
+        def on_stdout(line: str):
+            nonlocal _task
+            if "GET /ok" in line or "Uvicorn running on" in line:
+                set("")
+                sys.stdout.write(
+                    f"""Ready!
+- API: http://localhost:{port}
+"""
+                )
+                sys.stdout.flush()
+                _task.cancel()
+                return True
+            return False
+
+        async def subp_exec_task(*args, **kwargs):
+            nonlocal _task
+            _task = asyncio.create_task(subp_exec(*args, **kwargs))
+            await _task
 
-        set("Starting...")
         try:
             runner.run(
-                subp_exec(
-                    *compose_cmd,
-                    *args_up,
-                    input=stdin,
+                subp_exec_task(
+                    "docker",
+                    *args,
+                    tag,
                     verbose=verbose,
+                    on_stdout=on_stdout,
                 )
             )
-        except Exception as e:  # noqa: BLE001
-            # On failure, show diagnostics then ensure clean teardown
-            sys.stderr.write(f"docker compose up failed: {e}\n")
-            try:
-                sys.stderr.write("\n== docker compose ps ==\n")
-                runner.run(subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=False))
-            except Exception:
-                pass
-            try:
-                sys.stderr.write("\n== docker compose logs (api) ==\n")
-                runner.run(
-                    subp_exec(
-                        *compose_cmd,
-                        *args,
-                        "logs",
-                        "langgraph-api",
-                        input=stdin,
-                        verbose=False,
-                    )
-                )
-            except Exception:
-                pass
-            finally:
-                try:
-                    runner.run(
-                        subp_exec(
-                            *compose_cmd,
-                            *args,
-                            "down",
-                            "-v",
-                            "--remove-orphans",
-                            input=stdin,
-                            verbose=False,
-                        )
-                    )
-                finally:
-                    raise
-
-        set("")
-        base_url = f"http://localhost:{port}"
-        ok_url = f"{base_url}/ok"
-        print(f"Waiting for {ok_url} to respond with 200...")
-        deadline = time.time() + 30
-        last_err: Exception | None = None
-        while time.time() < deadline:
-            try:
-                with request.urlopen(ok_url, timeout=2) as resp:
-                    if resp.status == 200:
-                        sys.stdout.write(
-                            f"""Ready!\n- API: {base_url}\n- /ok: 200 OK\n"""
-                        )
-                        sys.stdout.flush()
-                        break
-                    else:
-                        last_err = RuntimeError(f"Unexpected status: {resp.status}")
-                        print(f"Unexpected status: {resp.status}")
-            except error.URLError as e:
-                last_err = e
-            except Exception as e:  # noqa: BLE001
-                last_err = e
-            time.sleep(0.5)
-        else:
-            # Bring stack down before raising
-            args_down = [*args, "down", "-v", "--remove-orphans"]
-            try:
-                runner.run(
-                    subp_exec(
-                        *compose_cmd,
-                        *args_down,
-                        input=stdin,
-                        verbose=verbose,
-                    )
-                )
-            finally:
-                raise SystemExit(
-                    f"/ok did not return 202 within timeout. Last error: {last_err}"
-                )
-
-        # Clean up: bring compose stack down to free ports for next test
-        args_down = [*args, "down", "-v", "--remove-orphans"]
-        runner.run(
-            subp_exec(
-                *compose_cmd,
-                *args_down,
-                input=stdin,
-                verbose=verbose,
-            )
-        )
+        except asyncio.CancelledError:
+            pass
 
 
 if __name__ == "__main__":
@@ -148,6 +110,6 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("-t", "--tag", type=str)
     parser.add_argument("-c", "--config", type=str, default="./langgraph.json")
-    parser.add_argument("-p", "--port", type=int, default=DEFAULT_PORT)
+    parser.add_argument("-p", "--port", default=DEFAULT_PORT)
     args = parser.parse_args()
     test(pathlib.Path(args.config), args.port, args.tag, verbose=True)

.github/workflows/_integration_test.yml

@@ -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 +33,42 @@ 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 }}
-        env:
-          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+        working-directory: libs/cli/examples
         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  --base-image "langchain/langgraph-trial"
+          cp .env.example .envg
+          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
+        run: |
+          langgraph build -t langgraph-test-b  --base-image "langchain/langgraph-trial"
+          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
+        run: |
+          langgraph build -t langgraph-test-c  --base-image "langchain/langgraph-trial"
+          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
+        run: |
+          langgraph build -t langgraph-test-d  --base-image "langchain/langgraph-trial"
+          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 ]

.github/workflows/_lint.yml

@@ -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

.github/workflows/_test.yml

@@ -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:

.github/workflows/_test_langgraph.yml

@@ -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:

.github/workflows/_test_release.yml

@@ -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/

.github/workflows/baseline.yml

@@ -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

.github/workflows/bench.yml

@@ -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]

.github/workflows/ci.yml

@@ -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:
@@ -78,7 +78,6 @@ jobs:
             "libs/checkpoint-sqlite",
             "libs/checkpoint-postgres",
             "libs/prebuilt",
-            "libs/sdk-py",
           ]
     if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
     uses: ./.github/workflows/_test.yml
@@ -100,9 +99,9 @@ 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 +117,7 @@ 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:

.github/workflows/codespell.yml

@@ -21,7 +21,7 @@
     
         steps:
           - name: Checkout
-            uses: actions/checkout@v5
+            uses: actions/checkout@v4
     
           - name: Install Dependencies
             run: |

.github/workflows/deploy_docs.yml

@@ -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/
 

.github/workflows/link_check.yml

@@ -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
 

.github/workflows/pr_lint.yml

@@ -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

.github/workflows/release.yml

@@ -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
@@ -62,13 +62,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
         run: |
           PKG_NAME=$(grep -m 1 "^name = " pyproject.toml | cut -d '"' -f 2)
-          if grep -q 'dynamic.*=.*\[.*"version".*\]' pyproject.toml; then
-            # handle dynamic versioning
-            DIR_NAME=$(echo "$PKG_NAME" | tr '-' '_')
-            VERSION=$(grep -m 1 '^__version__' "${DIR_NAME}/__init__.py" | cut -d '"' -f 2)
-          else
-            VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
-          fi
+          VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
           SHORT_PKG_NAME="$(echo "$PKG_NAME" | sed -e 's/langgraph//g' -e 's/-//g')"
           if [ -z $SHORT_PKG_NAME ]; then
             TAG="$VERSION"
@@ -87,7 +81,7 @@ 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 +152,7 @@ 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 +255,7 @@ 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 +264,7 @@ 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 +296,7 @@ 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 +305,7 @@ 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/

.github/workflows/run_notebooks.yml

@@ -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:

.github/workflows/uv_lock_ugprade.yml

@@ -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`.
             

README.md

@@ -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.

docs/README.md

@@ -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
 

docs/_scripts/generate_api_reference_links.py

@@ -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"),

docs/_scripts/js_translation/codeblocks/yarn.lock

@@ -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
 

docs/_scripts/notebook_convert.py

@@ -1,5 +1,3 @@
-"""Convert Jupyter notebooks to markdown with custom processing."""
-
 import ast
 import os
 import re

docs/_scripts/notebook_hooks.py

@@ -190,11 +190,11 @@ REDIRECT_MAP = {
     "concepts/langgraph_cloud.md": "https://docs.langchain.com/langgraph-platform/cloud",
     "concepts/langgraph_self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/hybrid",
     "concepts/langgraph_self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/self-hosted",
-    "concepts/langgraph_standalone_container.md": "https://docs.langchain.com/langgraph-platform/self-hosted#standalone-server",
+    "concepts/langgraph_standalone_container.md": "https://docs.langchain.com/langgraph-platform/self-hosted#data-plane-only",
     "cloud/deployment/cloud.md": "https://docs.langchain.com/langgraph-platform/cloud",
     "cloud/deployment/self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-hybrid",
     "cloud/deployment/self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-self-hosted-full-platform",
-    "cloud/deployment/standalone_container.md": "https://docs.langchain.com/langgraph-platform/deploy-standalone-server",
+    "cloud/deployment/standalone_container.md": "https://docs.langchain.com/langgraph-platform/deploy-data-plane-only",
     "concepts/server-mcp.md": "https://docs.langchain.com/langgraph-platform/server-mcp",
     "cloud/how-tos/human_in_the_loop_time_travel.md": "https://docs.langchain.com/langgraph-platform/human-in-the-loop-time-travel",
     "cloud/how-tos/add-human-in-the-loop.md": "https://docs.langchain.com/langgraph-platform/add-human-in-the-loop",

docs/docs/agents/context.md

@@ -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
         ...
     ```

docs/docs/agents/multi-agent.md

@@ -367,13 +367,13 @@ To implement handoffs with `createReactAgent`, you need to:
 
 3.  Define a parent graph that contains individual agents as nodes:
 
-    ```typescript
-    import { StateGraph, MessagesZodState } from "@langchain/langgraph";
-    const multiAgentGraph = new StateGraph(MessagesZodState)
-      .addNode("flight_assistant", flightAssistant)
-      .addNode("hotel_assistant", hotelAssistant)
-      // ...
-    ```
+        ```typescript
+        import { StateGraph, MessagesZodState } from "@langchain/langgraph";
+        const multiAgentGraph = new StateGraph(MessagesZodState)
+          .addNode("flight_assistant", flightAssistant)
+          .addNode("hotel_assistant", hotelAssistant)
+          // ...
+        ```
 
     :::
 
@@ -619,8 +619,7 @@ for await (const chunk of multiAgentGraph.stream({
 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.
 5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
-
-:::
+   :::
 
 !!! Note
 

docs/docs/cloud/deployment/cloud.md

@@ -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
 

docs/docs/cloud/reference/cli.md

@@ -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"

docs/docs/concepts/low_level.md

@@ -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

docs/docs/concepts/mcp.md

@@ -6,14 +6,7 @@
 
 Install the `langchain-mcp-adapters` library to use MCP tools in LangGraph:
 
-:::python
 ```bash
 pip install langchain-mcp-adapters
 ```
-:::
 
-:::js
-```bash
-npm install @langchain/mcp-adapters
-```
-:::

docs/docs/concepts/memory.md

@@ -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.

docs/docs/concepts/multi_agent.md

@@ -897,5 +897,5 @@ 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.

docs/docs/concepts/persistence.md

@@ -315,8 +315,7 @@ In our example, the output of `get_state_history` will look like this:
         tasks=(),
     ),
     StateSnapshot(
-        values={'foo': 'a', 'bar': ['a']},
-        next=('node_b',),
+        values={'foo': 'a', 'bar': ['a']}, next=('node_b',),
         config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f9-6ec4-8001-31981c2c39f8'}},
         metadata={'source': 'loop', 'writes': {'node_a': {'foo': 'a', 'bar': ['a']}}, 'step': 1},
         created_at='2024-08-29T19:19:38.819946+00:00',

docs/docs/concepts/plans.md

@@ -12,7 +12,7 @@ There are three different plans for using it.
 
 - **Developer**: All [LangSmith](https://smith.langchain.com/) users have access to this plan. You can sign up for this plan simply by creating a LangSmith account. This gives you access to the [local deployment](./deployment_options.md#free-deployment) option.
 - **Plus**: All [LangSmith](https://smith.langchain.com/) users with a [Plus account](https://docs.smith.langchain.com/administration/pricing) have access to this plan. You can sign up for this plan simply by upgrading your LangSmith account to the Plus plan type. This gives you access to the [Cloud](./deployment_options.md#cloud-saas) deployment option.
-- **Enterprise**: This is separate from LangSmith plans. You can sign up for this plan by [contacting our sales team](https://www.langchain.com/contact-sales). This gives you access to all [deployment options](./deployment_options.md).
+- **Enterprise**: This is separate from LangSmith plans. You can sign up for this plan by contacting sales@langchain.dev. This gives you access to all [deployment options](./deployment_options.md).
 
 
 ## Plan Details

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

@@ -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'}

docs/docs/how-tos/multi_agent.md

@@ -22,7 +22,6 @@ To set up communication between the agents in a multi-agent system you can use [
 
 To implement handoffs, you can return `Command` objects from your agent nodes or tools:
 
-:::python
 ```python
 from typing import Annotated
 from langchain_core.tools import tool, InjectedToolCallId
@@ -74,109 +73,25 @@ def create_handoff_tool(*, agent_name: str, description: str | None = None):
         commands = [tools_by_name[tool_call["name"]].invoke(tool_call) for tool_call in tool_calls]
         return commands
     ```
-:::
-
-:::js
-```typescript
-import { tool } from "@langchain/core/tools";
-import { Command, MessagesZodState } from "@langchain/langgraph";
-import { z } from "zod";
-
-function createHandoffTool({
-  agentName,
-  description,
-}: {
-  agentName: string;
-  description?: string;
-}) {
-  const name = `transfer_to_${agentName}`;
-  const toolDescription = description || `Transfer to ${agentName}`;
-
-  return tool(
-    async (_, config) => {
-      // (1)!
-      const state = config.state;
-      const toolCallId = config.toolCall.id;
-
-      const toolMessage = {
-        role: "tool" as const,
-        content: `Successfully transferred to ${agentName}`,
-        name: name,
-        tool_call_id: toolCallId,
-      };
-
-      return new Command({
-        // (3)!
-        goto: agentName,
-        // (4)!
-        update: { messages: [...state.messages, toolMessage] },
-        // (5)!
-        graph: Command.PARENT,
-      });
-    },
-    {
-      name,
-      description: toolDescription,
-      schema: z.object({}),
-    }
-  );
-}
-```
-
-1. Access the [state](../concepts/low_level.md#state) of the agent that is calling the handoff tool through the `config` parameter.
-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.
-5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
-
-!!! tip
-
-    If you want to use tools that return `Command`, you can either use prebuilt @[`create_react_agent`][create_react_agent] / @[`ToolNode`][ToolNode] components, or implement your own tool-executing node that collects `Command` objects returned by the tools and returns a list of them, e.g.:
-    
-    ```typescript
-    const callTools = async (state) => {
-      // ...
-      const commands = await Promise.all(
-        toolCalls.map(toolCall => toolsByName[toolCall.name].invoke(toolCall))
-      );
-      return commands;
-    };
-    ```
-:::
 
 !!! Important
 
     This handoff implementation assumes that:
     
-    - each agent receives overall message history (across all agents) in the multi-agent system as its input. If you want more control over agent inputs, see [this section](#control-agent-inputs)
-    - each agent outputs its internal messages history to the overall message history of the multi-agent system. If you want more control over **how agent outputs are added**, wrap the agent in a separate node function:
+      - each agent receives overall message history (across all agents) in the multi-agent system as its input. If you want more control over agent inputs, see [this section](#control-agent-inputs)
+      - each agent outputs its internal messages history to the overall message history of the multi-agent system. If you want more control over **how agent outputs are added**, wrap the agent in a separate node function:
 
-      :::python
-      ```python
-      def call_hotel_assistant(state):
-          # return agent's final response,
-          # excluding inner monologue
-          response = hotel_assistant.invoke(state)
-          # highlight-next-line
-          return {"messages": response["messages"][-1]}
-      ```
-      :::
-
-      :::js
-      ```typescript
-      const callHotelAssistant = async (state) => {
-        // return agent's final response,
-        // excluding inner monologue
-        const response = await hotelAssistant.invoke(state);
-        // highlight-next-line
-        return { messages: [response.messages.at(-1)] };
-      };
-      ```
-      :::
+        ```python
+        def call_hotel_assistant(state):
+            # return agent's final response,
+            # excluding inner monologue
+            response = hotel_assistant.invoke(state)
+            # highlight-next-line
+            return {"messages": response["messages"][-1]}
+        ```
 
 ### Control agent inputs
 
-:::python
 You can use the @[`Send()`][Send] primitive to directly send data to the worker agents during the handoff. For example, you can request that the calling agent populate a task description for the next agent:
 
 ```python
@@ -214,63 +129,6 @@ def create_task_description_handoff_tool(
 
     return handoff_tool
 ```
-:::
-
-:::js
-You can use the @[`Send()`][Send] primitive to directly send data to the worker agents during the handoff. For example, you can request that the calling agent populate a task description for the next agent:
-
-```typescript
-import { tool } from "@langchain/core/tools";
-import { Command, Send, MessagesZodState } from "@langchain/langgraph";
-import { z } from "zod";
-
-function createTaskDescriptionHandoffTool({
-  agentName,
-  description,
-}: {
-  agentName: string;
-  description?: string;
-}) {
-  const name = `transfer_to_${agentName}`;
-  const toolDescription = description || `Ask ${agentName} for help.`;
-
-  return tool(
-    async (
-      { taskDescription },
-      config
-    ) => {
-      const state = config.state;
-      
-      const taskDescriptionMessage = {
-        role: "user" as const,
-        content: taskDescription,
-      };
-      const agentInput = {
-        ...state,
-        messages: [taskDescriptionMessage],
-      };
-      
-      return new Command({
-        // highlight-next-line
-        goto: [new Send(agentName, agentInput)],
-        graph: Command.PARENT,
-      });
-    },
-    {
-      name,
-      description: toolDescription,
-      schema: z.object({
-        taskDescription: z
-          .string()
-          .describe(
-            "Description of what the next agent should do, including all of the relevant context."
-          ),
-      }),
-    }
-  );
-}
-```
-:::
 
 See the multi-agent [supervisor](../tutorials/multi_agent/agent_supervisor.md#4-create-delegation-tasks) example for a full example of using @[`Send()`][Send] in handoffs.
 
@@ -278,7 +136,6 @@ See the multi-agent [supervisor](../tutorials/multi_agent/agent_supervisor.md#4-
 
 You can use handoffs in any agents built with LangGraph. We recommend using the prebuilt [agent](../agents/overview.md) or [`ToolNode`](./tool-calling.md#toolnode), as they natively support handoffs tools returning `Command`. Below is an example of how you can implement a multi-agent system for booking travel using handoffs:
 
-:::python
 ```python
 from langgraph.prebuilt import create_react_agent
 from langgraph.graph import StateGraph, START, MessagesState
@@ -319,65 +176,9 @@ multi_agent_graph = (
     .compile()
 )
 ```
-:::
-
-:::js
-```typescript
-import { createReactAgent } from "@langchain/langgraph/prebuilt";
-import { StateGraph, START, MessagesZodState } from "@langchain/langgraph";
-import { z } from "zod";
-
-function createHandoffTool({
-  agentName,
-  description,
-}: {
-  agentName: string;
-  description?: string;
-}) {
-  // same implementation as above
-  // ...
-  return new Command(/* ... */);
-}
-
-// Handoffs
-const transferToHotelAssistant = createHandoffTool({
-  agentName: "hotel_assistant",
-});
-const transferToFlightAssistant = createHandoffTool({
-  agentName: "flight_assistant",
-});
-
-// Define agents
-const flightAssistant = createReactAgent({
-  llm: model,
-  // highlight-next-line
-  tools: [/* ... */, transferToHotelAssistant],
-  // highlight-next-line
-  name: "flight_assistant",
-});
-
-const hotelAssistant = createReactAgent({
-  llm: model,
-  // highlight-next-line
-  tools: [/* ... */, transferToFlightAssistant],
-  // highlight-next-line
-  name: "hotel_assistant",
-});
-
-// Define multi-agent graph
-const multiAgentGraph = new StateGraph(MessagesZodState)
-  // highlight-next-line
-  .addNode("flight_assistant", flightAssistant)
-  // highlight-next-line
-  .addNode("hotel_assistant", hotelAssistant)
-  .addEdge(START, "flight_assistant")
-  .compile();
-```
-:::
 
 ??? example "Full example: Multi-agent system for booking travel"
 
-    :::python
     ```python
     from typing import Annotated
     from langchain_core.messages import convert_to_messages
@@ -522,183 +323,6 @@ const multiAgentGraph = new StateGraph(MessagesZodState)
     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.
     5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
-    :::
-
-    :::js
-    ```typescript
-    import { tool } from "@langchain/core/tools";
-    import { createReactAgent } from "@langchain/langgraph/prebuilt";
-    import { StateGraph, START, MessagesZodState, Command } from "@langchain/langgraph";
-    import { ChatAnthropic } from "@langchain/anthropic";
-    import { isBaseMessage } from "@langchain/core/messages";
-    import { z } from "zod";
-
-    // We'll use a helper to render the streamed agent outputs nicely
-    const prettyPrintMessages = (update: Record<string, any>) => {
-      // Handle tuple case with namespace
-      if (Array.isArray(update)) {
-        const [ns, updateData] = update;
-        // Skip parent graph updates in the printouts
-        if (ns.length === 0) {
-          return;
-        }
-
-        const graphId = ns[ns.length - 1].split(":")[0];
-        console.log(`Update from subgraph ${graphId}:\n`);
-        update = updateData;
-      }
-
-      for (const [nodeName, updateValue] of Object.entries(update)) {
-        console.log(`Update from node ${nodeName}:\n`);
-
-        const messages = updateValue.messages || [];
-        for (const message of messages) {
-          if (isBaseMessage(message)) {
-            const textContent =
-              typeof message.content === "string"
-                ? message.content
-                : JSON.stringify(message.content);
-            console.log(`${message.getType()}: ${textContent}`);
-          }
-        }
-        console.log("\n");
-      }
-    };
-
-    function createHandoffTool({
-      agentName,
-      description,
-    }: {
-      agentName: string;
-      description?: string;
-    }) {
-      const name = `transfer_to_${agentName}`;
-      const toolDescription = description || `Transfer to ${agentName}`;
-
-      return tool(
-        async (_, config) => {
-          // highlight-next-line
-          const state = config.state; // (1)!
-          const toolCallId = config.toolCall.id;
-
-          const toolMessage = {
-            role: "tool" as const,
-            content: `Successfully transferred to ${agentName}`,
-            name: name,
-            tool_call_id: toolCallId,
-          };
-
-          return new Command({
-            // highlight-next-line
-            goto: agentName, // (3)!
-            // highlight-next-line
-            update: { messages: [...state.messages, toolMessage] }, // (4)!
-            // highlight-next-line
-            graph: Command.PARENT, // (5)!
-          });
-        },
-        {
-          name,
-          description: toolDescription,
-          schema: z.object({}),
-        }
-      );
-    }
-
-    // Handoffs
-    const transferToHotelAssistant = createHandoffTool({
-      agentName: "hotel_assistant",
-      description: "Transfer user to the hotel-booking assistant.",
-    });
-
-    const transferToFlightAssistant = createHandoffTool({
-      agentName: "flight_assistant",
-      description: "Transfer user to the flight-booking assistant.",
-    });
-
-    // Simple agent tools
-    const bookHotel = tool(
-      async ({ hotelName }) => {
-        return `Successfully booked a stay at ${hotelName}.`;
-      },
-      {
-        name: "book_hotel",
-        description: "Book a hotel",
-        schema: z.object({
-          hotelName: z.string(),
-        }),
-      }
-    );
-
-    const bookFlight = tool(
-      async ({ fromAirport, toAirport }) => {
-        return `Successfully booked a flight from ${fromAirport} to ${toAirport}.`;
-      },
-      {
-        name: "book_flight",
-        description: "Book a flight",
-        schema: z.object({
-          fromAirport: z.string(),
-          toAirport: z.string(),
-        }),
-      }
-    );
-
-    const model = new ChatAnthropic({
-      model: "claude-3-5-sonnet-latest",
-    });
-
-    // Define agents
-    const flightAssistant = createReactAgent({
-      llm: model,
-      // highlight-next-line
-      tools: [bookFlight, transferToHotelAssistant],
-      prompt: "You are a flight booking assistant",
-      // highlight-next-line
-      name: "flight_assistant",
-    });
-
-    const hotelAssistant = createReactAgent({
-      llm: model,
-      // highlight-next-line
-      tools: [bookHotel, transferToFlightAssistant],
-      prompt: "You are a hotel booking assistant",
-      // highlight-next-line
-      name: "hotel_assistant",
-    });
-
-    // Define multi-agent graph
-    const multiAgentGraph = new StateGraph(MessagesZodState)
-      .addNode("flight_assistant", flightAssistant)
-      .addNode("hotel_assistant", hotelAssistant)
-      .addEdge(START, "flight_assistant")
-      .compile();
-
-    // Run the multi-agent graph
-    const stream = await multiAgentGraph.stream(
-      {
-        messages: [
-          {
-            role: "user",
-            content: "book a flight from BOS to JFK and a stay at McKittrick Hotel",
-          },
-        ],
-      },
-      // highlight-next-line
-      { subgraphs: true }
-    );
-
-    for await (const chunk of stream) {
-      prettyPrintMessages(chunk);
-    }
-    ```
-
-    1. Access agent's state
-    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.
-    5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
-    :::
 
 ## Multi-turn conversation
 
@@ -709,7 +333,6 @@ The agents can then be implemented as nodes in a graph that executes agent steps
 1. **Wait for user input** to continue the conversation, or  
 2. **Route to another agent** (or back to itself, such as in a loop) via a [handoff](#handoffs)
 
-:::python
 ```python
 def human(state) -> Command[Literal["agent", "another_agent"]]:
     """A node for collecting user input."""
@@ -737,44 +360,6 @@ def agent(state) -> Command[Literal["agent", "another_agent", "human"]]:
     else:
         return Command(goto="human") # Go to human node
 ```
-:::
-
-:::js
-```typescript
-import { interrupt, Command } from "@langchain/langgraph";
-
-function human(state: MessagesState): Command {
-  const userInput: string = interrupt("Ready for user input.");
-
-  // Determine the active agent
-  const activeAgent = /* ... */;
-
-  return new Command({
-    update: {
-      messages: [{
-        role: "human",
-        content: userInput,
-      }]
-    },
-    goto: activeAgent,
-  });
-}
-
-function agent(state: MessagesState): Command {
-  // The condition for routing/halting can be anything, e.g. LLM tool call / structured output, etc.
-  const goto = getNextAgent(/* ... */); // 'agent' / 'anotherAgent'
-
-  if (goto) {
-    return new Command({
-      goto,
-      update: { myStateKey: "myStateValue" }
-    });
-  }
-
-  return new Command({ goto: "human" });
-}
-```
-:::
 
 ??? example "Full example: multi-agent system for travel recommendations"
 
@@ -785,7 +370,6 @@ function agent(state: MessagesState): Command {
     * travel_advisor: can help with travel destination recommendations. Can ask hotel_advisor for help.
     * hotel_advisor: can help with hotel recommendations. Can ask travel_advisor for help.
 
-    :::python
     ```python
     from langchain_anthropic import ChatAnthropic
     from langgraph.graph import MessagesState, StateGraph, START
@@ -987,267 +571,10 @@ function agent(state: MessagesState): Command {
     
     Would you like more specific information about any of these activities or would you like to know about other options in the area?
     ```
-    :::
-
-    :::js
-    ```typescript
-    import { ChatAnthropic } from "@langchain/anthropic";
-    import { StateGraph, START, MessagesZodState, Command, interrupt, MemorySaver } from "@langchain/langgraph";
-    import { createReactAgent } from "@langchain/langgraph/prebuilt";
-    import { tool } from "@langchain/core/tools";
-    import { z } from "zod";
-
-    const model = new ChatAnthropic({ model: "claude-3-5-sonnet-latest" });
-
-    const MultiAgentState = MessagesZodState.extend({
-      lastActiveAgent: z.string().optional(),
-    });
-
-    // Define travel advisor tools
-    const getTravelRecommendations = tool(
-      async () => {
-        // Placeholder implementation
-        return "Based on current trends, I recommend visiting Japan, Portugal, or New Zealand.";
-      },
-      {
-        name: "get_travel_recommendations",
-        description: "Get current travel destination recommendations",
-        schema: z.object({}),
-      }
-    );
-
-    const makeHandoffTool = (agentName: string) => {
-      return tool(
-        async (_, config) => {
-          const state = config.state;
-          const toolCallId = config.toolCall.id;
-
-          const toolMessage = {
-            role: "tool" as const,
-            content: `Successfully transferred to ${agentName}`,
-            name: `transfer_to_${agentName}`,
-            tool_call_id: toolCallId,
-          };
-
-          return new Command({
-            goto: agentName,
-            update: { messages: [...state.messages, toolMessage] },
-            graph: Command.PARENT,
-          });
-        },
-        {
-          name: `transfer_to_${agentName}`,
-          description: `Transfer to ${agentName}`,
-          schema: z.object({}),
-        }
-      );
-    };
-
-    const travelAdvisorTools = [
-      getTravelRecommendations,
-      makeHandoffTool("hotel_advisor"),
-    ];
-
-    const travelAdvisor = createReactAgent({
-      llm: model,
-      tools: travelAdvisorTools,
-      prompt: [
-        "You are a general travel expert that can recommend travel destinations (e.g. countries, cities, etc). ",
-        "If you need hotel recommendations, ask 'hotel_advisor' for help. ",
-        "You MUST include human-readable response before transferring to another agent."
-      ].join("")
-    });
-
-    const callTravelAdvisor = async (
-      state: z.infer<typeof MultiAgentState>
-    ): Promise<Command> => {
-      const response = await travelAdvisor.invoke(state);
-      const update = { ...response, lastActiveAgent: "travel_advisor" };
-      return new Command({ update, goto: "human" });
-    };
-
-    // Define hotel advisor tools
-    const getHotelRecommendations = tool(
-      async () => {
-        // Placeholder implementation
-        return "I recommend the Ritz-Carlton for luxury stays or boutique hotels for unique experiences.";
-      },
-      {
-        name: "get_hotel_recommendations",
-        description: "Get hotel recommendations for destinations",
-        schema: z.object({}),
-      }
-    );
-
-    const hotelAdvisorTools = [
-      getHotelRecommendations,
-      makeHandoffTool("travel_advisor"),
-    ];
-
-    const hotelAdvisor = createReactAgent({
-      llm: model,
-      tools: hotelAdvisorTools,
-      prompt: [
-        "You are a hotel expert that can provide hotel recommendations for a given destination. ",
-        "If you need help picking travel destinations, ask 'travel_advisor' for help.",
-        "You MUST include human-readable response before transferring to another agent."
-      ].join("")
-    });
-
-    const callHotelAdvisor = async (
-      state: z.infer<typeof MultiAgentState>
-    ): Promise<Command> => {
-      const response = await hotelAdvisor.invoke(state);
-      const update = { ...response, lastActiveAgent: "hotel_advisor" };
-      return new Command({ update, goto: "human" });
-    };
-
-    const humanNode = async (
-      state: z.infer<typeof MultiAgentState>
-    ): Promise<Command> => {
-      const userInput: string = interrupt("Ready for user input.");
-      const activeAgent = state.lastActiveAgent || "travel_advisor";
-
-      return new Command({
-        update: {
-          messages: [
-            {
-              role: "human",
-              content: userInput,
-            }
-          ]
-        },
-        goto: activeAgent,
-      });
-    };
-
-    const builder = new StateGraph(MultiAgentState)
-      .addNode("travel_advisor", callTravelAdvisor)
-      .addNode("hotel_advisor", callHotelAdvisor)
-      .addNode("human", humanNode)
-      .addEdge(START, "travel_advisor");
-
-    const checkpointer = new MemorySaver();
-    const graph = builder.compile({ checkpointer });
-    ```
-    
-    Let's test a multi turn conversation with this application.
-
-    ```typescript
-    import { v4 as uuidv4 } from "uuid";
-    import { Command } from "@langchain/langgraph";
-
-    const threadConfig = { configurable: { thread_id: uuidv4() } };
-
-    const inputs = [
-      // 1st round of conversation
-      {
-        messages: [
-          { role: "user", content: "i wanna go somewhere warm in the caribbean" }
-        ]
-      },
-      // Since we're using `interrupt`, we'll need to resume using the Command primitive.
-      // 2nd round of conversation
-      new Command({
-        resume: "could you recommend a nice hotel in one of the areas and tell me which area it is."
-      }),
-      // 3rd round of conversation
-      new Command({
-        resume: "i like the first one. could you recommend something to do near the hotel?"
-      }),
-    ];
-
-    for (const [idx, userInput] of inputs.entries()) {
-      console.log();
-      console.log(`--- Conversation Turn ${idx + 1} ---`);
-      console.log();
-      console.log(`User: ${JSON.stringify(userInput)}`);
-      console.log();
-      
-      for await (const update of await graph.stream(
-        userInput,
-        { ...threadConfig, streamMode: "updates" }
-      )) {
-        for (const [nodeId, value] of Object.entries(update)) {
-          if (value?.messages?.length) {
-            const lastMessage = value.messages.at(-1);
-            if (lastMessage?.getType?.() === "ai") {
-              console.log(`${nodeId}: ${lastMessage.content}`);
-            }
-          }
-        }
-      }
-    }
-    ```
-    
-    ```
-    --- Conversation Turn 1 ---
-    
-    User: {"messages":[{"role":"user","content":"i wanna go somewhere warm in the caribbean"}]}
-    
-    travel_advisor: Based on the recommendations, Aruba would be an excellent choice for your Caribbean getaway! Aruba is known as "One Happy Island" and offers:
-    - Year-round warm weather with consistent temperatures around 82°F (28°C)
-    - Beautiful white sand beaches like Eagle Beach and Palm Beach
-    - Clear turquoise waters perfect for swimming and snorkeling
-    - Minimal rainfall and location outside the hurricane belt
-    - A blend of Caribbean and Dutch culture
-    - Great dining options and nightlife
-    - Various water sports and activities
-    
-    Would you like me to get some specific hotel recommendations in Aruba for your stay? I can transfer you to our hotel advisor who can help with accommodations.
-    
-    --- Conversation Turn 2 ---
-    
-    User: Command { resume: 'could you recommend a nice hotel in one of the areas and tell me which area it is.' }
-    
-    hotel_advisor: Based on the recommendations, I can suggest two excellent options:
-    
-    1. The Ritz-Carlton, Aruba - Located in Palm Beach
-    - This luxury resort is situated in the vibrant Palm Beach area
-    - Known for its exceptional service and amenities
-    - Perfect if you want to be close to dining, shopping, and entertainment
-    - Features multiple restaurants, a casino, and a world-class spa
-    - Located on a pristine stretch of Palm Beach
-    
-    2. Bucuti & Tara Beach Resort - Located in Eagle Beach
-    - An adults-only boutique resort on Eagle Beach
-    - Known for being more intimate and peaceful
-    - Award-winning for its sustainability practices
-    - Perfect for a romantic getaway or peaceful vacation
-    - Located on one of the most beautiful beaches in the Caribbean
-    
-    Would you like more specific information about either of these properties or their locations?
-    
-    --- Conversation Turn 3 ---
-    
-    User: Command { resume: 'i like the first one. could you recommend something to do near the hotel?' }
-    
-    travel_advisor: Near the Ritz-Carlton in Palm Beach, here are some highly recommended activities:
-    
-    1. Visit the Palm Beach Plaza Mall - Just a short walk from the hotel, featuring shopping, dining, and entertainment
-    2. Try your luck at the Stellaris Casino - It's right in the Ritz-Carlton
-    3. Take a sunset sailing cruise - Many depart from the nearby pier
-    4. Visit the California Lighthouse - A scenic landmark just north of Palm Beach
-    5. Enjoy water sports at Palm Beach:
-       - Jet skiing
-       - Parasailing
-       - Snorkeling
-       - Stand-up paddleboarding
-    
-    Would you like more specific information about any of these activities or would you like to know about other options in the area?
-    ```
-    :::
 
 ## Prebuilt implementations
 
 LangGraph comes with prebuilt implementations of two of the most popular multi-agent architectures:
 
-:::python
 - [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-py) 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-py) library to create a swarm multi-agent systems.
-:::
-
-:::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.
-:::
+- [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-py) library to create a swarm multi-agent systems. 

docs/docs/how-tos/subgraph.md

@@ -9,20 +9,11 @@ When adding subgraphs, you need to define how the parent graph and the subgraph
 
 ## Setup
 
-:::python
 ```bash
 pip install -U langgraph
 ```
-:::
-
-:::js
-```bash
-npm install @langchain/langgraph
-```
-:::
 
 !!! tip "Set up LangSmith for LangGraph development"
-
     Sign up for [LangSmith](https://smith.langchain.com) to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started [here](https://docs.smith.langchain.com).
 
 ## Shared state schemas
@@ -31,7 +22,6 @@ A common case is for the parent graph and subgraph to communicate over a shared
 
 If your subgraph shares state keys with the parent graph, you can follow these steps to add it to your graph:
 
-:::python
 1. Define the subgraph workflow (`subgraph_builder` in the example below) and compile it
 2. Pass compiled subgraph to the `.add_node` method when defining the parent graph workflow
 
@@ -59,41 +49,9 @@ builder.add_node("node_1", subgraph)
 builder.add_edge(START, "node_1")
 graph = builder.compile()
 ```
-:::
-
-:::js
-1. Define the subgraph workflow (`subgraphBuilder` in the example below) and compile it
-2. Pass compiled subgraph to the `.addNode` method when defining the parent graph workflow
-
-```typescript
-import { StateGraph, START } from "@langchain/langgraph";
-import { z } from "zod";
-
-const State = z.object({
-  foo: z.string(),
-});
-
-// Subgraph
-const subgraphBuilder = new StateGraph(State)
-  .addNode("subgraphNode1", (state) => {
-    return { foo: "hi! " + state.foo };
-  })
-  .addEdge(START, "subgraphNode1");
-
-const subgraph = subgraphBuilder.compile();
-
-// Parent graph
-const builder = new StateGraph(State)
-  .addNode("node1", subgraph)
-  .addEdge(START, "node1");
-
-const graph = builder.compile();
-```
-:::
 
 ??? example "Full example: shared state schemas"
 
-    :::python
     ```python
     from typing_extensions import TypedDict
     from langgraph.graph.state import StateGraph, START
@@ -143,61 +101,6 @@ const graph = builder.compile();
     {'node_1': {'foo': 'hi! foo'}}
     {'node_2': {'foo': 'hi! foobar'}}
     ```
-    :::
-
-    :::js
-    ```typescript
-    import { StateGraph, START } from "@langchain/langgraph";
-    import { z } from "zod";
-
-    // Define subgraph
-    const SubgraphState = z.object({
-      foo: z.string(),  // (1)! 
-      bar: z.string(),  // (2)!
-    });
-    
-    const subgraphBuilder = new StateGraph(SubgraphState)
-      .addNode("subgraphNode1", (state) => {
-        return { bar: "bar" };
-      })
-      .addNode("subgraphNode2", (state) => {
-        // note that this node is using a state key ('bar') that is only available in the subgraph
-        // and is sending update on the shared state key ('foo')
-        return { foo: state.foo + state.bar };
-      })
-      .addEdge(START, "subgraphNode1")
-      .addEdge("subgraphNode1", "subgraphNode2");
-    
-    const subgraph = subgraphBuilder.compile();
-    
-    // Define parent graph
-    const ParentState = z.object({
-      foo: z.string(),
-    });
-    
-    const builder = new StateGraph(ParentState)
-      .addNode("node1", (state) => {
-        return { foo: "hi! " + state.foo };
-      })
-      .addNode("node2", subgraph)
-      .addEdge(START, "node1")
-      .addEdge("node1", "node2");
-    
-    const graph = builder.compile();
-    
-    for await (const chunk of await graph.stream({ foo: "foo" })) {
-      console.log(chunk);
-    }
-    ```
-
-    3. This key is shared with the parent graph state
-    4. This key is private to the `SubgraphState` and is not visible to the parent graph
-    
-    ```
-    { node1: { foo: 'hi! foo' } }
-    { node2: { foo: 'hi! foobar' } }
-    ```
-    :::
 
 ## Different state schemas
 
@@ -205,7 +108,6 @@ For more complex systems you might want to define subgraphs that have a **comple
 
 If that's the case for your application, you need to define a node **function that invokes the subgraph**. This function needs to transform the input (parent) state to the subgraph state before invoking the subgraph, and transform the results back to the parent state before returning the state update from the node.
 
-:::python
 ```python
 from typing_extensions import TypedDict
 from langgraph.graph.state import StateGraph, START
@@ -240,48 +142,9 @@ graph = builder.compile()
 
 1. Transform the state to the subgraph state
 2. Transform response back to the parent state
-:::
-
-:::js
-```typescript
-import { StateGraph, START } from "@langchain/langgraph";
-import { z } from "zod";
-
-const SubgraphState = z.object({
-  bar: z.string(),
-});
-
-// Subgraph
-const subgraphBuilder = new StateGraph(SubgraphState)
-  .addNode("subgraphNode1", (state) => {
-    return { bar: "hi! " + state.bar };
-  })
-  .addEdge(START, "subgraphNode1");
-
-const subgraph = subgraphBuilder.compile();
-
-// Parent graph
-const State = z.object({
-  foo: z.string(),
-});
-
-const builder = new StateGraph(State)
-  .addNode("node1", async (state) => {
-    const subgraphOutput = await subgraph.invoke({ bar: state.foo }); // (1)!
-    return { foo: subgraphOutput.bar }; // (2)!
-  })
-  .addEdge(START, "node1");
-
-const graph = builder.compile();
-```
-
-1. Transform the state to the subgraph state
-2. Transform response back to the parent state
-:::
 
 ??? example "Full example: different state schemas"
 
-    :::python
     ```python
     from typing_extensions import TypedDict
     from langgraph.graph.state import StateGraph, START
@@ -337,74 +200,11 @@ const graph = builder.compile();
     (('node_2:9c36dd0f-151a-cb42-cbad-fa2f851f9ab7',), {'grandchild_2': {'bar': 'hi! foobaz'}})
     ((), {'node_2': {'foo': 'hi! foobaz'}})
     ```
-    :::
-
-    :::js
-    ```typescript
-    import { StateGraph, START } from "@langchain/langgraph";
-    import { z } from "zod";
-
-    // Define subgraph
-    const SubgraphState = z.object({
-      // note that none of these keys are shared with the parent graph state
-      bar: z.string(),
-      baz: z.string(),
-    });
-    
-    const subgraphBuilder = new StateGraph(SubgraphState)
-      .addNode("subgraphNode1", (state) => {
-        return { baz: "baz" };
-      })
-      .addNode("subgraphNode2", (state) => {
-        return { bar: state.bar + state.baz };
-      })
-      .addEdge(START, "subgraphNode1")
-      .addEdge("subgraphNode1", "subgraphNode2");
-    
-    const subgraph = subgraphBuilder.compile();
-    
-    // Define parent graph
-    const ParentState = z.object({
-      foo: z.string(),
-    });
-    
-    const builder = new StateGraph(ParentState)
-      .addNode("node1", (state) => {
-        return { foo: "hi! " + state.foo };
-      })
-      .addNode("node2", async (state) => {
-        const response = await subgraph.invoke({ bar: state.foo }); // (1)!
-        return { foo: response.bar }; // (2)!
-      })
-      .addEdge(START, "node1")
-      .addEdge("node1", "node2");
-    
-    const graph = builder.compile();
-    
-    for await (const chunk of await graph.stream(
-      { foo: "foo" }, 
-      { subgraphs: true }
-    )) {
-      console.log(chunk);
-    }
-    ```
-
-    3. Transform the state to the subgraph state
-    4. Transform response back to the parent state
-
-    ```
-    [[], { node1: { foo: 'hi! foo' } }]
-    [['node2:9c36dd0f-151a-cb42-cbad-fa2f851f9ab7'], { subgraphNode1: { baz: 'baz' } }]
-    [['node2:9c36dd0f-151a-cb42-cbad-fa2f851f9ab7'], { subgraphNode2: { bar: 'hi! foobaz' } }]
-    [[], { node2: { foo: 'hi! foobaz' } }]
-    ```
-    :::
 
 ??? example "Full example: different state schemas (two levels of subgraphs)"
 
     This is an example with two levels of subgraphs: parent -> child -> grandchild.
 
-    :::python
     ```python
     # Grandchild graph
     from typing_extensions import TypedDict
@@ -488,102 +288,14 @@ const graph = builder.compile();
     ((), {'child': {'my_key': 'hi Bob, how are you today?'}})
     ((), {'parent_2': {'my_key': 'hi Bob, how are you today? bye!'}})
     ```
-    :::
-
-    :::js
-    ```typescript
-    import { StateGraph, START, END } from "@langchain/langgraph";
-    import { z } from "zod";
-
-    // Grandchild graph
-    const GrandChildState = z.object({
-      myGrandchildKey: z.string(),
-    });
-    
-    const grandchild = new StateGraph(GrandChildState)
-      .addNode("grandchild1", (state) => {
-        // NOTE: child or parent keys will not be accessible here
-        return { myGrandchildKey: state.myGrandchildKey + ", how are you" };
-      })
-      .addEdge(START, "grandchild1")
-      .addEdge("grandchild1", END);
-    
-    const grandchildGraph = grandchild.compile();
-    
-    // Child graph
-    const ChildState = z.object({
-      myChildKey: z.string(),
-    });
-    
-    const child = new StateGraph(ChildState)
-      .addNode("child1", async (state) => {
-        // NOTE: parent or grandchild keys won't be accessible here
-        const grandchildGraphInput = { myGrandchildKey: state.myChildKey }; // (1)!
-        const grandchildGraphOutput = await grandchildGraph.invoke(grandchildGraphInput);
-        return { myChildKey: grandchildGraphOutput.myGrandchildKey + " today?" }; // (2)!
-      }) // (3)!
-      .addEdge(START, "child1")
-      .addEdge("child1", END);
-    
-    const childGraph = child.compile();
-    
-    // Parent graph
-    const ParentState = z.object({
-      myKey: z.string(),
-    });
-    
-    const parent = new StateGraph(ParentState)
-      .addNode("parent1", (state) => {
-        // NOTE: child or grandchild keys won't be accessible here
-        return { myKey: "hi " + state.myKey };
-      })
-      .addNode("child", async (state) => {
-        const childGraphInput = { myChildKey: state.myKey }; // (4)!
-        const childGraphOutput = await childGraph.invoke(childGraphInput);
-        return { myKey: childGraphOutput.myChildKey }; // (5)!
-      }) // (6)!
-      .addNode("parent2", (state) => {
-        return { myKey: state.myKey + " bye!" };
-      })
-      .addEdge(START, "parent1")
-      .addEdge("parent1", "child")
-      .addEdge("child", "parent2")
-      .addEdge("parent2", END);
-    
-    const parentGraph = parent.compile();
-    
-    for await (const chunk of await parentGraph.stream(
-      { myKey: "Bob" }, 
-      { subgraphs: true }
-    )) {
-      console.log(chunk);
-    }
-    ```
-
-    7. We're transforming the state from the child state channels (`myChildKey`) to the grandchild state channels (`myGrandchildKey`)
-    8. We're transforming the state from the grandchild state channels (`myGrandchildKey`) back to the child state channels (`myChildKey`)
-    9. We're passing a function here instead of just compiled graph (`grandchildGraph`)
-    10. We're transforming the state from the parent state channels (`myKey`) to the child state channels (`myChildKey`)
-    11. We're transforming the state from the child state channels (`myChildKey`) back to the parent state channels (`myKey`)
-    12. We're passing a function here instead of just a compiled graph (`childGraph`)
-
-    ```
-    [[], { parent1: { myKey: 'hi Bob' } }]
-    [['child:2e26e9ce-602f-862c-aa66-1ea5a4655e3b', 'child1:781bb3b1-3971-84ce-810b-acf819a03f9c'], { grandchild1: { myGrandchildKey: 'hi Bob, how are you' } }]
-    [['child:2e26e9ce-602f-862c-aa66-1ea5a4655e3b'], { child1: { myChildKey: 'hi Bob, how are you today?' } }]
-    [[], { child: { myKey: 'hi Bob, how are you today?' } }]
-    [[], { parent2: { myKey: 'hi Bob, how are you today? bye!' } }]
-    ```
-    :::
 
 ## Add persistence 
 
 You only need to **provide the checkpointer when compiling the parent graph**. LangGraph will automatically propagate the checkpointer to the child subgraphs.
 
-:::python
 ```python
 from langgraph.graph import START, StateGraph
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from typing_extensions import TypedDict
 
 class State(TypedDict):
@@ -605,66 +317,20 @@ builder = StateGraph(State)
 builder.add_node("node_1", subgraph)
 builder.add_edge(START, "node_1")
 
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 graph = builder.compile(checkpointer=checkpointer)
 ```    
-:::
 
-:::js
-```typescript
-import { StateGraph, START, MemorySaver } from "@langchain/langgraph";
-import { z } from "zod";
+If you want the subgraph to **have its own memory**, you can compile it `with checkpointer=True`. This is useful in [multi-agent](../concepts/multi_agent.md) systems, if you want agents to keep track of their internal message histories:
 
-const State = z.object({
-  foo: z.string(),
-});
-
-// Subgraph
-const subgraphBuilder = new StateGraph(State)
-  .addNode("subgraphNode1", (state) => {
-    return { foo: state.foo + "bar" };
-  })
-  .addEdge(START, "subgraphNode1");
-
-const subgraph = subgraphBuilder.compile();
-
-// Parent graph
-const builder = new StateGraph(State)
-  .addNode("node1", subgraph)
-  .addEdge(START, "node1");
-
-const checkpointer = new MemorySaver();
-const graph = builder.compile({ checkpointer });
-```    
-:::
-
-If you want the subgraph to **have its own memory**, you can compile it with the appropriate checkpointer option. This is useful in [multi-agent](../concepts/multi_agent.md) systems, if you want agents to keep track of their internal message histories:
-
-:::python
 ```python
 subgraph_builder = StateGraph(...)
 subgraph = subgraph_builder.compile(checkpointer=True)
 ```
-:::
-
-:::js
-```typescript
-const subgraphBuilder = new StateGraph(...)
-const subgraph = subgraphBuilder.compile({ checkpointer: true });
-```
-:::
 
 ## View subgraph state
 
-When you enable [persistence](../concepts/persistence.md), you can [inspect the graph state](../concepts/persistence.md#checkpoints) (checkpoint) via the appropriate method. To view the subgraph state, you can use the subgraphs option.
-
-:::python
-You can inspect the graph state via `graph.get_state(config)`. To view the subgraph state, you can use `graph.get_state(config, subgraphs=True)`.
-:::
-
-:::js
-You can inspect the graph state via `graph.getState(config)`. To view the subgraph state, you can use `graph.getState(config, { subgraphs: true })`.
-:::
+When you enable [persistence](../concepts/persistence.md), you can [inspect the graph state](../concepts/persistence.md#checkpoints) (checkpoint) via `graph.get_state(config)`. To view the subgraph state, you can use `graph.get_state(config, subgraphs=True)`.
 
 !!! important "Available **only** when interrupted"
 
@@ -672,10 +338,9 @@ You can inspect the graph state via `graph.getState(config)`. To view the subgra
 
 ??? example "View interrupted subgraph state"
 
-    :::python
     ```python
     from langgraph.graph import START, StateGraph
-    from langgraph.checkpoint.memory import MemorySaver
+    from langgraph.checkpoint.memory import InMemorySaver
     from langgraph.types import interrupt, Command
     from typing_extensions import TypedDict
     
@@ -700,7 +365,7 @@ You can inspect the graph state via `graph.getState(config)`. To view the subgra
     builder.add_node("node_1", subgraph)
     builder.add_edge(START, "node_1")
     
-    checkpointer = MemorySaver()
+    checkpointer = InMemorySaver()
     graph = builder.compile(checkpointer=checkpointer)
     
     config = {"configurable": {"thread_id": "1"}}
@@ -714,53 +379,11 @@ You can inspect the graph state via `graph.getState(config)`. To view the subgra
     ```
     
     1. This will be available only when the subgraph is interrupted. Once you resume the graph, you won't be able to access the subgraph state.
-    :::
-
-    :::js
-    ```typescript
-    import { StateGraph, START, MemorySaver, interrupt, Command } from "@langchain/langgraph";
-    import { z } from "zod";
-    
-    const State = z.object({
-      foo: z.string(),
-    });
-    
-    // Subgraph
-    const subgraphBuilder = new StateGraph(State)
-      .addNode("subgraphNode1", (state) => {
-        const value = interrupt("Provide value:");
-        return { foo: state.foo + value };
-      })
-      .addEdge(START, "subgraphNode1");
-    
-    const subgraph = subgraphBuilder.compile();
-    
-    // Parent graph
-    const builder = new StateGraph(State)
-      .addNode("node1", subgraph)
-      .addEdge(START, "node1");
-    
-    const checkpointer = new MemorySaver();
-    const graph = builder.compile({ checkpointer });
-    
-    const config = { configurable: { thread_id: "1" } };
-    
-    await graph.invoke({ foo: "" }, config);
-    const parentState = await graph.getState(config);
-    const subgraphState = (await graph.getState(config, { subgraphs: true })).tasks[0].state; // (1)!
-    
-    // resume the subgraph
-    await graph.invoke(new Command({ resume: "bar" }), config);
-    ```
-    
-    2. This will be available only when the subgraph is interrupted. Once you resume the graph, you won't be able to access the subgraph state.
-    :::
 
 ## Stream subgraph outputs
 
-To include outputs from subgraphs in the streamed outputs, you can set the subgraphs option in the stream method of the parent graph. This will stream outputs from both the parent graph and any subgraphs.
+To include outputs from subgraphs in the streamed outputs, you can set `subgraphs=True` in the `.stream()` method of the parent graph. This will stream outputs from both the parent graph and any subgraphs.
 
-:::python
 ```python
 for chunk in graph.stream(
     {"foo": "foo"},
@@ -771,27 +394,9 @@ for chunk in graph.stream(
 ```
 
 1. Set `subgraphs=True` to stream outputs from subgraphs.
-:::
-
-:::js
-```typescript
-for await (const chunk of await graph.stream(
-  { foo: "foo" },
-  {
-    subgraphs: true, // (1)!
-    streamMode: "updates",
-  }
-)) {
-  console.log(chunk);
-}
-```
-
-1. Set `subgraphs: true` to stream outputs from subgraphs.
-:::
 
 ??? example "Stream from subgraphs"
 
-    :::python
     ```python
     from typing_extensions import TypedDict
     from langgraph.graph.state import StateGraph, START
@@ -845,66 +450,4 @@ for await (const chunk of await graph.stream(
     (('node_2:e58e5673-a661-ebb0-70d4-e298a7fc28b7',), {'subgraph_node_1': {'bar': 'bar'}})
     (('node_2:e58e5673-a661-ebb0-70d4-e298a7fc28b7',), {'subgraph_node_2': {'foo': 'hi! foobar'}})
     ((), {'node_2': {'foo': 'hi! foobar'}})
-    ```
-    :::
-
-    :::js
-    ```typescript
-    import { StateGraph, START } from "@langchain/langgraph";
-    import { z } from "zod";
-
-    // Define subgraph
-    const SubgraphState = z.object({
-      foo: z.string(),
-      bar: z.string(),
-    });
-    
-    const subgraphBuilder = new StateGraph(SubgraphState)
-      .addNode("subgraphNode1", (state) => {
-        return { bar: "bar" };
-      })
-      .addNode("subgraphNode2", (state) => {
-        // note that this node is using a state key ('bar') that is only available in the subgraph
-        // and is sending update on the shared state key ('foo')
-        return { foo: state.foo + state.bar };
-      })
-      .addEdge(START, "subgraphNode1")
-      .addEdge("subgraphNode1", "subgraphNode2");
-    
-    const subgraph = subgraphBuilder.compile();
-    
-    // Define parent graph
-    const ParentState = z.object({
-      foo: z.string(),
-    });
-    
-    const builder = new StateGraph(ParentState)
-      .addNode("node1", (state) => {
-        return { foo: "hi! " + state.foo };
-      })
-      .addNode("node2", subgraph)
-      .addEdge(START, "node1")
-      .addEdge("node1", "node2");
-    
-    const graph = builder.compile();
-
-    for await (const chunk of await graph.stream(
-      { foo: "foo" },
-      {
-        streamMode: "updates",
-        subgraphs: true, // (1)!
-      }
-    )) {
-      console.log(chunk);
-    }
-    ```
-  
-    2. Set `subgraphs: true` to stream outputs from subgraphs.
-
-    ```
-    [[], { node1: { foo: 'hi! foo' } }]
-    [['node2:e58e5673-a661-ebb0-70d4-e298a7fc28b7'], { subgraphNode1: { bar: 'bar' } }]
-    [['node2:e58e5673-a661-ebb0-70d4-e298a7fc28b7'], { subgraphNode2: { foo: 'hi! foobar' } }]
-    [[], { node2: { foo: 'hi! foobar' } }]
-    ```
-    :::
+    

docs/docs/tutorials/workflows.md

@@ -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"]

docs/overrides/main.html

@@ -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>
+<strong>LangGraph Platform docs have moved!</strong> Find the LangGraph Platform docs at the new <a href="https://docs.langchain.com/langgraph-platform" target="_blank">LangChain Docs</a> site.
 {% endblock %}

docs/pyproject.toml

@@ -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]

docs/uv.lock

@@ -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" },

examples/persistence_postgres.ipynb

@@ -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"
    ]
   }
  ],

examples/rag/langgraph_crag_local.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",

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

@@ -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,
@@ -16,15 +21,10 @@ from langgraph.checkpoint.base import (
     get_checkpoint_id,
     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
 
@@ -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"]),
                 },
             },

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

@@ -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,
@@ -16,15 +21,10 @@ from langgraph.checkpoint.base import (
     get_checkpoint_id,
     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
 
@@ -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"]),
                 },
             },

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

@@ -5,6 +5,8 @@ from collections.abc import Sequence
 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,
@@ -12,7 +14,6 @@ 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]]
 

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

@@ -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_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.

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

@@ -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"]

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

@@ -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,

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

@@ -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

libs/checkpoint-postgres/tests/test_async.py

@@ -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
 
 
@@ -344,34 +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"] == {}

libs/checkpoint-postgres/tests/test_async_store.py

@@ -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,

libs/checkpoint-postgres/tests/test_store.py

@@ -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"

libs/checkpoint-postgres/tests/test_sync.py

@@ -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
 
 
@@ -332,33 +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"] == {}

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

@@ -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 = (

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

@@ -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)

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

@@ -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
 
 

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

@@ -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,

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

@@ -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,

libs/checkpoint-sqlite/tests/test_aiosqlite.py

@@ -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
 
 

libs/checkpoint-sqlite/tests/test_async_store.py

@@ -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

libs/checkpoint-sqlite/tests/test_sqlite.py

@@ -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

libs/checkpoint-sqlite/tests/test_store.py

@@ -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"

libs/checkpoint-sqlite/tests/test_ttl.py

@@ -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"
-        )

libs/checkpoint/langgraph/cache/redis/__init__.py

@@ -1,144 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Mapping, Sequence
-from typing import Any
-
-from langgraph.cache.base import BaseCache, FullKey, Namespace, ValueT
-from langgraph.checkpoint.serde.base import SerializerProtocol
-
-
-class RedisCache(BaseCache[ValueT]):
-    """Redis-based cache implementation with TTL support."""
-
-    def __init__(
-        self,
-        redis: Any,
-        *,
-        serde: SerializerProtocol | None = None,
-        prefix: str = "langgraph:cache:",
-    ) -> None:
-        """Initialize the cache with a Redis client.
-
-        Args:
-            redis: Redis client instance (sync or async)
-            serde: Serializer to use for values
-            prefix: Key prefix for all cached values
-        """
-        super().__init__(serde=serde)
-        self.redis = redis
-        self.prefix = prefix
-
-    def _make_key(self, ns: Namespace, key: str) -> str:
-        """Create a Redis key from namespace and key."""
-        ns_str = ":".join(ns) if ns else ""
-        return f"{self.prefix}{ns_str}:{key}" if ns_str else f"{self.prefix}{key}"
-
-    def _parse_key(self, redis_key: str) -> tuple[Namespace, str]:
-        """Parse a Redis key back to namespace and key."""
-        if not redis_key.startswith(self.prefix):
-            raise ValueError(
-                f"Key {redis_key} does not start with prefix {self.prefix}"
-            )
-
-        remaining = redis_key[len(self.prefix) :]
-        if ":" in remaining:
-            parts = remaining.split(":")
-            key = parts[-1]
-            ns_parts = parts[:-1]
-            return (tuple(ns_parts), key)
-        else:
-            return (tuple(), remaining)
-
-    def get(self, keys: Sequence[FullKey]) -> dict[FullKey, ValueT]:
-        """Get the cached values for the given keys."""
-        if not keys:
-            return {}
-
-        # Build Redis keys
-        redis_keys = [self._make_key(ns, key) for ns, key in keys]
-
-        # Get values from Redis using MGET
-        try:
-            raw_values = self.redis.mget(redis_keys)
-        except Exception:
-            # If Redis is unavailable, return empty dict
-            return {}
-
-        values: dict[FullKey, ValueT] = {}
-        for i, raw_value in enumerate(raw_values):
-            if raw_value is not None:
-                try:
-                    # Deserialize the value
-                    encoding, data = raw_value.split(b":", 1)
-                    values[keys[i]] = self.serde.loads_typed((encoding.decode(), data))
-                except Exception:
-                    # Skip corrupted entries
-                    continue
-
-        return values
-
-    async def aget(self, keys: Sequence[FullKey]) -> dict[FullKey, ValueT]:
-        """Asynchronously get the cached values for the given keys."""
-        return self.get(keys)
-
-    def set(self, mapping: Mapping[FullKey, tuple[ValueT, int | None]]) -> None:
-        """Set the cached values for the given keys and TTLs."""
-        if not mapping:
-            return
-
-        # Use pipeline for efficient batch operations
-        pipe = self.redis.pipeline()
-
-        for (ns, key), (value, ttl) in mapping.items():
-            redis_key = self._make_key(ns, key)
-            encoding, data = self.serde.dumps_typed(value)
-
-            # Store as "encoding:data" format
-            serialized_value = f"{encoding}:".encode() + data
-
-            if ttl is not None:
-                pipe.setex(redis_key, ttl, serialized_value)
-            else:
-                pipe.set(redis_key, serialized_value)
-
-        try:
-            pipe.execute()
-        except Exception:
-            # Silently fail if Redis is unavailable
-            pass
-
-    async def aset(self, mapping: Mapping[FullKey, tuple[ValueT, int | None]]) -> None:
-        """Asynchronously set the cached values for the given keys and TTLs."""
-        self.set(mapping)
-
-    def clear(self, namespaces: Sequence[Namespace] | None = None) -> None:
-        """Delete the cached values for the given namespaces.
-        If no namespaces are provided, clear all cached values."""
-        try:
-            if namespaces is None:
-                # Clear all keys with our prefix
-                pattern = f"{self.prefix}*"
-                keys = self.redis.keys(pattern)
-                if keys:
-                    self.redis.delete(*keys)
-            else:
-                # Clear specific namespaces
-                keys_to_delete = []
-                for ns in namespaces:
-                    ns_str = ":".join(ns) if ns else ""
-                    pattern = (
-                        f"{self.prefix}{ns_str}:*" if ns_str else f"{self.prefix}*"
-                    )
-                    keys = self.redis.keys(pattern)
-                    keys_to_delete.extend(keys)
-
-                if keys_to_delete:
-                    self.redis.delete(*keys_to_delete)
-        except Exception:
-            # Silently fail if Redis is unavailable
-            pass
-
-    async def aclear(self, namespaces: Sequence[Namespace] | None = None) -> None:
-        """Asynchronously delete the cached values for the given namespaces.
-        If no namespaces are provided, clear all cached values."""
-        self.clear(namespaces)

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

@@ -81,9 +81,6 @@ class Checkpoint(TypedDict):
     This keeps track of the versions of the channels that each node has seen.
     Used to determine which nodes to execute next.
     """
-    updated_channels: list[str] | None
-    """The channels that were updated in this checkpoint.
-    """
 
 
 def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
@@ -95,7 +92,6 @@ def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
         channel_versions=checkpoint["channel_versions"].copy(),
         versions_seen={k: v.copy() for k, v in checkpoint["versions_seen"].items()},
         pending_sends=checkpoint.get("pending_sends", []).copy(),
-        updated_channels=checkpoint.get("updated_channels", None),
     )
 
 
@@ -441,7 +437,6 @@ def empty_checkpoint() -> Checkpoint:
         channel_versions={},
         versions_seen={},
         pending_sends=[],
-        updated_channels=None,
     )
 
 
@@ -475,5 +470,4 @@ def create_checkpoint(
         channel_versions=checkpoint["channel_versions"],
         versions_seen=checkpoint["versions_seen"],
         pending_sends=checkpoint.get("pending_sends", []),
-        updated_channels=None,
     )

libs/checkpoint/langgraph/store/base/batch.py

@@ -64,21 +64,14 @@ class AsyncBatchedBaseStore(BaseStore):
         super().__init__()
         self._loop = asyncio.get_running_loop()
         self._aqueue: asyncio.Queue[tuple[asyncio.Future, Op]] = asyncio.Queue()
-        self._task: asyncio.Task | None = None
-        self._ensure_task()
+        self._task = self._loop.create_task(_run(self._aqueue, weakref.ref(self)))
 
     def __del__(self) -> None:
         try:
-            if self._task:
-                self._task.cancel()
+            self._task.cancel()
         except RuntimeError:
             pass
 
-    def _ensure_task(self) -> None:
-        """Ensure the background processing loop is running."""
-        if self._task is None or self._task.done():
-            self._task = self._loop.create_task(_run(self._aqueue, weakref.ref(self)))
-
     async def aget(
         self,
         namespace: tuple[str, ...],
@@ -86,7 +79,7 @@ class AsyncBatchedBaseStore(BaseStore):
         *,
         refresh_ttl: bool | None = None,
     ) -> Item | None:
-        self._ensure_task()
+        assert not self._task.done()
         fut = self._loop.create_future()
         self._aqueue.put_nowait(
             (
@@ -111,7 +104,7 @@ class AsyncBatchedBaseStore(BaseStore):
         offset: int = 0,
         refresh_ttl: bool | None = None,
     ) -> list[SearchItem]:
-        self._ensure_task()
+        assert not self._task.done()
         fut = self._loop.create_future()
         self._aqueue.put_nowait(
             (
@@ -137,7 +130,7 @@ class AsyncBatchedBaseStore(BaseStore):
         *,
         ttl: float | None | NotProvided = NOT_PROVIDED,
     ) -> None:
-        self._ensure_task()
+        assert not self._task.done()
         _validate_namespace(namespace)
         fut = self._loop.create_future()
         self._aqueue.put_nowait(
@@ -155,7 +148,7 @@ class AsyncBatchedBaseStore(BaseStore):
         namespace: tuple[str, ...],
         key: str,
     ) -> None:
-        self._ensure_task()
+        assert not self._task.done()
         fut = self._loop.create_future()
         self._aqueue.put_nowait((fut, PutOp(namespace, key, None)))
         return await fut
@@ -169,7 +162,7 @@ class AsyncBatchedBaseStore(BaseStore):
         limit: int = 100,
         offset: int = 0,
     ) -> list[tuple[str, ...]]:
-        self._ensure_task()
+        assert not self._task.done()
         fut = self._loop.create_future()
         match_conditions = []
         if prefix:

libs/checkpoint/langgraph/store/base/embed.py

@@ -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 == "*":

libs/checkpoint/pyproject.toml

@@ -32,7 +32,6 @@ dev = [
   "numpy",
   "pandas",
   "pandas-stubs>=2.2.2.240807",
-  "redis",
 ]
 
 [tool.hatch.build.targets.wheel]

libs/checkpoint/tests/test_redis_cache.py

@@ -1,316 +0,0 @@
-"""Unit tests for Redis cache implementation."""
-
-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:
-        """Set up test Redis client and cache."""
-        self.client = redis.Redis(
-            host="localhost", port=6379, db=0, decode_responses=False
-        )
-        try:
-            self.client.ping()
-        except redis.ConnectionError:
-            pytest.skip("Redis server not available")
-
-        self.cache: RedisCache = RedisCache(self.client, prefix="test:cache:")
-
-        # Clean up before each test
-        self.client.flushdb()
-
-    def teardown_method(self) -> None:
-        """Clean up after each test."""
-        try:
-            self.client.flushdb()
-        except Exception:
-            pass
-
-    def test_basic_set_and_get(self) -> None:
-        """Test basic set and get operations."""
-        keys: list[FullKey] = [(("graph", "node"), "key1")]
-        values = {keys[0]: ({"result": 42}, None)}
-
-        # Set value
-        self.cache.set(values)
-
-        # Get value
-        result = self.cache.get(keys)
-        assert len(result) == 1
-        assert result[keys[0]] == {"result": 42}
-
-    def test_batch_operations(self) -> None:
-        """Test batch set and get operations."""
-        keys: list[FullKey] = [
-            (("graph", "node1"), "key1"),
-            (("graph", "node2"), "key2"),
-            (("other", "node"), "key3"),
-        ]
-        values = {
-            keys[0]: ({"result": 1}, None),
-            keys[1]: ({"result": 2}, 60),  # With TTL
-            keys[2]: ({"result": 3}, None),
-        }
-
-        # Set values
-        self.cache.set(values)
-
-        # Get all values
-        result = self.cache.get(keys)
-        assert len(result) == 3
-        assert result[keys[0]] == {"result": 1}
-        assert result[keys[1]] == {"result": 2}
-        assert result[keys[2]] == {"result": 3}
-
-    def test_ttl_behavior(self) -> None:
-        """Test TTL (time-to-live) functionality."""
-        key: FullKey = (("graph", "node"), "ttl_key")
-        values = {key: ({"data": "expires_soon"}, 1)}  # 1 second TTL
-
-        # Set with TTL
-        self.cache.set(values)
-
-        # Should be available immediately
-        result = self.cache.get([key])
-        assert len(result) == 1
-        assert result[key] == {"data": "expires_soon"}
-
-        # Wait for expiration
-        time.sleep(1.1)
-
-        # Should be expired
-        result = self.cache.get([key])
-        assert len(result) == 0
-
-    def test_namespace_isolation(self) -> None:
-        """Test that different namespaces are isolated."""
-        key1: FullKey = (("graph1", "node"), "same_key")
-        key2: FullKey = (("graph2", "node"), "same_key")
-
-        values = {key1: ({"graph": 1}, None), key2: ({"graph": 2}, None)}
-
-        self.cache.set(values)
-
-        result = self.cache.get([key1, key2])
-        assert result[key1] == {"graph": 1}
-        assert result[key2] == {"graph": 2}
-
-    def test_clear_all(self) -> None:
-        """Test clearing all cached values."""
-        keys: list[FullKey] = [
-            (("graph", "node1"), "key1"),
-            (("graph", "node2"), "key2"),
-        ]
-        values = {keys[0]: ({"result": 1}, None), keys[1]: ({"result": 2}, None)}
-
-        self.cache.set(values)
-
-        # Verify data exists
-        result = self.cache.get(keys)
-        assert len(result) == 2
-
-        # Clear all
-        self.cache.clear()
-
-        # Verify data is gone
-        result = self.cache.get(keys)
-        assert len(result) == 0
-
-    def test_clear_by_namespace(self) -> None:
-        """Test clearing cached values by namespace."""
-        keys: list[FullKey] = [
-            (("graph1", "node"), "key1"),
-            (("graph2", "node"), "key2"),
-            (("graph1", "other"), "key3"),
-        ]
-        values = {
-            keys[0]: ({"result": 1}, None),
-            keys[1]: ({"result": 2}, None),
-            keys[2]: ({"result": 3}, None),
-        }
-
-        self.cache.set(values)
-
-        # Clear only graph1 namespace
-        self.cache.clear([("graph1", "node"), ("graph1", "other")])
-
-        # graph1 should be cleared, graph2 should remain
-        result = self.cache.get(keys)
-        assert len(result) == 1
-        assert result[keys[1]] == {"result": 2}
-
-    def test_empty_operations(self) -> None:
-        """Test behavior with empty keys/values."""
-        # Empty get
-        result = self.cache.get([])
-        assert result == {}
-
-        # Empty set
-        self.cache.set({})  # Should not raise error
-
-    def test_nonexistent_keys(self) -> None:
-        """Test getting keys that don't exist."""
-        keys: list[FullKey] = [(("graph", "node"), "nonexistent")]
-        result = self.cache.get(keys)
-        assert len(result) == 0
-
-    @pytest.mark.asyncio
-    async def test_async_operations(self) -> None:
-        """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)
-        try:
-            client.ping()
-        except Exception:
-            pytest.skip("Redis not available")
-
-        cache: RedisCache = RedisCache(client, prefix="test:async:")
-
-        keys: list[FullKey] = [(("graph", "node"), "async_key")]
-        values = {keys[0]: ({"async": True}, None)}
-
-        # Async set (delegates to sync)
-        await cache.aset(values)
-
-        # Async get (delegates to sync)
-        result = await cache.aget(keys)
-        assert len(result) == 1
-        assert result[keys[0]] == {"async": True}
-
-        # Cleanup
-        client.flushdb()
-
-    @pytest.mark.asyncio
-    async def test_async_clear(self) -> None:
-        """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)
-        try:
-            client.ping()
-        except Exception:
-            pytest.skip("Redis not available")
-
-        cache: RedisCache = RedisCache(client, prefix="test:async:")
-
-        keys: list[FullKey] = [(("graph", "node"), "key")]
-        values = {keys[0]: ({"data": "test"}, None)}
-
-        await cache.aset(values)
-
-        # Verify data exists
-        result = await cache.aget(keys)
-        assert len(result) == 1
-
-        # Clear all (delegates to sync)
-        await cache.aclear()
-
-        # Verify data is gone
-        result = await cache.aget(keys)
-        assert len(result) == 0
-
-        # Cleanup
-        client.flushdb()
-
-    def test_redis_unavailable_get(self) -> None:
-        """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:")
-
-        keys: list[FullKey] = [(("graph", "node"), "key")]
-        result = cache.get(keys)
-
-        # Should return empty dict when Redis unavailable
-        assert result == {}
-
-    def test_redis_unavailable_set(self) -> None:
-        """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:")
-
-        keys: list[FullKey] = [(("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:
-        """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:")
-
-        keys: list[FullKey] = [(("graph", "node"), "key")]
-        values = {keys[0]: ({"data": "test"}, None)}
-
-        # Should return empty dict for get (delegates to sync)
-        result = await cache.aget(keys)
-        assert result == {}
-
-        # Should not raise exception for set (delegates to sync)
-        await cache.aset(values)  # Should silently fail
-
-    def test_corrupted_data_handling(self) -> None:
-        """Test handling of corrupted data in Redis."""
-        # Set some valid data first
-        keys: list[FullKey] = [(("graph", "node"), "valid_key")]
-        values = {keys[0]: ({"data": "valid"}, None)}
-        self.cache.set(values)
-
-        # Manually insert corrupted data
-        corrupted_key = self.cache._make_key(("graph", "node"), "corrupted_key")
-        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")]
-        result = self.cache.get(all_keys)
-
-        assert len(result) == 1
-        assert result[keys[0]] == {"data": "valid"}
-
-    def test_key_parsing_edge_cases(self) -> None:
-        """Test key parsing with edge cases."""
-        # Test empty namespace
-        key1: FullKey = ((), "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",
-        )
-        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:
-        """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")
-        values = {key: (large_data, None)}
-
-        self.cache.set(values)
-        result = self.cache.get([key])
-
-        assert len(result) == 1
-        assert result[key] == large_data

libs/checkpoint/tests/test_store.py

@@ -34,42 +34,6 @@ class MockAsyncBatchedStore(AsyncBatchedBaseStore):
         return self._store.batch(ops)
 
 
-async def test_async_batch_store_resilience() -> None:
-    """Test that AsyncBatchedBaseStore recovers gracefully from task cancellation."""
-    doc = {"foo": "bar"}
-    async_store = MockAsyncBatchedStore()
-
-    await async_store.aput(("foo", "langgraph", "foo"), "bar", doc)
-
-    # Store the original task reference
-    original_task = async_store._task
-    assert original_task is not None
-    assert not original_task.done()
-
-    # Cancel the background task
-    original_task.cancel()
-    await asyncio.sleep(0.01)
-    assert original_task.cancelled()
-
-    # Perform a new operation - this should trigger _ensure_task() to create a new task
-    result = await async_store.asearch(("foo", "langgraph", "foo"))
-    assert len(result) > 0
-    assert result[0].value == doc
-
-    # Verify a new task was created
-    new_task = async_store._task
-    assert new_task is not None
-    assert new_task is not original_task
-    assert not new_task.done()
-
-    # Test that operations continue to work with the new task
-    doc2 = {"baz": "qux"}
-    await async_store.aput(("test", "namespace"), "key", doc2)
-    result2 = await async_store.aget(("test", "namespace"), "key")
-    assert result2 is not None
-    assert result2.value == doc2
-
-
 def test_get_text_at_path() -> None:
     nested_data = {
         "name": "test",
@@ -950,8 +914,8 @@ 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 +985,3 @@ 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"

libs/cli/Makefile

@@ -4,9 +4,8 @@
 # TESTING AND COVERAGE
 ######################
 
-TEST?= "tests/unit_tests"
 test:
-	uv run pytest $(TEST)
+	uv run pytest tests/unit_tests
 test-integration:
 	uv run pytest tests/integration_tests
 

libs/cli/examples/.env.example

@@ -1,3 +1,10 @@
 OPENAI_API_KEY=placeholder
 ANTHROPIC_API_KEY=placeholder
 TAVILY_API_KEY=placeholder
+LANGCHAIN_TRACING_V2=false
+LANGCHAIN_ENDPOINT=placeholder
+LANGCHAIN_API_KEY=placeholder
+LANGCHAIN_PROJECT=placeholder
+LANGGRAPH_AUTH_TYPE=noop
+LANGSMITH_AUTH_ENDPOINT=placeholder
+LANGSMITH_TENANT_ID=placeholder

libs/cli/examples/graph_prerelease_reqs/agent.py

@@ -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()

libs/cli/examples/graph_prerelease_reqs/deps/additional_deps/pyproject.toml

@@ -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"
-]

libs/cli/examples/graph_prerelease_reqs/deps/zuper_deps/pyproject.toml

@@ -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"
-]

libs/cli/examples/graph_prerelease_reqs/langgraph.json

@@ -1,13 +0,0 @@
-{
-    "python_version": "3.12",
-    "dependencies": [
-      ".",
-      "./deps/additional_deps",
-      "./deps/zuper_deps"
-    ],
-    "graphs": {
-      "agent": "./agent.py:graph"
-    },
-    "env": "../.env"
-  }
-  

libs/cli/examples/graph_prerelease_reqs/pyproject.toml

@@ -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"

libs/cli/examples/graph_prerelease_reqs_fail/agent.py

@@ -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()

libs/cli/examples/graph_prerelease_reqs_fail/langgraph.json

@@ -1,11 +0,0 @@
-{
-    "python_version": "3.12",
-    "dependencies": [
-      "."
-    ],
-    "graphs": {
-      "agent": "./agent.py:graph"
-    },
-    "env": "../.env"
-  }
-  

libs/cli/examples/graph_prerelease_reqs_fail/pyproject.toml

@@ -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",
-]

libs/cli/examples/graphs/agent.py

@@ -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)

libs/cli/examples/graphs/langgraph.json

@@ -1,5 +1,4 @@
 {
-  "$schema": "https://langgra.ph/schema.json",
   "python_version": "3.12",
   "dependencies": [
     "langchain_community",

libs/cli/examples/graphs_reqs_a/graphs_submod/agent.py

@@ -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)

libs/cli/examples/graphs_reqs_a/langgraph.json

@@ -1,5 +1,4 @@
 {
-  "$schema": "https://langgra.ph/schema.json",
   "dependencies": [
     "."
   ],

libs/cli/examples/graphs_reqs_b/graphs_submod/agent.py

@@ -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)

libs/cli/examples/graphs_reqs_b/langgraph.json

@@ -1,5 +1,4 @@
 {
-  "$schema": "https://langgra.ph/schema.json",
   "dependencies": [
     "."
   ],

libs/cli/generate_schema.py

@@ -163,7 +163,14 @@ def generate_schema():
 
     # Add enum constraint for python_version
     if "python_version" in python_schema["properties"]:
-        python_schema["properties"]["python_version"]["enum"] = ["3.11", "3.12", "3.13"]
+        python_schema["properties"]["python_version"]["enum"] = ["3.11", "3.12"]
+
+    # Add enum constraint for image_distro
+    if "image_distro" in python_schema["properties"]:
+        python_schema["properties"]["image_distro"]["anyOf"] = [
+            {"type": "string", "enum": ["debian", "wolfi"]},
+            {"type": "null"},
+        ]
 
     # Create Node.js schema with node_version
     node_schema = {

libs/cli/js-examples/langgraph.json

@@ -1,5 +1,4 @@
 {
-  "$schema": "https://langgra.ph/schema.json",
   "node_version": "20",
   "graphs": {
     "agent": "./src/agent/graph.ts:graph"

libs/cli/js-monorepo-example/.eslintrc.cjs

@@ -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 }],
-  },
-};

libs/cli/js-monorepo-example/apps/agent/langgraph.json

@@ -1,7 +0,0 @@
-{
-  "node_version": "20",
-  "graphs": {
-    "agent": "./src/graph.ts:graph"
-  },
-  "env": "../../.env"
-}

libs/cli/js-monorepo-example/apps/agent/package.json

@@ -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"
-  }
-}

libs/cli/js-monorepo-example/apps/agent/src/graph.ts

@@ -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();