target version

remove 3.9 stuff
feat(prebuilt): native structured output support w/ all sorts of models (#5961 )
2026-09-13 21:27:52 +02:00 · 2025-08-21 11:21:45 -04:00 · 2025-08-21 11:15:25 -04:00 · 2025-08-21 10:43:50 -04:00 · 2025-08-20 11:23:14 -04:00 · 2025-08-20 09:15:42 -04:00
213 changed files with 7611 additions and 15188 deletions
@@ -1,9 +1,6 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
  - name: Documentation
    url: https://github.com/langchain-ai/docs/issues/new?template=langgraph.yml
    about: Report an issue related to the LangGraph documentation
  - name: LangChain Forum
    url:  https://forum.langchain.com/
-    about: General community discussions and support
+    about: General community discussions, support, and feature requests
@@ -0,0 +1,19 @@
 name: Documentation
 description: Report an issue related to the LangGraph documentation.
 title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
 labels: [documentation]
 body:
 - type: textarea
  attributes: 
    label: "Issue with current documentation:"
    description: >
      Please make sure to leave a reference to the document/code you're
      referring to.
 - type: textarea
  attributes:
    label: "Idea or request for content:"
    description: >
      Please describe as clearly as possible what topics you think are missing
      from the current documentation.
@@ -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
@@ -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
+import langgraph_cli.config
-from langgraph_cli.constants import DEFAULT_PORT
+
 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):
+def test(
-    """Spin up API with Postgres/Redis via docker compose and wait until ready."""
+    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)
-
+        # open config
        # Validate config and prepare compose stdin/args using built image
        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`
+        set("Running...")
-        args_up = [*args, "up", "--remove-orphans", "--wait"]
+        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"]
+        _task = None
-        if capabilities.compose_type == "standalone":
+
-            compose_cmd = ["docker-compose"]
+        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(
+                subp_exec_task(
-                    *compose_cmd,
+                    "docker",
-                    *args_up,
+                    *args,
-                    input=stdin,
+                    tag,
                    verbose=verbose,
                    on_stdout=on_stdout,
                )
            )
-        except Exception as e:  # noqa: BLE001
+        except asyncio.CancelledError:
-            # On failure, show diagnostics then ensure clean teardown
+            pass
            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,
            )
        )
 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)
@@ -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 }}
+        working-directory: libs/cli/examples
        env:
          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
        run: |
-          # Build the image for this example
+          # The build-arg isn't used; just testing that we accept other args
-          langgraph build -t ${{ matrix.example.tag }}
+          langgraph build -t langgraph-test-a  --base-image "langchain/langgraph-trial"
-          # Prepare environment file from local or parent example directory
+          cp .env.example .envg
-          if [ -f .env.example ]; then cp .env.example .env; elif [ -f ../.env.example ]; then cp ../.env.example .env && cp ../.env.example ../.env; fi
+          timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -c langgraph.json -t langgraph-test-a
-          if [ -n "${{ 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
+      - name: Build and test service B
-          # Run the integration test using the built tag
+        if: steps.changed-files.outputs.all
-          # Compute repo root to reference the shared script robustly
+        working-directory: libs/cli/examples/graphs
-          REPO_ROOT=$(git rev-parse --show-toplevel)
+        run: |
-          timeout 60 python "$REPO_ROOT/.github/scripts/run_langgraph_cli_test.py" -t ${{ matrix.example.tag }}
+          langgraph build -t langgraph-test-b  --base-image "langchain/langgraph-trial"
          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-b
      - name: Build and test service C
        if: steps.changed-files.outputs.all
        working-directory: libs/cli/examples/graphs_reqs_a
        run: |
          langgraph build -t langgraph-test-c  --base-image "langchain/langgraph-trial"
          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-c
      - name: Build and test service D
        if: steps.changed-files.outputs.all
        working-directory: libs/cli/examples/graphs_reqs_b
        run: |
          langgraph build -t langgraph-test-d  --base-image "langchain/langgraph-trial"
          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-d
      - name: Build JS service
        if: steps.changed-files.outputs.all
        working-directory: libs/cli/js-examples
        run: |
          langgraph build -t langgraph-test-e
      - name: Build JS monorepo service
        if: steps.changed-files.outputs.all
        working-directory: libs/cli/js-monorepo-example
        run: |
          langgraph build -t langgraph-test-f -c apps/agent/langgraph.json --build-command "yarn run turbo build" --install-command "yarn install"
      - name: Build Python monorepo service
        if: steps.changed-files.outputs.all
        working-directory: libs/cli/python-monorepo-example
        run: |
          langgraph build -t langgraph-test-g -c apps/agent/langgraph.json
          cp apps/agent/.env.example apps/agent/.env
          if [ -n "${{ 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 || [ $? -eq 1 ]
@@ -31,7 +31,7 @@ jobs:
          - "3.12"
    name: "lint #${{ 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
@@ -17,7 +17,6 @@ jobs:
    strategy:
      matrix:
        python-version:
          - "3.9"
          - "3.10"
          - "3.11"
          - "3.12"
@@ -25,7 +24,7 @@ jobs:
    name: "test #${{ matrix.python-version }}"
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Set up Python ${{ matrix.python-version }}
        uses: astral-sh/setup-uv@v6
        with:
@@ -12,7 +12,6 @@ jobs:
    strategy:
      matrix:
        python-version:
          - "3.9"
          - "3.10"
          - "3.11"
          - "3.12"
@@ -23,7 +22,7 @@ jobs:
        working-directory: libs/langgraph
    name: "test #${{ matrix.python-version }}"
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Set up Python ${{ matrix.python-version }}
        uses: astral-sh/setup-uv@v6
        with:
@@ -24,7 +24,7 @@ jobs:
      version: ${{ steps.check-version.outputs.version }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Set up Python $${ env.PYTHON_VERSION }}
        uses: astral-sh/setup-uv@v6
@@ -75,9 +75,9 @@ jobs:
      id-token: write
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v4
        with:
          name: test-dist
          path: ${{ inputs.working-directory }}/dist/
@@ -17,7 +17,7 @@ jobs:
      run:
        working-directory: libs/langgraph
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - run: SHA=$(git rev-parse HEAD) && echo "SHA=$SHA" >> $GITHUB_ENV
      - name: Set up Python 3.11
        uses: astral-sh/setup-uv@v6
@@ -15,7 +15,7 @@ jobs:
      run:
        working-directory: libs/langgraph
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - id: files
        name: Get changed files
        uses: Ana06/get-changed-files@v2.3.0
@@ -57,7 +57,7 @@ jobs:
            echo EOF
          } >> "$GITHUB_OUTPUT"
      - name: Annotation
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
        with:
          script: |
            const file = JSON.parse(`${{ steps.files.outputs.added_modified_renamed }}`)[0]
@@ -27,7 +27,7 @@ jobs:
      python: ${{ steps.filter.outputs.python }}
      deps: ${{ steps.filter.outputs.deps }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - uses: dorny/paths-filter@v3
        id: filter
        with:
@@ -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:
@@ -21,7 +21,7 @@
        steps:
          - name: Checkout
-            uses: actions/checkout@v5
+            uses: actions/checkout@v4
          - name: Install Dependencies
            run: |
@@ -28,7 +28,7 @@ jobs:
    outputs:
      changed-files: ${{ steps.changed-files.outputs.added_modified }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Get changed files
        id: changed-files
        uses: Ana06/get-changed-files@v2.3.0
@@ -41,7 +41,7 @@ jobs:
    env:
      GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
@@ -140,7 +140,7 @@ jobs:
      - name: Upload Pages Artifact
        # if: github.ref == 'refs/heads/main'
-        uses: actions/upload-pages-artifact@v4
+        uses: actions/upload-pages-artifact@v3
        with:
          path: ./docs/site/
@@ -19,7 +19,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
        with:
          fetch-depth: 0
@@ -36,7 +36,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
        with:
          fetch-depth: 1
@@ -12,7 +12,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Validate PR Title
-        uses: amannn/action-semantic-pull-request@v6
+        uses: amannn/action-semantic-pull-request@v5
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        with:
@@ -40,7 +40,6 @@ jobs:
            sdk-py
            docs
            ci
            deps
          requireScope: false
          ignoreLabels: |
            ignore-lint-pr-title
@@ -26,7 +26,7 @@ jobs:
      tag: ${{ steps.check-version.outputs.tag }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Set up Python
        uses: astral-sh/setup-uv@v6
@@ -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
+          VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
            # handle dynamic versioning
            DIR_NAME=$(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
          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/
@@ -28,7 +28,7 @@ jobs:
          - "latest"
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Set up Python + Poetry
        uses: astral-sh/setup-uv@v6
        with:
@@ -16,13 +16,13 @@ jobs:
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
      - name: Set up uv
        uses: astral-sh/setup-uv@v6
        with:
          # use minimum supported Python version
-          python-version: "3.9"
+          python-version: "3.10"
          enable-cache: true
          cache-suffix: "uv-lock-upgrade"
@@ -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`"
+          commit-message: "chore[deps]: upgrade dependencies with `uv lock --upgrade`"
-          title: "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`.
@@ -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.
@@ -1,126 +1,24 @@
-# LangGraph Documentation
+# Setup
-For more information on contributing to our documentation, see the [Contributing Guide](../CONTRIBUTING.md).
+To setup requirements for building docs you can run:
-
+
-## Structure
+```bash
-
+uv sync --group test
-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
+## Serving documentation locally
-
+
-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:
+To run the documentation server locally you can run:
 - **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:
 ```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:
+This will start the documentation server on [http://127.0.0.1:8000/langgraph/](http://127.0.0.1:8000/langgraph/).
 - 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`
 ## 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 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 subsequently, the cells with network requests will be replayed from the cassettes
 ## Adding new notebooks
@@ -1,5 +1,3 @@
 """Generate API reference links for imports in Python code blocks within markdown files."""
 import ast
 import importlib
 import logging
@@ -72,18 +70,8 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
    ([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
    ([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
    # other prebuilts
-    (
+    (["langgraph_supervisor"], "langgraph_supervisor.supervisor", "create_supervisor", "supervisor"),
-        ["langgraph_supervisor"],
+    (["langgraph_supervisor"], "langgraph_supervisor.handoff", "create_handoff_tool", "supervisor"),
        "langgraph_supervisor.supervisor",
        "create_supervisor",
        "supervisor",
    ),
    (
        ["langgraph_supervisor"],
        "langgraph_supervisor.handoff",
        "create_handoff_tool",
        "supervisor",
    ),
    ([], "langgraph_supervisor.handoff", "create_forward_message_tool", "supervisor"),
    (["langgraph_swarm"], "langgraph_swarm.swarm", "create_swarm", "swarm"),
    (["langgraph_swarm"], "langgraph_swarm.swarm", "add_active_agent_router", "swarm"),
@@ -2108,9 +2108,9 @@ __metadata:
  linkType: hard
 "hono@npm:^4.5.4":
-  version: 4.9.7
+  version: 4.8.9
-  resolution: "hono@npm:4.9.7"
+  resolution: "hono@npm:4.8.9"
-  checksum: 10c0/089184660a9211ea216ab95bafa45260e371651cb019db49828064b7982b0ae61cc3c4715324bfeb9037aa2460c39ffa2c91d84ad0c8d500fa77cbcc7fc07a8f
+  checksum: 10c0/385539d1787fdc747bc869ef0e5ccc9f39cbe40289b94f23eecfc82c6ca440f059704647cd6381a5066d2cf7baa43ab25184c78d44af4c5c98a5c5b07670059e
  languageName: node
  linkType: hard
@@ -1,5 +1,3 @@
 """Convert Jupyter notebooks to markdown with custom processing."""
 import ast
 import os
 import re
@@ -144,9 +144,9 @@ REDIRECT_MAP = {
    "concepts/langgraph_cli.md": "https://docs.langchain.com/langgraph-platform/langgraph-cli",
    "concepts/langgraph_studio.md": "https://docs.langchain.com/langgraph-platform/langgraph-studio",
    "cloud/how-tos/studio/quick_start.md": "https://docs.langchain.com/langgraph-platform/quick-start-studio",
-    "cloud/how-tos/invoke_studio.md": "https://docs.langchain.com/langgraph-platform/use-studio#run-application",
+    "cloud/how-tos/invoke_studio.md": "https://docs.langchain.com/langgraph-platform/invoke-studio",
-    "cloud/how-tos/studio/manage_assistants.md": "https://docs.langchain.com/langgraph-platform/use-studio#manage-assistants",
+    "cloud/how-tos/studio/manage_assistants.md": "https://docs.langchain.com/langgraph-platform/manage-assistants-studio",
-    "cloud/how-tos/threads_studio.md": "https://docs.langchain.com/langgraph-platform/use-studio#manage-threads",
+    "cloud/how-tos/threads_studio.md": "https://docs.langchain.com/langgraph-platform/threads-studio",
    "cloud/how-tos/iterate_graph_studio.md": "https://docs.langchain.com/langgraph-platform/iterate-graph-studio",
    "cloud/how-tos/studio/run_evals.md": "https://docs.langchain.com/langgraph-platform/run-evals-studio",
    "cloud/how-tos/clone_traces_studio.md": "https://docs.langchain.com/langgraph-platform/clone-traces-studio",
@@ -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",
@@ -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
        ...
    ```
@@ -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. 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. 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
@@ -483,19 +483,19 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
    RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn
-    ADD ./graphs /deps/outer-graphs/src
+    ADD ./graphs /deps/__outer_graphs/src
    RUN set -ex && \
        for line in '[project]' \
                    'name = "graphs"' \
                    'version = "0.1"' \
                    '[tool.setuptools.package-data]' \
                    '"*" = ["**/*"]'; do \
-            echo "$line" >> /deps/outer-graphs/pyproject.toml; \
+            echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
        done
    RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
-    ENV LANGSERVE_GRAPHS='{"agent": "/deps/outer-graphs/src/agent.py:graph", "storm": "/deps/outer-graphs/src/storm.py:graph"}'
+    ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
    ```
    ???+ note "Updating your langgraph.json file"
@@ -1040,7 +1040,7 @@ def node_a(state: State, runtime: Runtime[ContextSchema]):
    ...
 ```
-See [this guide](../how-tos/graph-api.md#add-runtime-configuration) for a full breakdown on configuration.
+See [this guide](../how-tos/graph-api.ipynb#add-runtime-configuration) for a full breakdown on configuration.
 :::
 :::js
@@ -134,7 +134,7 @@ def update_instructions(state: State, store: BaseStore):
    namespace = ("instructions",)
    current_instructions = store.search(namespace)[0]
    # Memory logic
-    prompt = prompt_template.format(instructions=current_instructions.value["instructions"], conversation=state["messages"])
+    prompt = prompt_template.format(instructions=instructions.value["instructions"], conversation=state["messages"])
    output = llm.invoke(prompt)
    new_instructions = output['new_instructions']
    store.put(("agent_instructions",), "agent_a", {"instructions": new_instructions})
@@ -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 [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.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 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.
@@ -315,8 +315,7 @@ In our example, the output of `get_state_history` will look like this:
        tasks=(),
    ),
    StateSnapshot(
-        values={'foo': 'a', 'bar': ['a']},
+        values={'foo': 'a', 'bar': ['a']}, next=('node_b',),
        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',
@@ -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
@@ -1019,7 +1019,7 @@ console.log(await graph.invoke({}, { configurable: { myRuntimeValue: "b" } }));
    # Usage
    input_message = {"role": "user", "content": "hi"}
    # With no configuration, uses default (Anthropic)
-    response_1 = graph.invoke({"messages": [input_message]}, context=ContextSchema())["messages"][-1]
+    response_1 = graph.invoke({"messages": [input_message]})["messages"][-1]
    # Or, can set OpenAI
    response_2 = graph.invoke({"messages": [input_message]}, context={"model_provider": "openai"})["messages"][-1]
@@ -1205,7 +1205,7 @@ There are many use cases where you may wish for your node to have a custom retry
 To configure a retry policy, pass the `retry_policy` parameter to the [add_node](../reference/graphs.md#langgraph.graph.state.StateGraph.add_node). The `retry_policy` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters and associate it with a node:
 ```python
-from langgraph.types import RetryPolicy
+from langgraph.pregel import RetryPolicy
 builder.add_node(
    "node_name",
@@ -1260,7 +1260,7 @@ By default, the retry policy retries on any exception except for the following:
    from typing_extensions import TypedDict
    from langchain.chat_models import init_chat_model
    from langgraph.graph import END, MessagesState, StateGraph, START
-    from langgraph.types import RetryPolicy
+    from langgraph.pregel import RetryPolicy
    from langchain_community.utilities import SQLDatabase
    from langchain_core.messages import AIMessage
@@ -1422,15 +1422,15 @@ const builder = new StateGraph(State)
 :::
 ??? info "Why split application steps into a sequence with LangGraph?"
-    LangGraph makes it easy to add an underlying persistence layer to your application.
+LangGraph makes it easy to add an underlying persistence layer to your application.
-    This allows state to be checkpointed in between the execution of nodes, so your LangGraph nodes govern:
+This allows state to be checkpointed in between the execution of nodes, so your LangGraph nodes govern:
- How state updates are [checkpointed](../concepts/persistence.md)
+    - How state updates are [checkpointed](../concepts/persistence.md)
- How interruptions are resumed in [human-in-the-loop](../concepts/human_in_the_loop.md) workflows
+    - How interruptions are resumed in [human-in-the-loop](../concepts/human_in_the_loop.md) workflows
- How we can "rewind" and branch-off executions using LangGraph's [time travel](../concepts/time-travel.md) features
+    - How we can "rewind" and branch-off executions using LangGraph's [time travel](../concepts/time-travel.md) features
-They also determine how execution steps are [streamed](../concepts/streaming.md), and how your application is visualized
+    They also determine how execution steps are [streamed](../concepts/streaming.md), and how your application is visualized
-and debugged using [LangGraph Studio](../concepts/langgraph_studio.md).
+    and debugged using [LangGraph Studio](../concepts/langgraph_studio.md).
 Let's demonstrate an end-to-end example. We will create a sequence of three steps:
@@ -2110,6 +2110,7 @@ builder.add_edge(START, "generate_topics")
 builder.add_conditional_edges("generate_topics", continue_to_jokes, ["generate_joke"])
 builder.add_edge("generate_joke", "best_joke")
 builder.add_edge("best_joke", END)
 builder.add_edge("generate_topics", END)
 graph = builder.compile()
 ```
@@ -2332,7 +2333,7 @@ from IPython.display import Image, display
 display(Image(graph.get_graph().draw_mermaid_png()))
 ```
-![Simple loop graph](assets/graph_api_image_7.png)
+![Simple loop graph](assets/graph_api_image_3.png)
 :::
 :::js
@@ -3271,7 +3272,7 @@ from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeSt
 display(Image(app.get_graph().draw_mermaid_png()))
 ```
-![Fractal graph visualization](assets/graph_api_image_10.png)
+![Fractal graph visualization](assets/graph_api_image_5.png)
 **Using Mermaid + Pyppeteer**
@@ -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']}"
+    i.interrupt_id: f"human input for prompt {i.value}"
-    for i in graph.get_state(config).interrupts
+    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'}
@@ -1948,7 +1948,7 @@ const llmWithTools = llm.bindTools(tools);
    # Conditional edge function to route to the tool node or end based upon whether the LLM made a tool call
-    def should_continue(state: MessagesState) -> Literal["Action", END]:
+    def should_continue(state: MessagesState) -> Literal["environment", END]:
        """Decide if we should continue the loop or stop based upon whether the LLM made a tool call"""
        messages = state["messages"]
@@ -291,7 +291,7 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
    }
    .md-banner {
-      background-color: #FFAE42;
+      background-color: #CFC9FA;
      color: #000000;
    }
@@ -360,5 +360,5 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
 {% endblock %}
 {% block announce %}
-These docs will be deprecated and removed with the release of LangGraph v1.0 in October 2025. <a href="https://docs.langchain.com/oss/python/langgraph/overview" target="_blank">Visit the v1.0 alpha docs</a>
+<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 %}
@@ -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",
+    "hub>=3.0.1,<4",
-    "xxhash>=3.5.0,<4.0.0",
+    "xxhash>=3.5.0,<4",
-    "black>=25.1.0,<26.0.0",
+    "black>=25.1.0,<26",
 ]
 [dependency-groups]
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.11, <4"
 resolution-markers = [
    "python_full_version >= '3.13' and platform_python_implementation != 'PyPy'",
@@ -2337,7 +2337,7 @@ wheels = [
 [[package]]
 name = "langgraph"
-version = "0.6.7"
+version = "0.6.2"
 source = { editable = "../libs/langgraph" }
 dependencies = [
    { name = "langchain-core" },
@@ -2380,7 +2380,6 @@ dev = [
    { name = "pytest-repeat" },
    { name = "pytest-watcher" },
    { name = "pytest-xdist", extras = ["psutil"] },
    { name = "redis" },
    { name = "ruff" },
    { name = "syrupy" },
    { name = "types-requests" },
@@ -2414,7 +2413,6 @@ dev = [
    { name = "pytest-asyncio" },
    { name = "pytest-mock" },
    { name = "pytest-watcher" },
    { name = "redis" },
    { name = "ruff" },
 ]
@@ -2645,7 +2643,7 @@ test = [
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.6.4"
+version = "0.6.2"
 source = { editable = "../libs/prebuilt" }
 dependencies = [
    { name = "langchain-core" },
@@ -2676,6 +2674,7 @@ dev = [
 [[package]]
 name = "langgraph-sdk"
 version = "0.2.0"
 source = { editable = "../libs/sdk-py" }
 dependencies = [
    { name = "httpx" },
@@ -5,7 +5,7 @@
   "id": "18526f23",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/add-memory.md"
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/persistence_postgres.ipynb"
   ]
  }
 ],
@@ -707,9 +707,7 @@
    "    \"\"\"\n",
    "    Find all tool calls in the messages returned\n",
    "    \"\"\"\n",
-    "    tool_calls = [\n",
+    "    tool_calls = [tc['name'] for m in messages['messages'] for tc in getattr(m, 'tool_calls', [])]\n",
    "        tc[\"name\"] for m in messages[\"messages\"] for tc in getattr(m, \"tool_calls\", [])\n",
    "    ]\n",
    "    return tool_calls\n",
    "\n",
    "\n",
@@ -7,6 +7,11 @@ from contextlib import contextmanager
 from typing import Any
 from langchain_core.runnables import RunnableConfig
 from psycopg import Capabilities, Connection, Cursor, Pipeline
 from psycopg.rows import DictRow, dict_row
 from psycopg.types.json import Jsonb
 from psycopg_pool import ConnectionPool
 from langgraph.checkpoint.base import (
    WRITES_IDX_MAP,
    ChannelVersions,
@@ -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"]),
                },
            },
@@ -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"]),
                },
            },
@@ -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]]
@@ -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.
@@ -1,4 +1,4 @@
 from langgraph.store.postgres.aio import AsyncPostgresStore
-from langgraph.store.postgres.base import PoolConfig, PostgresStore
+from langgraph.store.postgres.base import PostgresStore
-__all__ = ["AsyncPostgresStore", "PoolConfig", "PostgresStore"]
+__all__ = ["AsyncPostgresStore", "PostgresStore"]
@@ -8,6 +8,11 @@ from types import TracebackType
 from typing import Any, Callable, cast
 import orjson
 from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
 from psycopg.rows import DictRow, dict_row
 from psycopg_pool import AsyncConnectionPool
 from langgraph.checkpoint.postgres import _ainternal
 from langgraph.store.base import (
    GetOp,
    ListNamespacesOp,
@@ -17,11 +22,6 @@ from langgraph.store.base import (
    SearchOp,
 )
 from langgraph.store.base.batch import AsyncBatchedBaseStore
 from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
 from psycopg.rows import DictRow, dict_row
 from psycopg_pool import AsyncConnectionPool
 from langgraph.checkpoint.postgres import _ainternal
 from langgraph.store.postgres.base import (
    PLACEHOLDER,
    BasePostgresStore,
@@ -22,6 +22,14 @@ from typing import (
 )
 import orjson
 from psycopg import Capabilities, Connection, Cursor, Pipeline
 from psycopg.rows import DictRow, dict_row
 from psycopg.types.json import Jsonb
 from psycopg_pool import ConnectionPool
 from typing_extensions import TypedDict
 from langgraph.checkpoint.postgres import _ainternal as _ainternal
 from langgraph.checkpoint.postgres import _internal as _pg_internal
 from langgraph.store.base import (
    BaseStore,
    GetOp,
@@ -38,14 +46,6 @@ from langgraph.store.base import (
    get_text_at_path,
    tokenize_path,
 )
 from psycopg import Capabilities, Connection, Cursor, Pipeline
 from psycopg.rows import DictRow, dict_row
 from psycopg.types.json import Jsonb
 from psycopg_pool import ConnectionPool
 from typing_extensions import TypedDict
 from langgraph.checkpoint.postgres import _ainternal as _ainternal
 from langgraph.checkpoint.postgres import _internal as _pg_internal
 if TYPE_CHECKING:
    from langchain_core.embeddings import Embeddings
@@ -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"] == {}
@@ -12,6 +12,8 @@ from typing import Any
 import pytest
 from langchain_core.embeddings import Embeddings
 from psycopg import AsyncConnection
 from langgraph.store.base import (
    GetOp,
    Item,
@@ -19,8 +21,6 @@ from langgraph.store.base import (
    PutOp,
    SearchOp,
 )
 from psycopg import AsyncConnection
 from langgraph.store.postgres import AsyncPostgresStore
 from tests.conftest import (
    DEFAULT_URI,
@@ -9,6 +9,8 @@ from uuid import uuid4
 import pytest
 from langchain_core.embeddings import Embeddings
 from psycopg import Connection
 from langgraph.store.base import (
    GetOp,
    Item,
@@ -17,8 +19,6 @@ from langgraph.store.base import (
    PutOp,
    SearchOp,
 )
 from psycopg import Connection
 from langgraph.store.postgres import PostgresStore
 from tests.conftest import (
    DEFAULT_URI,
@@ -861,41 +861,3 @@ def test_store_ttl(store):
    # Now has been (TTL_SECONDS-2)*2 > TTL_SECONDS + TTL_SECONDS/2
    res = store.search(ns, query="bar", refresh_ttl=False)
    assert len(res) == 0
@pytest.mark.parametrize(
    "vector_type,distance_type",
    [
        ("vector", "cosine"),
        ("vector", "inner_product"),
        ("halfvec", "cosine"),
        ("halfvec", "inner_product"),
    ],
 )
 def test_non_ascii(
    request: Any,
    fake_embeddings: CharacterEmbeddings,
    vector_type: str,
    distance_type: str,
 ) -> None:
    """Test support for non-ascii characters"""
    with _create_vector_store(vector_type, distance_type, fake_embeddings) as store:
        store.put(("user_123", "memories"), "1", {"text": "这是中文"})  # Chinese
        store.put(
            ("user_123", "memories"), "2", {"text": "これは日本語です"}
        )  # Japanese
        store.put(("user_123", "memories"), "3", {"text": "이건 한국어야"})  # Korean
        store.put(("user_123", "memories"), "4", {"text": "Это русский"})  # Russian
        store.put(("user_123", "memories"), "5", {"text": "यह रूसी है"})  # Hindi
        result1 = store.search(("user_123", "memories"), query="这是中文")
        result2 = store.search(("user_123", "memories"), query="これは日本語です")
        result3 = store.search(("user_123", "memories"), query="이건 한국어야")
        result4 = store.search(("user_123", "memories"), query="Это русский")
        result5 = store.search(("user_123", "memories"), query="यह रूसी है")
        assert result1[0].key == "1"
        assert result2[0].key == "2"
        assert result3[0].key == "3"
        assert result4[0].key == "4"
        assert result5[0].key == "5"
@@ -7,6 +7,10 @@ from uuid import uuid4
 import pytest
 from langchain_core.runnables import RunnableConfig
 from psycopg import Connection
 from psycopg.rows import dict_row
 from psycopg_pool import ConnectionPool
 from langgraph.checkpoint.base import (
    EXCLUDED_METADATA_KEYS,
    Checkpoint,
@@ -14,12 +18,8 @@ from langgraph.checkpoint.base import (
    create_checkpoint,
    empty_checkpoint,
 )
 from langgraph.checkpoint.serde.types import TASKS
 from psycopg import Connection
 from psycopg.rows import dict_row
 from psycopg_pool import ConnectionPool
 from langgraph.checkpoint.postgres import PostgresSaver, ShallowPostgresSaver
 from langgraph.checkpoint.serde.types import TASKS
 from tests.conftest import DEFAULT_POSTGRES_URI
@@ -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"] == {}
@@ -8,6 +8,7 @@ from contextlib import closing, contextmanager
 from typing import Any, cast
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
    WRITES_IDX_MAP,
    BaseCheckpointSaver,
@@ -20,7 +21,6 @@ from langgraph.checkpoint.base import (
    get_checkpoint_metadata,
 )
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
 from langgraph.checkpoint.sqlite.utils import search_where
 _AIO_ERROR_MSG = (
@@ -8,6 +8,7 @@ from typing import Any, Callable, TypeVar, cast
 import aiosqlite
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
    WRITES_IDX_MAP,
    BaseCheckpointSaver,
@@ -20,7 +21,6 @@ from langgraph.checkpoint.base import (
    get_checkpoint_metadata,
 )
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
 from langgraph.checkpoint.sqlite.utils import search_where
 T = TypeVar("T", bound=Callable)
@@ -5,6 +5,7 @@ from collections.abc import Sequence
 from typing import Any
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import get_checkpoint_id
@@ -11,6 +11,7 @@ from typing import Any, Callable, cast
 import aiosqlite
 import orjson
 import sqlite_vec  # type: ignore[import-untyped]
 from langgraph.store.base import (
    GetOp,
    ListNamespacesOp,
@@ -21,7 +22,6 @@ from langgraph.store.base import (
    TTLConfig,
 )
 from langgraph.store.base.batch import AsyncBatchedBaseStore
 from langgraph.store.sqlite.base import (
    _PLACEHOLDER,
    BaseSqliteStore,
@@ -507,9 +507,7 @@ class AsyncSqliteStore(AsyncBatchedBaseStore, BaseSqliteStore):
            results: List to store results in.
            cur: Database cursor.
        """
-        prepared_queries, embedding_requests = self._prepare_batch_search_queries(
+        queries, embedding_requests = self._prepare_batch_search_queries(search_ops)
            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):
+            for (idx, _), embedding in zip(embedding_requests, vectors):
-                # Find the corresponding query in prepared_queries
+                _params_list: list = queries[idx][1]
-                # The embed_req_idx is the original index in search_ops, which should map to prepared_queries
+                for i, param in enumerate(_params_list):
-                if embed_req_idx < len(prepared_queries):
+                    if param is _PLACEHOLDER:
-                    _params_list: list = prepared_queries[embed_req_idx][1]
+                        _params_list[i] = sqlite_vec.serialize_float32(embedding)
                    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 (original_op_idx, _), (query, params, needs_refresh) in zip(
+        for (idx, _), (query, params) in zip(search_ops, queries):
            search_ops, prepared_queries
        ):
            await cur.execute(query, params)
            rows = await cur.fetchall()
-            if needs_refresh and rows and self.ttl_config:
+            if "score" in query:
                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
                items = [
                    _row_to_search_item(
-                        _decode_ns_text(row[0]),  # prefix
+                        _decode_ns_text(row[0]),
                        {
-                            "key": row[1],  # key
+                            "key": row[1],
-                            "value": row[2],  # value
+                            "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
+                            "key": row[1],
-                            "value": row[2],  # value
+                            "value": row[2],
                            "created_at": row[3],
                            "updated_at": row[4],
                            "expires_at": row[5] if len(row) > 5 else None,
@@ -598,7 +559,7 @@ class AsyncSqliteStore(AsyncBatchedBaseStore, BaseSqliteStore):
                    for row in rows
                ]
-            results[original_op_idx] = items
+            results[idx] = items
    async def _batch_list_namespaces_ops(
        self,
@@ -13,6 +13,7 @@ from typing import Any, Callable, Literal, NamedTuple, cast
 import orjson
 import sqlite_vec  # type: ignore[import-untyped]
 from langgraph.store.base import (
    BaseStore,
    GetOp,
@@ -371,15 +372,13 @@ class BaseSqliteStore:
    def _prepare_batch_search_queries(
        self, search_ops: Sequence[tuple[int, SearchOp]]
    ) -> tuple[
-        list[
+        list[tuple[str, list[None | str | list[float]]]],  # queries, params
            tuple[str, list[None | str | list[float]], bool]
        ],  # queries, params, needs_refresh
        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
+            # Handle TTL refresh if requested
-            needs_ttl_refresh = bool(
+            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
+            queries.append((final_sql, final_params))
            final_sql = base_query
            final_params = params
            queries.append((final_sql, final_params, needs_ttl_refresh))
        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(
+        queries, embedding_requests = self._prepare_batch_search_queries(search_ops)
            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):
+            for (idx, _), embedding in zip(embedding_requests, embeddings):
-                if embed_req_idx < len(prepared_queries):
+                _params_list: list = queries[idx][1]
-                    _params_list: list = prepared_queries[embed_req_idx][1]
+                for i, param in enumerate(_params_list):
-                    for i, param in enumerate(_params_list):
+                    if param is _PLACEHOLDER:
-                        if param is _PLACEHOLDER:
+                        _params_list[i] = sqlite_vec.serialize_float32(embedding)
                            _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 (original_op_idx, _), (query, params, needs_refresh) in zip(
+        for (idx, _), (query, params) in zip(search_ops, queries):
            search_ops, prepared_queries
        ):
            cur.execute(query, params)
            rows = cur.fetchall()
            if needs_refresh and rows and self.ttl_config:
                keys_to_refresh = []
                for row_data in rows:
                    keys_to_refresh.append((row_data[0], row_data[1]))
                if keys_to_refresh:
                    updates_by_prefix = defaultdict(list)
                    for prefix_text, key_text in keys_to_refresh:
                        updates_by_prefix[prefix_text].append(key_text)
                    for prefix_text, key_list in updates_by_prefix.items():
                        placeholders = ",".join(["?"] * len(key_list))
                        update_query = f"""
                            UPDATE store
                            SET expires_at = DATETIME(CURRENT_TIMESTAMP, '+' || ttl_minutes || ' minutes')
                            WHERE prefix = ? AND key IN ({placeholders}) AND ttl_minutes IS NOT NULL
                        """
                        update_params = (prefix_text, *key_list)
                        try:
                            cur.execute(update_query, update_params)
                        except Exception as e:
                            logger.error(
                                f"Error during TTL refresh update for search: {e}"
                            )
            if "score" in query:  # Vector search query
                items = [
                    _row_to_search_item(
@@ -1408,7 +1385,7 @@ class SqliteStore(BaseSqliteStore, BaseStore):
                    for row in rows
                ]
-            results[original_op_idx] = items
+            results[idx] = items
    def _batch_list_namespaces_ops(
        self,
@@ -2,13 +2,13 @@ from typing import Any
 import pytest
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
    Checkpoint,
    CheckpointMetadata,
    create_checkpoint,
    empty_checkpoint,
 )
 from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
@@ -8,6 +8,7 @@ from contextlib import asynccontextmanager
 from typing import Optional, Union, cast
 import pytest
 from langgraph.store.base import (
    GetOp,
    Item,
@@ -15,7 +16,6 @@ from langgraph.store.base import (
    PutOp,
    SearchOp,
 )
 from langgraph.store.sqlite import AsyncSqliteStore
 from langgraph.store.sqlite.base import SqliteIndexConfig
 from tests.test_store import CharacterEmbeddings
@@ -2,13 +2,13 @@ from typing import Any, cast
 import pytest
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
    Checkpoint,
    CheckpointMetadata,
    create_checkpoint,
    empty_checkpoint,
 )
 from langgraph.checkpoint.sqlite import SqliteSaver
 from langgraph.checkpoint.sqlite.utils import _metadata_predicate, search_where
@@ -116,17 +116,7 @@ class TestSqliteSaver:
                search_results_5[1].config["configurable"]["checkpoint_ns"],
            } == {"", "inner"}
-            # search with before param
+            # TODO: test before and limit params
            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"
    def test_search_where(self) -> None:
        # call method / assertions
@@ -9,6 +9,7 @@ from typing import Any, Literal, Optional, Union, cast
 import pytest
 from langchain_core.embeddings import Embeddings
 from langgraph.store.base import (
    GetOp,
    Item,
@@ -17,7 +18,6 @@ from langgraph.store.base import (
    PutOp,
    SearchOp,
 )
 from langgraph.store.sqlite import SqliteStore
 from langgraph.store.sqlite.base import SqliteIndexConfig
@@ -1067,31 +1067,3 @@ def test_sql_injection_vulnerability(store: SqliteStore) -> None:
    with pytest.raises(ValueError, match="Invalid filter key"):
        store.search(("docs",), filter={malicious_key: "dummy"})
@pytest.mark.parametrize("distance_type", VECTOR_TYPES)
 def test_non_ascii(
    fake_embeddings: CharacterEmbeddings,
    distance_type: str,
 ) -> None:
    """Test support for non-ascii characters"""
    with create_vector_store(fake_embeddings, distance_type=distance_type) as store:
        store.put(("user_123", "memories"), "1", {"text": "这是中文"})  # Chinese
        store.put(
            ("user_123", "memories"), "2", {"text": "これは日本語です"}
        )  # Japanese
        store.put(("user_123", "memories"), "3", {"text": "이건 한국어야"})  # Korean
        store.put(("user_123", "memories"), "4", {"text": "Это русский"})  # Russian
        store.put(("user_123", "memories"), "5", {"text": "यह रूसी है"})  # Hindi
        result1 = store.search(("user_123", "memories"), query="这是中文")
        result2 = store.search(("user_123", "memories"), query="これは日本語です")
        result3 = store.search(("user_123", "memories"), query="이건 한국어야")
        result4 = store.search(("user_123", "memories"), query="Это русский")
        result5 = store.search(("user_123", "memories"), query="यह रूसी है")
        assert result1[0].key == "1"
        assert result2[0].key == "2"
        assert result3[0].key == "3"
        assert result4[0].key == "4"
        assert result5[0].key == "5"
@@ -7,7 +7,6 @@ import time
 from collections.abc import Generator
 import pytest
 from langgraph.store.base import TTLConfig
 from langgraph.store.sqlite import SqliteStore
 from langgraph.store.sqlite.aio import AsyncSqliteStore
@@ -94,13 +93,9 @@ def test_ttl_sweeper(temp_db_file: str) -> None:
    ttl_seconds = 2
    ttl_minutes = ttl_seconds / 60
    ttl_config: TTLConfig = {
        "default_ttl": ttl_minutes,
        "sweep_interval_minutes": ttl_minutes / 2,
    }
    with SqliteStore.from_conn_string(
        temp_db_file,
-        ttl=ttl_config,
+        ttl={"default_ttl": ttl_minutes, "sweep_interval_minutes": ttl_minutes / 2},
    ) as store:
        store.setup()
@@ -303,14 +298,9 @@ async def test_async_ttl_sweeper(temp_db_file: str) -> None:
    ttl_seconds = 2
    ttl_minutes = ttl_seconds / 60
    ttl_config: TTLConfig = {
        "default_ttl": ttl_minutes,
        "sweep_interval_minutes": ttl_minutes / 2,
    }
    async with AsyncSqliteStore.from_conn_string(
        temp_db_file,
-        ttl=ttl_config,
+        ttl={"default_ttl": ttl_minutes, "sweep_interval_minutes": ttl_minutes / 2},
    ) as store:
        await store.setup()
@@ -363,67 +353,3 @@ async def test_async_search_with_ttl(temp_db_file: str) -> None:
        # Search after expiration
        results = await store.asearch(("test",), filter={"value": "apple"})
        assert len(results) == 0
@pytest.mark.asyncio
@pytest.mark.flaky(retries=3)
 async def test_async_asearch_refresh_ttl(temp_db_file: str) -> None:
    """Test TTL refresh on asearch with async API."""
    ttl_seconds = 4.0  # Increased TTL for less sensitivity to timing
    ttl_minutes = ttl_seconds / 60.0
    async with AsyncSqliteStore.from_conn_string(
        temp_db_file, ttl={"default_ttl": ttl_minutes, "refresh_on_read": True}
    ) as store:
        await store.setup()
        namespace = ("docs", "user1")
        # t=0: items put, expire at t=4.0s
        await store.aput(namespace, "item1", {"text": "content1", "id": 1})
        await store.aput(namespace, "item2", {"text": "content2", "id": 2})
        # t=3.0s: (after sleep ttl_seconds * 0.75 = 3s)
        await asyncio.sleep(ttl_seconds * 0.75)
        # Perform asearch with refresh_ttl=True for item1.
        # item1's TTL should be refreshed. New expiry: t=3.0s + 4.0s = t=7.0s.
        # item2's TTL is not affected. Expires at t=4.0s.
        searched_items = await store.asearch(
            namespace, filter={"id": 1}, refresh_ttl=True
        )
        assert len(searched_items) == 1
        assert searched_items[0].key == "item1"
        # t=5.0s: (after sleep ttl_seconds * 0.5 = 2s more. Total elapsed: 3s + 2s = 5s)
        await asyncio.sleep(ttl_seconds * 0.5)
        # At this point:
        # - item1 (refreshed by asearch) should expire at t=7.0s. Should be ALIVE.
        # - item2 (original TTL) should have expired at t=4.0s. Should be GONE after sweep.
        await store.sweep_ttl()
        # Check item1 (should exist due to asearch refresh)
        item1_check1 = await store.aget(namespace, "item1", refresh_ttl=False)
        assert item1_check1 is not None, (
            "Item1 should exist after asearch refresh and first sweep"
        )
        assert item1_check1.value["text"] == "content1"
        # Check item2 (should be gone)
        item2_check1 = await store.aget(namespace, "item2", refresh_ttl=False)
        assert item2_check1 is None, (
            "Item2 should be gone after its original TTL expired"
        )
        # t=7.5s: (after sleep ttl_seconds * 0.625 = 2.5s more. Total elapsed: 5s + 2.5s = 7.5s)
        await asyncio.sleep(ttl_seconds * 0.625)
        # At this point:
        # - item1 (refreshed by asearch, expired at t=7.0s) should be GONE after sweep.
        await store.sweep_ttl()
        # Check item1 again (should be gone now)
        item1_final_check = await store.aget(namespace, "item1", refresh_ttl=False)
        assert item1_final_check is None, (
            "Item1 should be gone after its refreshed TTL expired"
        )
@@ -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(
+                            results.append(json.dumps(current_obj, sort_keys=True))
                                json.dumps(
                                    current_obj, sort_keys=True, ensure_ascii=False
                                )
                            )
        # Handle wildcard
        elif token == "*":
@@ -5,13 +5,12 @@ import time
 import pytest
 import redis
 from langgraph.cache.base import FullKey
 from langgraph.cache.redis import RedisCache
 class TestRedisCache:
    @pytest.fixture(autouse=True)
-    def setup(self) -> None:
+    def setup(self):
        """Set up test Redis client and cache."""
        self.client = redis.Redis(
            host="localhost", port=6379, db=0, decode_responses=False
@@ -21,21 +20,21 @@ class TestRedisCache:
        except redis.ConnectionError:
            pytest.skip("Redis server not available")
-        self.cache: RedisCache = RedisCache(self.client, prefix="test:cache:")
+        self.cache = RedisCache(self.client, prefix="test:cache:")
        # Clean up before each test
        self.client.flushdb()
-    def teardown_method(self) -> None:
+    def teardown_method(self):
        """Clean up after each test."""
        try:
            self.client.flushdb()
        except Exception:
            pass
-    def test_basic_set_and_get(self) -> None:
+    def test_basic_set_and_get(self):
        """Test basic set and get operations."""
-        keys: list[FullKey] = [(("graph", "node"), "key1")]
+        keys = [(("graph", "node"), "key1")]
        values = {keys[0]: ({"result": 42}, None)}
        # Set value
@@ -46,9 +45,9 @@ class TestRedisCache:
        assert len(result) == 1
        assert result[keys[0]] == {"result": 42}
-    def test_batch_operations(self) -> None:
+    def test_batch_operations(self):
        """Test batch set and get operations."""
-        keys: list[FullKey] = [
+        keys = [
            (("graph", "node1"), "key1"),
            (("graph", "node2"), "key2"),
            (("other", "node"), "key3"),
@@ -69,9 +68,9 @@ class TestRedisCache:
        assert result[keys[1]] == {"result": 2}
        assert result[keys[2]] == {"result": 3}
-    def test_ttl_behavior(self) -> None:
+    def test_ttl_behavior(self):
        """Test TTL (time-to-live) functionality."""
-        key: FullKey = (("graph", "node"), "ttl_key")
+        key = (("graph", "node"), "ttl_key")
        values = {key: ({"data": "expires_soon"}, 1)}  # 1 second TTL
        # Set with TTL
@@ -89,10 +88,10 @@ class TestRedisCache:
        result = self.cache.get([key])
        assert len(result) == 0
-    def test_namespace_isolation(self) -> None:
+    def test_namespace_isolation(self):
        """Test that different namespaces are isolated."""
-        key1: FullKey = (("graph1", "node"), "same_key")
+        key1 = (("graph1", "node"), "same_key")
-        key2: FullKey = (("graph2", "node"), "same_key")
+        key2 = (("graph2", "node"), "same_key")
        values = {key1: ({"graph": 1}, None), key2: ({"graph": 2}, None)}
@@ -102,12 +101,9 @@ class TestRedisCache:
        assert result[key1] == {"graph": 1}
        assert result[key2] == {"graph": 2}
-    def test_clear_all(self) -> None:
+    def test_clear_all(self):
        """Test clearing all cached values."""
-        keys: list[FullKey] = [
+        keys = [(("graph", "node1"), "key1"), (("graph", "node2"), "key2")]
            (("graph", "node1"), "key1"),
            (("graph", "node2"), "key2"),
        ]
        values = {keys[0]: ({"result": 1}, None), keys[1]: ({"result": 2}, None)}
        self.cache.set(values)
@@ -123,9 +119,9 @@ class TestRedisCache:
        result = self.cache.get(keys)
        assert len(result) == 0
-    def test_clear_by_namespace(self) -> None:
+    def test_clear_by_namespace(self):
        """Test clearing cached values by namespace."""
-        keys: list[FullKey] = [
+        keys = [
            (("graph1", "node"), "key1"),
            (("graph2", "node"), "key2"),
            (("graph1", "other"), "key3"),
@@ -146,7 +142,7 @@ class TestRedisCache:
        assert len(result) == 1
        assert result[keys[1]] == {"result": 2}
-    def test_empty_operations(self) -> None:
+    def test_empty_operations(self):
        """Test behavior with empty keys/values."""
        # Empty get
        result = self.cache.get([])
@@ -155,25 +151,27 @@ class TestRedisCache:
        # Empty set
        self.cache.set({})  # Should not raise error
-    def test_nonexistent_keys(self) -> None:
+    def test_nonexistent_keys(self):
        """Test getting keys that don't exist."""
-        keys: list[FullKey] = [(("graph", "node"), "nonexistent")]
+        keys = [(("graph", "node"), "nonexistent")]
        result = self.cache.get(keys)
        assert len(result) == 0
    @pytest.mark.asyncio
-    async def test_async_operations(self) -> None:
+    async def test_async_operations(self):
        """Test async set and get operations with sync Redis client."""
        # Create sync Redis client and cache (like main integration tests)
-        client = redis.Redis(host="localhost", port=6379, db=1, decode_responses=False)
+        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:")
+        cache = RedisCache(client, prefix="test:async:")
-        keys: list[FullKey] = [(("graph", "node"), "async_key")]
+        keys = [(("graph", "node"), "async_key")]
        values = {keys[0]: ({"async": True}, None)}
        # Async set (delegates to sync)
@@ -188,18 +186,20 @@ class TestRedisCache:
        client.flushdb()
    @pytest.mark.asyncio
-    async def test_async_clear(self) -> None:
+    async def test_async_clear(self):
        """Test async clear operations with sync Redis client."""
        # Create sync Redis client and cache (like main integration tests)
-        client = redis.Redis(host="localhost", port=6379, db=1, decode_responses=False)
+        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:")
+        cache = RedisCache(client, prefix="test:async:")
-        keys: list[FullKey] = [(("graph", "node"), "key")]
+        keys = [(("graph", "node"), "key")]
        values = {keys[0]: ({"data": "test"}, None)}
        await cache.aset(values)
@@ -218,44 +218,44 @@ class TestRedisCache:
        # Cleanup
        client.flushdb()
-    def test_redis_unavailable_get(self) -> None:
+    def test_redis_unavailable_get(self):
        """Test behavior when Redis is unavailable during get operations."""
        # Create cache with non-existent Redis server
        bad_client = redis.Redis(
            host="nonexistent", port=9999, socket_connect_timeout=0.1
        )
-        cache: RedisCache = RedisCache(bad_client, prefix="test:cache:")
+        cache = RedisCache(bad_client, prefix="test:cache:")
-        keys: list[FullKey] = [(("graph", "node"), "key")]
+        keys = [(("graph", "node"), "key")]
        result = cache.get(keys)
        # Should return empty dict when Redis unavailable
        assert result == {}
-    def test_redis_unavailable_set(self) -> None:
+    def test_redis_unavailable_set(self):
        """Test behavior when Redis is unavailable during set operations."""
        # Create cache with non-existent Redis server
        bad_client = redis.Redis(
            host="nonexistent", port=9999, socket_connect_timeout=0.1
        )
-        cache: RedisCache = RedisCache(bad_client, prefix="test:cache:")
+        cache = RedisCache(bad_client, prefix="test:cache:")
-        keys: list[FullKey] = [(("graph", "node"), "key")]
+        keys = [(("graph", "node"), "key")]
        values = {keys[0]: ({"data": "test"}, None)}
        # Should not raise exception when Redis unavailable
        cache.set(values)  # Should silently fail
    @pytest.mark.asyncio
-    async def test_redis_unavailable_async(self) -> None:
+    async def test_redis_unavailable_async(self):
        """Test async behavior when Redis is unavailable."""
        # Create sync cache with non-existent Redis server (like main integration tests)
        bad_client = redis.Redis(
            host="nonexistent", port=9999, socket_connect_timeout=0.1
        )
-        cache: RedisCache = RedisCache(bad_client, prefix="test:cache:")
+        cache = RedisCache(bad_client, prefix="test:cache:")
-        keys: list[FullKey] = [(("graph", "node"), "key")]
+        keys = [(("graph", "node"), "key")]
        values = {keys[0]: ({"data": "test"}, None)}
        # Should return empty dict for get (delegates to sync)
@@ -265,10 +265,10 @@ class TestRedisCache:
        # Should not raise exception for set (delegates to sync)
        await cache.aset(values)  # Should silently fail
-    def test_corrupted_data_handling(self) -> None:
+    def test_corrupted_data_handling(self):
        """Test handling of corrupted data in Redis."""
        # Set some valid data first
-        keys: list[FullKey] = [(("graph", "node"), "valid_key")]
+        keys = [(("graph", "node"), "valid_key")]
        values = {keys[0]: ({"data": "valid"}, None)}
        self.cache.set(values)
@@ -277,36 +277,33 @@ class TestRedisCache:
        self.client.set(corrupted_key, b"invalid:data:format:too:many:colons")
        # Should skip corrupted entry and return only valid ones
-        all_keys: list[FullKey] = [keys[0], (("graph", "node"), "corrupted_key")]
+        all_keys = [keys[0], (("graph", "node"), "corrupted_key")]
        result = self.cache.get(all_keys)
        assert len(result) == 1
        assert result[keys[0]] == {"data": "valid"}
-    def test_key_parsing_edge_cases(self) -> None:
+    def test_key_parsing_edge_cases(self):
        """Test key parsing with edge cases."""
        # Test empty namespace
-        key1: FullKey = ((), "empty_ns")
+        key1 = ((), "empty_ns")
        values = {key1: ({"data": "empty_ns"}, None)}
        self.cache.set(values)
        result = self.cache.get([key1])
        assert result[key1] == {"data": "empty_ns"}
        # Test namespace with special characters
-        key2: FullKey = (
+        key2 = (("graph:with:colons", "node-with-dashes"), "key_with_underscores")
            ("graph:with:colons", "node-with-dashes"),
            "key_with_underscores",
        )
        values = {key2: ({"data": "special_chars"}, None)}
        self.cache.set(values)
        result = self.cache.get([key2])
        assert result[key2] == {"data": "special_chars"}
-    def test_large_data_serialization(self) -> None:
+    def test_large_data_serialization(self):
        """Test handling of large data objects."""
        # Create a large data structure
        large_data = {"large_list": list(range(1000)), "nested": {"data": "x" * 1000}}
-        key: FullKey = (("graph", "node"), "large_key")
+        key = (("graph", "node"), "large_key")
        values = {key: (large_data, None)}
        self.cache.set(values)
@@ -950,8 +950,8 @@ async def test_embed_with_path(fake_embeddings: CharacterEmbeddings) -> None:
    assert results[0].key != results[1].key
    ascore = results[0].score
    bscore = results[1].score
    assert ascore == bscore
    assert ascore is not None and bscore is not None
    assert ascore == pytest.approx(bscore, abs=1e-5)
    results = await store.asearch(("test",), query="uuu")
    assert len(results) == 2
@@ -1021,27 +1021,3 @@ async def test_embed_with_path(fake_embeddings: CharacterEmbeddings) -> None:
    assert len(results) == 3
    doc5_result = next(r for r in results if r.key == "doc5")
    assert doc5_result.score is None
 def test_non_ascii(fake_embeddings: CharacterEmbeddings) -> None:
    """Test support for non-ascii characters"""
    store = InMemoryStore(
        index={"dims": fake_embeddings.dims, "embed": fake_embeddings}
    )
    store.put(("user_123", "memories"), "1", {"text": "这是中文"})  # Chinese
    store.put(("user_123", "memories"), "2", {"text": "これは日本語です"})  # Japanese
    store.put(("user_123", "memories"), "3", {"text": "이건 한국어야"})  # Korean
    store.put(("user_123", "memories"), "4", {"text": "Это русский"})  # Russian
    store.put(("user_123", "memories"), "5", {"text": "यह रूसी है"})  # Hindi
    result1 = store.search(("user_123", "memories"), query="这是中文")
    result2 = store.search(("user_123", "memories"), query="これは日本語です")
    result3 = store.search(("user_123", "memories"), query="이건 한국어야")
    result4 = store.search(("user_123", "memories"), query="Это русский")
    result5 = store.search(("user_123", "memories"), query="यह रूसी है")
    assert result1[0].key == "1"
    assert result2[0].key == "2"
    assert result3[0].key == "3"
    assert result4[0].key == "4"
    assert result5[0].key == "5"
@@ -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
@@ -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
@@ -1,89 +0,0 @@
 from collections.abc import Sequence
 from typing import Annotated, Literal, TypedDict
 from langchain_community.tools.tavily_search import TavilySearchResults
 from langchain_core.messages import BaseMessage
 from langchain_openai import ChatOpenAI
 from langgraph.graph import END, StateGraph, add_messages
 from langgraph.prebuilt import ToolNode
 tools = [TavilySearchResults(max_results=1)]
 model_oai = ChatOpenAI(temperature=0)
 model_oai = model_oai.bind_tools(tools)
 class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]
 # Define the function that determines whether to continue or not
 def should_continue(state):
    messages = state["messages"]
    last_message = messages[-1]
    # If there are no tool calls, then we finish
    if not last_message.tool_calls:
        return "end"
    # Otherwise if there is, we continue
    else:
        return "continue"
 # Define the function that calls the model
 def call_model(state, config):
    model = model_oai
    messages = state["messages"]
    response = model.invoke(messages)
    # We return a list, because this will get added to the existing list
    return {"messages": [response]}
 # Define the function to execute tools
 tool_node = ToolNode(tools)
 class ContextSchema(TypedDict):
    model: Literal["anthropic", "openai"]
 # Define a new graph
 workflow = StateGraph(AgentState, context_schema=ContextSchema)
 # Define the two nodes we will cycle between
 workflow.add_node("agent", call_model)
 workflow.add_node("action", tool_node)
 # Set the entrypoint as `agent`
 # This means that this node is the first one called
 workflow.set_entry_point("agent")
 # We now add a conditional edge
 workflow.add_conditional_edges(
    # First, we define the start node. We use `agent`.
    # This means these are the edges taken after the `agent` node is called.
    "agent",
    # Next, we pass in the function that will determine which node is called next.
    should_continue,
    # Finally we pass in a mapping.
    # The keys are strings, and the values are other nodes.
    # END is a special node marking that the graph should finish.
    # What will happen is we will call `should_continue`, and then the output of that
    # will be matched against the keys in this mapping.
    # Based on which one it matches, that node will then be called.
    {
        # If `tools`, then we call the tool node.
        "continue": "action",
        # Otherwise we finish.
        "end": END,
    },
 )
 # We now add a normal edge from `tools` to `agent`.
 # This means that after `tools` is called, `agent` node is called next.
 workflow.add_edge("action", "agent")
 # Finally, we compile it!
 # This compiles it into a LangChain Runnable,
 # meaning you can use it as you would any other runnable
 graph = workflow.compile()
@@ -1,9 +0,0 @@
 [project]
 name = "graph-prerelease-reqs-additional-deps"
 version = "0.1.0"
 description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
  "langgraph==0.6.0"
 ]
@@ -1,9 +0,0 @@
 [project]
 name = "graph-prerelease-reqs-zuper-deps"
 version = "0.1.0"
 description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
  "langchain-openai==0.3.0"
 ]
@@ -1,13 +0,0 @@
 {
    "python_version": "3.12",
    "dependencies": [
      ".",
      "./deps/additional_deps",
      "./deps/zuper_deps"
    ],
    "graphs": {
      "agent": "./agent.py:graph"
    },
    "env": "../.env"
  }
@@ -1,14 +0,0 @@
 [project]
 name = "graph-prerelease-reqs"
 version = "0.1.0"
 description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
  "langchain-openai==1.0.0a2",
  "langgraph==1.0.0a2",
  "langchain_community>=0.3.0",
 ]
 [tool.uv]
 prerelease = "allow"
@@ -1,89 +0,0 @@
 from collections.abc import Sequence
 from typing import Annotated, Literal, TypedDict
 from langchain_community.tools.tavily_search import TavilySearchResults
 from langchain_core.messages import BaseMessage
 from langchain_openai import ChatOpenAI
 from langgraph.graph import END, StateGraph, add_messages
 from langgraph.prebuilt import ToolNode
 tools = [TavilySearchResults(max_results=1)]
 model_oai = ChatOpenAI(temperature=0)
 model_oai = model_oai.bind_tools(tools)
 class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]
 # Define the function that determines whether to continue or not
 def should_continue(state):
    messages = state["messages"]
    last_message = messages[-1]
    # If there are no tool calls, then we finish
    if not last_message.tool_calls:
        return "end"
    # Otherwise if there is, we continue
    else:
        return "continue"
 # Define the function that calls the model
 def call_model(state, config):
    model = model_oai
    messages = state["messages"]
    response = model.invoke(messages)
    # We return a list, because this will get added to the existing list
    return {"messages": [response]}
 # Define the function to execute tools
 tool_node = ToolNode(tools)
 class ContextSchema(TypedDict):
    model: Literal["anthropic", "openai"]
 # Define a new graph
 workflow = StateGraph(AgentState, context_schema=ContextSchema)
 # Define the two nodes we will cycle between
 workflow.add_node("agent", call_model)
 workflow.add_node("action", tool_node)
 # Set the entrypoint as `agent`
 # This means that this node is the first one called
 workflow.set_entry_point("agent")
 # We now add a conditional edge
 workflow.add_conditional_edges(
    # First, we define the start node. We use `agent`.
    # This means these are the edges taken after the `agent` node is called.
    "agent",
    # Next, we pass in the function that will determine which node is called next.
    should_continue,
    # Finally we pass in a mapping.
    # The keys are strings, and the values are other nodes.
    # END is a special node marking that the graph should finish.
    # What will happen is we will call `should_continue`, and then the output of that
    # will be matched against the keys in this mapping.
    # Based on which one it matches, that node will then be called.
    {
        # If `tools`, then we call the tool node.
        "continue": "action",
        # Otherwise we finish.
        "end": END,
    },
 )
 # We now add a normal edge from `tools` to `agent`.
 # This means that after `tools` is called, `agent` node is called next.
 workflow.add_edge("action", "agent")
 # Finally, we compile it!
 # This compiles it into a LangChain Runnable,
 # meaning you can use it as you would any other runnable
 graph = workflow.compile()
@@ -1,11 +0,0 @@
 {
    "python_version": "3.12",
    "dependencies": [
      "."
    ],
    "graphs": {
      "agent": "./agent.py:graph"
    },
    "env": "../.env"
  }
@@ -1,11 +0,0 @@
 [project]
 name = "graph-prerelease-reqs"
 version = "0.1.0"
 description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
  "langchain-openai==1.0.0a2",
  "langgraph==1.0.0a2",
  "langchain_community>=0.3.0",
 ]
@@ -7,7 +7,6 @@ from langchain_core.messages import BaseMessage
 from langchain_openai import ChatOpenAI
 from langgraph.graph import END, StateGraph, add_messages
 from langgraph.prebuilt import ToolNode
 from langgraph.runtime import Runtime
 tools = [TavilySearchResults(max_results=1)]
@@ -18,10 +17,6 @@ model_anth = model_anth.bind_tools(tools)
 model_oai = model_oai.bind_tools(tools)
 class AgentContext(TypedDict):
    model: Literal["anthropic", "openai"]
 class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]
@@ -39,8 +34,8 @@ def should_continue(state):
 # Define the function that calls the model
-def call_model(state, runtime: Runtime[AgentContext]):
+def call_model(state, config):
-    if runtime.context.get("model", "anthropic") == "anthropic":
+    if config["configurable"].get("model", "anthropic") == "anthropic":
        model = model_anth
    else:
        model = model_oai
@@ -54,8 +49,12 @@ def call_model(state, runtime: Runtime[AgentContext]):
 tool_node = ToolNode(tools)
 class ContextSchema(TypedDict):
    model: Literal["anthropic", "openai"]
 # Define a new graph
-workflow = StateGraph(AgentState, context_schema=AgentContext)
+workflow = StateGraph(AgentState, context_schema=ContextSchema)
 # Define the two nodes we will cycle between
 workflow.add_node("agent", call_model)
@@ -1,5 +1,4 @@
 {
  "$schema": "https://langgra.ph/schema.json",
  "python_version": "3.12",
  "dependencies": [
    "langchain_community",
@@ -1,6 +1,6 @@
 from collections.abc import Sequence
 from pathlib import Path
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, TypedDict
 from langchain_anthropic import ChatAnthropic
 from langchain_community.tools.tavily_search import TavilySearchResults
@@ -8,7 +8,6 @@ from langchain_core.messages import BaseMessage
 from langchain_openai import ChatOpenAI
 from langgraph.graph import END, StateGraph, add_messages
 from langgraph.prebuilt import ToolNode
 from langgraph.runtime import Runtime
 tools = [TavilySearchResults(max_results=1)]
@@ -22,10 +21,6 @@ prompt = open(Path(__file__).parent.parent / "prompt.txt").read()
 subprompt = open(Path(__file__).parent / "subprompt.txt").read()
 class AgentContext(TypedDict):
    model: Literal["anthropic", "openai"]
 class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]
@@ -43,8 +38,8 @@ def should_continue(state):
 # Define the function that calls the model
-def call_model(state, runtime: Runtime[AgentContext]):
+def call_model(state, config):
-    if runtime.context.get("model", "anthropic") == "anthropic":
+    if config["configurable"].get("model", "anthropic") == "anthropic":
        model = model_anth
    else:
        model = model_oai
@@ -57,8 +52,9 @@ def call_model(state, runtime: Runtime[AgentContext]):
 # Define the function to execute tools
 tool_node = ToolNode(tools)
 # Define a new graph
-workflow = StateGraph(AgentState, context_schema=AgentContext)
+workflow = StateGraph(AgentState)
 # Define the two nodes we will cycle between
 workflow.add_node("agent", call_model)
@@ -1,5 +1,4 @@
 {
  "$schema": "https://langgra.ph/schema.json",
  "dependencies": [
    "."
  ],
@@ -1,6 +1,6 @@
 from collections.abc import Sequence
 from pathlib import Path
-from typing import Annotated, Literal, TypedDict
+from typing import Annotated, TypedDict
 from langchain_anthropic import ChatAnthropic
 from langchain_community.tools.tavily_search import TavilySearchResults
@@ -8,7 +8,6 @@ from langchain_core.messages import BaseMessage
 from langchain_openai import ChatOpenAI
 from langgraph.graph import END, StateGraph, add_messages
 from langgraph.prebuilt import ToolNode
 from langgraph.runtime import Runtime
 tools = [TavilySearchResults(max_results=1)]
@@ -22,10 +21,6 @@ prompt = open(Path(__file__).parent.parent / "prompt.txt").read()
 subprompt = open(Path(__file__).parent / "subprompt.txt").read()
 class AgentContext(TypedDict):
    model: Literal["anthropic", "openai"]
 class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]
@@ -43,8 +38,8 @@ def should_continue(state):
 # Define the function that calls the model
-def call_model(state, runtime: Runtime[AgentContext]):
+def call_model(state, config):
-    if runtime.context.get("model", "anthropic") == "anthropic":
+    if config["configurable"].get("model", "anthropic") == "anthropic":
        model = model_anth
    else:
        model = model_oai
@@ -59,7 +54,7 @@ tool_node = ToolNode(tools)
 # Define a new graph
-workflow = StateGraph(AgentState, context_schema=AgentContext)
+workflow = StateGraph(AgentState)
 # Define the two nodes we will cycle between
 workflow.add_node("agent", call_model)
@@ -1,5 +1,4 @@
 {
  "$schema": "https://langgra.ph/schema.json",
  "dependencies": [
    "."
  ],
@@ -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 = {
@@ -1,5 +1,4 @@
 {
  "$schema": "https://langgra.ph/schema.json",
  "node_version": "20",
  "graphs": {
    "agent": "./src/agent/graph.ts:graph"
@@ -1,62 +0,0 @@
 module.exports = {
  extends: [
    "eslint:recommended",
    "prettier",
    "plugin:@typescript-eslint/recommended",
  ],
  parserOptions: {
    ecmaVersion: 12,
    parser: "@typescript-eslint/parser",
    project: "./tsconfig.json",
    sourceType: "module",
  },
  plugins: ["import", "@typescript-eslint", "no-instanceof"],
  ignorePatterns: [
    ".eslintrc.cjs",
    "scripts",
    "src/utils/lodash/*",
    "node_modules",
    "dist",
    "dist-cjs",
    "*.js",
    "*.cjs",
    "*.d.ts",
  ],
  rules: {
    "no-process-env": 2,
    "no-instanceof/no-instanceof": 2,
    "@typescript-eslint/explicit-module-boundary-types": 0,
    "@typescript-eslint/no-empty-function": 0,
    "@typescript-eslint/no-shadow": 0,
    "@typescript-eslint/no-empty-interface": 0,
    "@typescript-eslint/no-use-before-define": ["error", "nofunc"],
    "@typescript-eslint/no-unused-vars": ["warn", { args: "none" }],
    "@typescript-eslint/no-floating-promises": "error",
    "@typescript-eslint/no-misused-promises": "error",
    camelcase: 0,
    "class-methods-use-this": 0,
    "import/extensions": [2, "ignorePackages"],
    "import/no-extraneous-dependencies": [
      "error",
      { devDependencies: ["**/*.test.ts"] },
    ],
    "import/no-unresolved": 0,
    "import/prefer-default-export": 0,
    "keyword-spacing": "error",
    "max-classes-per-file": 0,
    "max-len": 0,
    "no-await-in-loop": 0,
    "no-bitwise": 0,
    "no-console": 0,
    "no-restricted-syntax": 0,
    "no-shadow": 0,
    "no-continue": 0,
    "no-underscore-dangle": 0,
    "no-use-before-define": 0,
    "no-useless-constructor": 0,
    "no-return-await": 0,
    "consistent-return": 0,
    "no-else-return": 0,
    "new-cap": ["error", { properties: false, capIsNew: false }],
  },
 };
@@ -1,7 +0,0 @@
 {
  "node_version": "20",
  "graphs": {
    "agent": "./src/graph.ts:graph"
  },
  "env": "../../.env"
 }
@@ -1,18 +0,0 @@
 {
  "name": "@js-monorepo-example/agent",
  "version": "0.0.1",
  "type": "module",
  "main": "src/graph.ts",
  "scripts": {
    "build": "tsc",
    "clean": "rm -rf dist"
  },
  "dependencies": {
    "@js-monorepo-example/shared": "*",
    "@langchain/core": "^0.3.2",
    "@langchain/langgraph": "^0.2.5"
  },
  "devDependencies": {
    "typescript": "^5.3.3"
  }
 }
@@ -1,47 +0,0 @@
 /**
 * Simple LangGraph.js example for monorepo testing
 */
 import { StateGraph } from "@langchain/langgraph";
 import { RunnableConfig } from "@langchain/core/runnables";
 import { StateAnnotation } from "./state.js";
 import { getGreeting } from "@js-monorepo-example/shared";
 /**
 * Simple node that uses the shared library
 */
 const callModel = async (
  state: typeof StateAnnotation.State,
  _config: RunnableConfig,
 ): Promise<typeof StateAnnotation.Update> => {
  // Use functions from the shared library
  const greeting = getGreeting();
  return {
    messages: [
      {
        role: "assistant",
        content: `${greeting}`,
      },
    ],
  };
 };
 /**
 * Simple routing function
 */
 export const route = (
  state: typeof StateAnnotation.State,
 ): "__end__" | "callModel" => {
  if (state.messages.length > 0) {
    return "__end__";
  }
  return "callModel";
 };
 // Create the graph
 const builder = new StateGraph(StateAnnotation)
  .addNode("callModel", callModel)
  .addEdge("__start__", "callModel")
  .addConditionalEdges("callModel", route);
 export const graph = builder.compile();
@@ -1,15 +0,0 @@
 import { BaseMessage, BaseMessageLike } from "@langchain/core/messages";
 import { Annotation, messagesStateReducer } from "@langchain/langgraph";
 /**
 * Simple state annotation for the agent
 */
 export const StateAnnotation = Annotation.Root({
  /**
   * Messages track the primary execution state of the agent.
   */
  messages: Annotation<BaseMessage[], BaseMessageLike[]>({
    reducer: messagesStateReducer,
    default: () => [],
  }),
 });
@@ -1,9 +0,0 @@
 {
  "extends": "../../tsconfig.json",
  "compilerOptions": {
    "outDir": "./dist",
    "rootDir": "./src"
  },
  "include": ["src/**/*"],
  "exclude": ["node_modules", "dist"]
 }
@@ -1,14 +0,0 @@
 {
  "name": "@js-monorepo-example/shared",
  "version": "0.0.1",
  "type": "module",
  "main": "dist/index.js",
  "types": "dist/index.d.ts",
  "scripts": {
    "build": "tsc",
    "clean": "rm -rf dist"
  },
  "devDependencies": {
    "typescript": "^5.3.3"
  }
 }
@@ -1,6 +0,0 @@
 /**
 * Simple utility functions for monorepo testing
 */
 export function getGreeting(): string {
  return "Hello from shared library!";
 }
@@ -1,9 +0,0 @@
 {
  "extends": "../../tsconfig.json",
  "compilerOptions": {
    "outDir": "./dist",
    "rootDir": "./src"
  },
  "include": ["src/**/*"],
  "exclude": ["node_modules", "dist"]
 }
@@ -1,34 +0,0 @@
 {
  "name": "js-monorepo-example",
  "version": "0.0.1",
  "packageManager": "yarn@1.22.22",
  "description": "A simple monorepo example for LangGraph integration testing.",
  "private": true,
  "workspaces": [
    "libs/*",
    "apps/*"
  ],
  "type": "module",
  "scripts": {
    "build": "turbo build",
    "clean": "turbo clean",
    "test": "turbo test",
    "format": "prettier --write .",
    "lint": "eslint 'apps/**/*.ts' 'libs/**/*.ts'"
  },
  "devDependencies": {
    "turbo": "^2.5.0",
    "typescript": "^5.3.3",
    "@tsconfig/recommended": "^1.0.7",
    "@eslint/eslintrc": "^3.1.0",
    "@eslint/js": "^9.9.1",
    "eslint": "^8.41.0",
    "eslint-config-prettier": "^8.8.0",
    "eslint-plugin-import": "^2.27.5",
    "eslint-plugin-no-instanceof": "^1.0.1",
    "eslint-plugin-prettier": "^4.2.1",
    "@typescript-eslint/eslint-plugin": "^5.59.8",
    "@typescript-eslint/parser": "^5.59.8",
    "prettier": "^3.3.3"
  }
 }
@@ -1,16 +0,0 @@
 {
  "extends": "@tsconfig/recommended",
  "compilerOptions": {
    "target": "ES2022",
    "module": "ESNext",
    "moduleResolution": "node",
    "allowSyntheticDefaultImports": true,
    "esModuleInterop": true,
    "skipLibCheck": true,
    "strict": true,
    "declaration": true,
    "outDir": "./dist"
  },
  "include": ["apps/**/*", "libs/**/*"],
  "exclude": ["node_modules", "dist"]
 }
@@ -1,15 +0,0 @@
 {
  "$schema": "https://turbo.build/schema.json",
  "tasks": {
    "build": {
      "dependsOn": ["^build"],
      "outputs": ["dist/**"]
    },
    "clean": {
      "dependsOn": ["^clean"]
    },
    "test": {
      "dependsOn": ["^test"]
    }
  }
 }
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Sydney Runkle	6f2453024a	target version	2025-08-21 11:21:45 -04:00
Sydney Runkle	b22227588c	remove 3.9 stuff	2025-08-21 11:15:25 -04:00
Caspar Broekhuizen GitHub Sydney Runkle Sydney Runkle	f4cdeea6ad	feat(prebuilt): native structured output support w/ all sorts of models (#5961 ) * Adds support for `NativeOutput` via a new `NativeOutput` dataclass * Adds support for structured output specification via the following (pydantic models already supported) * dataclasses * typed dicts * json schemas * Adds mocking support to support native strategies with `FakeToolCallingModel` * Add new default tool message when `tool_message_content` not provided * Smart "selection" of native vs tool output based on provider support, necessitates profiles down the line Considered questions * do we want to enforce docstrings? -- decided on no for now * do we want to enforce names (titles) on json schemas? -- decided no for now, defaulting to `structured_output` * do we want to validate that json schemas coming in are valid? -- decided no for now * do we want to validate model results against a given json schema? we validate against all other types (typed dict, dataclass, etc) w/ pydantic -- decided no for now TODO in future PRs: * Figure out retry policy * Add standard testing (handed off to @casparb) * Further privatize certain structures (like the bindings) -- this is low prio --------- Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com> Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>	2025-08-21 10:43:50 -04:00
Sydney RunkleandGitHub	1cd1373788	chore(prebuilt): clean up public state (#5973 ) precursor to `prepare_call` PR, cleaning up existing logic w/ pre model hook * use one combined `AgentState` instead of the one w/ and w/o structured response * remove exposed pydantic agent state + loosen bounds on state type * remove llm input messages pattern, should be made possible with prepare_call also * some remaining test fixes in `langgraph` to adapt to new `model` node name (used to be `agent`)	2025-08-20 11:23:14 -04:00
Sydney RunkleandGitHub	f994d16b49	chore(prebuilt): critical renaming (#5971 ) * `create_react_agent` -> `create_agent` * `agent` node -> `model` node	2025-08-20 09:15:42 -04:00
Sydney RunkleandGitHub	20953b4728	chore(prebuilt): remove config schema deprecation for new version (#5970 ) don't need this deprecation warning as we're migrating to langchain	2025-08-20 09:04:11 -04:00
Sydney RunkleandGitHub	e670815780	chore(prebuilt): rework structured outputs -- type safety, etc (#5962 ) * make `_SchemaSpec` private * Add ability to customize message used in artificial tool response	2025-08-20 08:52:51 -04:00
Sydney RunkleandGitHub	4151861ca2	chore(prebuilt): remove v1 (#5960 )	2025-08-19 14:30:32 -04:00
Sydney RunkleandGitHub	5239184ba6	chore(prebuilt): revert optional multiple nodes for tools (#5959 )	2025-08-19 14:19:57 -04:00
Sydney RunkleandGitHub	a5aa9ce27d	chore(prebuilt): remove support for models that used `bind_X` (#5958 ) Remove support for models w/ `.bind` used to streamline public API + recommendations Also cleaning up `typing.py` file as requested :)	2025-08-19 13:56:16 -04:00
Eugene YurtsevandGitHub	b58a7fb2fe	feat(prebuilt): support ToolOutput response_format (#5915 ) * Add support for ToolOutput response format. * I don't love the name -- it's confusing unless you know that it's parameterizing a strategy. We should determine if we want to support our old strategy for doing things -- it has a higher latency (one extra LLM call), but it's a reasonable built-in strategy as it doesn't do anything awkward with conversation history. (Wouldn't surprising if it has overall better performance than tool choice for longer conversations)	2025-08-14 23:34:46 -04:00
Eugene Yurtsev	ebae60045f	Fix spelling typo	2025-08-14 14:51:13 -04:00
Eugene YurtsevandGitHub	42f9683d73	chore(prebuilt): breaking do not support prebound tools on model (#5912 ) Do not support prebound tools on the model. There's reason users should be prebinding tools to the model! This is a breaking change that might affect some users, but the work-around is simple -- provide tools into the create_react_agent api.	2025-08-14 14:46:11 -04:00
Eugene YurtsevandGitHub	69249e724d	chore(prebuilt): Separate prompt from model (#5909 ) Quick clean up to simplify the logic by which messages into the model are prepared.	2025-08-14 12:59:09 -04:00
Eugene YurtsevandGitHub	e6d71a586d	chore(prebuilt): remove structured tool support from ToolNode (#5902 ) Remove structured tool support from ToolNode We'll handle structured tools directly in the call_model nodes.	2025-08-13 22:32:21 -04:00
Eugene YurtsevandGitHub	50601dc02c	feat(prebuilt): Add structured output tools to ToolNode (#5899 ) * Add structured output tools to ToolNode * Fix default tool node name to match the actual default ('tools') * Update doc-strings to explain what inputs/outputs are for the ToolNode. * Mark internal attributes as private (potentially breaking -- although hopefully users aren't accessing these) ## Decisions points * OK with two properties? Done since users may be relying on `tools_by_name` and expanding the return type will break user code. ## Changes in public/private interface ### Marked as public * Make `tools_by_name` an official public property * Make `structured_output_tools` a public property ### Marked as private There should be no reason why users are accessing these attributes ```python _tool_to_state_args _tool_to_store_arg _handle_tool_errors _messages_key ``` ### Usage ```python class OutputSchema(BaseModel): name: str age: int location: str tool_node = ToolNode([OutputSchema]) # Test that the structured output tool is registered correctly assert "OutputSchema" in tool_node.structured_output_tools # Create a tool call that matches the schema tool_call = { "name": "OutputSchema", "args": {"name": "Alice", "age": 30, "location": "NYC"}, "id": "call_123", "type": "tool_call", } # Test sync execution result = tool_node.invoke( {"messages": [AIMessage(content="", tool_calls=[tool_call])]} ) # Should return a Command with structured response assert isinstance(result, list) assert len(result) == 1 command = result[0] assert isinstance(command, Command) # Check the update structure assert "messages" in command.update assert "structured_response" in command.update # Check the tool message tool_message = command.update["messages"][0] assert isinstance(tool_message, ToolMessage) assert tool_message.name == "OutputSchema" assert tool_message.tool_call_id == "call_123" # Check the structured response structured_response = command.update["structured_response"] assert isinstance(structured_response, OutputSchema) assert structured_response.name == "Alice" assert structured_response.age == 30 assert structured_response.location == "NYC" ```	2025-08-13 15:16:53 -04:00
Eugene YurtsevandGitHub	9e174e7e8b	chore(prebuilt): move unit tests for ToolNode into the tool node testing code (#5893 ) Move unit tests for ToolNode into the tool node testing code	2025-08-13 11:15:10 -04:00
Eugene YurtsevandGitHub	9e9a5d2498	feat(prebuilt): Split tool node to individual tool nodes (#5888 ) Add option to split tool node to individual nodes. Summary: * User code (specifically streaming) may break if it's relying on the name of the `tools` node * The boolean flag in the interface is likely temporary (especially if there are no major breaking changes) * We'll need to decide if we can get rid of the version in create react agent. "v1" is not consistent conceptually with a node per tool.	2025-08-13 09:49:27 -04:00
Eugene Yurtsev	7e257dadd6	x	2025-08-12 21:52:21 -04:00
Eugene Yurtsev	2fed0e4852	Internal refactor of create react-agent	2025-08-12 21:50:19 -04:00