x

**Description:** 
This PR adds the Python SDK types necessary for langgraph platform users
to inject their own custom encryption-at-rest functions. See [docs
PR](https://github.com/langchain-ai/docs/pull/1715) for more details.

note: this PR adds a starlette dev dependency so that custom encryption
can access BaseUser information.

**Issue:**
required for LSD-172 

**Dependencies:**
- [depended upon by associated langgraph-api
changes](https://github.com/langchain-ai/langgraph-api/pull/1773)(this
PR must merge before that one)
- [docs PR](https://github.com/langchain-ai/docs/pull/1715)

**TODO:**
- [x] move docs to docs repo
- [x] bump package versions before merge

---------

Signed-off-by: Connor Braa <cwlbraa@langchain.dev>
Co-authored-by: Claude <noreply@anthropic.com>

.github/scripts/check_sdk_methods.py

@@ -38,7 +38,7 @@ def main():
         tree = ast.parse(file.read())
 
     classes = find_classes(tree)
-  
+
     def is_sync(class_spec: Tuple[str, List[str]]) -> bool:
         return class_spec[0].startswith("Sync")
 

.github/scripts/run_langgraph_cli_test.py

@@ -1,7 +1,8 @@
+import logging
 import pathlib
 import sys
 import time
-from urllib import request, error
+from urllib import error, request
 
 import langgraph_cli
 import langgraph_cli.config
@@ -11,9 +12,13 @@ from langgraph_cli.constants import DEFAULT_PORT
 from langgraph_cli.exec import Runner, subp_exec
 from langgraph_cli.progress import Progress
 
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+
 
 def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
     """Spin up API with Postgres/Redis via docker compose and wait until ready."""
+    logger.info("Starting test...")
     with Runner() as runner, Progress(message="Pulling...") as set:
         # Detect docker/compose capabilities
         capabilities = langgraph_cli.docker.check_capabilities(runner)
@@ -57,7 +62,9 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
             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))
+                runner.run(
+                    subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=False)
+                )
             except Exception:
                 pass
             try:
@@ -93,7 +100,7 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
         set("")
         base_url = f"http://localhost:{port}"
         ok_url = f"{base_url}/ok"
-        print(f"Waiting for {ok_url} to respond with 200...")
+        logger.info(f"Waiting for {ok_url} to respond with 200...")
         deadline = time.time() + 30
         last_err: Exception | None = None
         while time.time() < deadline:
@@ -107,13 +114,16 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
                         break
                     else:
                         last_err = RuntimeError(f"Unexpected status: {resp.status}")
-                        print(f"Unexpected status: {resp.status}")
+                        logger.error(f"Unexpected status: {resp.status}")
             except error.URLError as e:
+                logger.error(f"URLError: {e}")
                 last_err = e
             except Exception as e:  # noqa: BLE001
+                logger.error(f"Exception: {e}")
                 last_err = e
             time.sleep(0.5)
         else:
+            logger.error("Timeout waiting for /ok to return 200")
             # Bring stack down before raising
             args_down = [*args, "down", "-v", "--remove-orphans"]
             try:
@@ -131,15 +141,23 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
                 )
 
         # 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,
+        logger.info("Test succeeded. Bringing down compose stack...")
+        try:
+            args_down = [*args, "down", "-v", "--remove-orphans"]
+            runner.run(
+                subp_exec(
+                    *compose_cmd,
+                    *args_down,
+                    input=stdin,
+                    verbose=verbose,
+                )
             )
-        )
+            logger.info("Compose stack down. Finishing...")
+        except Exception:
+            logger.exception("Failed to bring down compose stack")
+            pass
+
+    logger.info("Test finished")
 
 
 if __name__ == "__main__":
@@ -150,4 +168,10 @@ if __name__ == "__main__":
     parser.add_argument("-c", "--config", type=str, default="./langgraph.json")
     parser.add_argument("-p", "--port", type=int, default=DEFAULT_PORT)
     args = parser.parse_args()
-    test(pathlib.Path(args.config), args.port, args.tag, verbose=True)
+    try:
+        test(pathlib.Path(args.config), args.port, args.tag, verbose=True)
+    except BaseException:
+        logger.exception("Test failed")
+        raise
+    
+    logger.info("Test execution finished")

.github/workflows/_integration_test.yml

@@ -13,7 +13,6 @@ jobs:
       matrix:
         python-version:
           - "3.10"
-          - "3.11"
           - "3.14"
         example:
           - name: A
@@ -33,7 +32,7 @@ jobs:
       run:
         working-directory: libs/cli
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Get changed files
         id: changed-files
         uses: Ana06/get-changed-files@v2.3.0
@@ -67,19 +66,19 @@ jobs:
           timeout 60 python "$REPO_ROOT/.github/scripts/run_langgraph_cli_test.py" -t ${{ matrix.example.tag }}
 
       - name: Build JS service
-        if: steps.changed-files.outputs.all
+        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
         working-directory: libs/cli/js-examples
         run: |
           langgraph build -t langgraph-test-e
 
       - name: Build JS monorepo service
-        if: steps.changed-files.outputs.all
+        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
         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
+        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
         working-directory: libs/cli/python-monorepo-example
         run: |
           langgraph build -t langgraph-test-g -c apps/agent/langgraph.json
@@ -88,24 +87,32 @@ jobs:
           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
+        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
         working-directory: libs/cli/examples/graph_prerelease_reqs
         run: |
           langgraph build -t langgraph-test-h
           cp ../.env.example .env
           if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
           timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-h
+          echo "Finished starting up langgraph-test-h"
           LANGGRAPH_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langgraph'); print(v);")
-          if [ "$LANGGRAPH_VERSION" != "1.0.0a2" ]; then
+          if [ "$LANGGRAPH_VERSION" != "1.0.2" ]; then
+            echo "LANGGRAPH_VERSION != 1.0.2; $LANGGRAPH_VERSION"
             exit 1
           fi
           LANGCHAIN_OPENAI_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-openai'); print(v);")
-          if [ "$LANGCHAIN_OPENAI_VERSION" != "0.3.0" ]; then
+          if [ "$LANGCHAIN_OPENAI_VERSION" != "1.0.1" ]; then
+            echo "LANGCHAIN_OPENAI_VERSION != 1.0.1; $LANGCHAIN_OPENAI_VERSION"
+            exit 1
+          fi
+          LANGCHAIN_ANTHROPIC_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-anthropic'); print(v);")
+          if [ "$LANGCHAIN_ANTHROPIC_VERSION" != "1.0.0a5" ]; then
+            echo "LANGCHAIN_ANTHROPIC_VERSION != 1.0.0a5; $LANGCHAIN_ANTHROPIC_VERSION"
             exit 1
           fi
 
       - name: Build and test prerelease reqs fail service
-        if: steps.changed-files.outputs.all
+        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
         working-directory: libs/cli/examples/graph_prerelease_reqs_fail
         run: |
           langgraph build -t langgraph-test-i || [ $? -eq 1 ]

.github/workflows/_lint.yml

@@ -31,7 +31,7 @@ jobs:
           - "3.12"
     name: "lint #${{ matrix.python-version }}"
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Get changed files
         id: changed-files
         uses: Ana06/get-changed-files@v2.3.0

.github/workflows/_test.yml

@@ -25,7 +25,7 @@ jobs:
 
     name: "test #${{ matrix.python-version }}"
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Set up Python ${{ matrix.python-version }}
         uses: astral-sh/setup-uv@v7
         with:

.github/workflows/_test_langgraph.yml

@@ -23,7 +23,7 @@ jobs:
         working-directory: libs/langgraph
     name: "test #${{ matrix.python-version }}"
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Set up Python ${{ matrix.python-version }}
         uses: astral-sh/setup-uv@v7
         with:

.github/workflows/_test_release.yml

@@ -23,7 +23,7 @@ jobs:
       version: ${{ steps.check-version.outputs.version }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: Set up Python $${ env.PYTHON_VERSION }}
         uses: astral-sh/setup-uv@v7
@@ -74,7 +74,7 @@ jobs:
       id-token: write
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - uses: actions/download-artifact@v6
         with:

.github/workflows/baseline.yml

@@ -17,7 +17,7 @@ jobs:
       run:
         working-directory: libs/langgraph
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - run: SHA=$(git rev-parse HEAD) && echo "SHA=$SHA" >> $GITHUB_ENV
       - name: Set up Python 3.11
         uses: astral-sh/setup-uv@v7

.github/workflows/bench.yml

@@ -15,7 +15,7 @@ jobs:
       run:
         working-directory: libs/langgraph
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - id: files
         name: Get changed files
         uses: Ana06/get-changed-files@v2.3.0

.github/workflows/ci.yml

@@ -27,7 +27,7 @@ jobs:
       python: ${{ steps.filter.outputs.python }}
       deps: ${{ steps.filter.outputs.deps }}
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - uses: dorny/paths-filter@v3
         id: filter
         with:
@@ -100,7 +100,7 @@ jobs:
     name: "Check SDK methods matching"
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Set up Python
         uses: actions/setup-python@v6
         with:
@@ -118,7 +118,7 @@ jobs:
         python-version:
           - "3.11"
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Set up Python ${{ matrix.python-version }}
         uses: astral-sh/setup-uv@v7
         with:

.github/workflows/codespell.yml

@@ -21,7 +21,7 @@
     
         steps:
           - name: Checkout
-            uses: actions/checkout@v5
+            uses: actions/checkout@v6
     
           - name: Install Dependencies
             run: |

.github/workflows/deploy_docs.yml

@@ -1,72 +0,0 @@
-name: Deploy Docs Redirects
-
-on:
-  push:
-    branches:
-      - main
-  workflow_dispatch:
-
-permissions:
-  contents: read
-  pages: write
-  id-token: write
-
-concurrency:
-  group: "pages"
-  cancel-in-progress: false
-
-defaults:
-  run:
-    working-directory: docs
-
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    timeout-minutes: 10 # Job will be cancelled if it runs for more than 10 minutes
-    env:
-      GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
-    steps:
-      - uses: actions/checkout@v5
-        with:
-          fetch-depth: 0
-
-      - name: Set up Python
-        uses: astral-sh/setup-uv@v7
-        with:
-          python-version: "3.12"
-          enable-cache: true
-          cache-suffix: "docs"
-
-      - name: Install dependencies
-        run: |
-          yarn
-          uv sync --all-groups
-          # we run this installation only for internal PRs
-          # as GITHUB_TOKEN is not available for PRs from outside contributors
-          if [ -n "${GITHUB_TOKEN}" ]; then
-            uv run pip install "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
-          fi
-
-      - name: Build llms-text
-        run: make llms-text
-
-      - name: Build site (redirects only)
-        run: make build-docs
-        env:
-          OPENAI_API_KEY: sf-proj-1234567890 # fake placeholder, shouldn't actually be used
-          ANTHROPIC_API_KEY: sk-ant-api03-1234567890 # fake placeholder, shouldn't actually be used
-
-      - name: Configure GitHub Pages
-        if: github.ref == 'refs/heads/main'
-        uses: actions/configure-pages@v5
-
-      - name: Upload Pages Artifact
-        if: github.ref == 'refs/heads/main'
-        uses: actions/upload-pages-artifact@v4
-        with:
-          path: ./docs/site/
-
-      - name: Deploy to GitHub Pages
-        if: github.ref == 'refs/heads/main'
-        id: deployment
-        uses: actions/deploy-pages@v4

.github/workflows/link_check.yml

@@ -19,7 +19,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
 
@@ -36,7 +36,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           fetch-depth: 1
 

.github/workflows/release.yml

@@ -25,7 +25,7 @@ jobs:
       tag: ${{ steps.check-version.outputs.tag }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: Set up Python
         uses: astral-sh/setup-uv@v7
@@ -86,7 +86,7 @@ jobs:
     outputs:
       release-body: ${{ steps.generate-release-body.outputs.release-body }}
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           repository: langchain-ai/langgraph
           path: langgraph
@@ -157,7 +157,7 @@ jobs:
       - test-pypi-publish
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       # We explicitly *don't* set up caching here. This ensures our tests are
       # maximally sensitive to catching breakage.
@@ -260,7 +260,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: Set up Python
         uses: astral-sh/setup-uv@v7
@@ -301,7 +301,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: Set up Python
         uses: astral-sh/setup-uv@v7
@@ -322,5 +322,6 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
           generateReleaseNotes: false
           tag: ${{needs.build.outputs.tag}}
+          name: ${{ needs.build.outputs.pkg-name }}==${{ needs.build.outputs.version }}
           body: ${{ needs.release-notes.outputs.release-body }}
           commit: ${{ github.sha }}

.github/workflows/run_notebooks.yml

@@ -28,7 +28,7 @@ jobs:
           - "latest"
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Set up Python + Poetry
         uses: astral-sh/setup-uv@v7
         with:

.github/workflows/uv_lock_ugprade.yml

@@ -16,7 +16,7 @@ jobs:
     runs-on: ubuntu-latest
     
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       
       - name: Set up uv
         uses: astral-sh/setup-uv@v7

README.md

@@ -67,7 +67,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 - [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
 
 > [!NOTE]
-> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
+> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).
 
 ## Additional resources
 

docs/_scripts/js_translation/codeblocks/yarn.lock

@@ -2108,9 +2108,9 @@ __metadata:
   linkType: hard
 
 "hono@npm:^4.5.4":
-  version: 4.9.7
-  resolution: "hono@npm:4.9.7"
-  checksum: 10c0/089184660a9211ea216ab95bafa45260e371651cb019db49828064b7982b0ae61cc3c4715324bfeb9037aa2460c39ffa2c91d84ad0c8d500fa77cbcc7fc07a8f
+  version: 4.10.3
+  resolution: "hono@npm:4.10.3"
+  checksum: 10c0/bdcc4c7066c74ba7cfa63ed6550768a0f43a420286c8f8f74b7012ea4901b8b06778fa8e98264b46f1a86920f056b7ede1f07814da4934912f9945def4977c29
   languageName: node
   linkType: hard
 
@@ -2340,13 +2340,13 @@ __metadata:
   linkType: hard
 
 "js-yaml@npm:^4.1.0":
-  version: 4.1.0
-  resolution: "js-yaml@npm:4.1.0"
+  version: 4.1.1
+  resolution: "js-yaml@npm:4.1.1"
   dependencies:
     argparse: "npm:^2.0.1"
   bin:
     js-yaml: bin/js-yaml.js
-  checksum: 10c0/184a24b4eaacfce40ad9074c64fd42ac83cf74d8c8cd137718d456ced75051229e5061b8633c3366b8aada17945a7a356b337828c19da92b51ae62126575018f
+  checksum: 10c0/561c7d7088c40a9bb53cc75becbfb1df6ae49b34b5e6e5a81744b14ae8667ec564ad2527709d1a6e7d5e5fa6d483aa0f373a50ad98d42fde368ec4a190d4fae7
   languageName: node
   linkType: hard
 

docs/_scripts/notebook_hooks.py

@@ -81,7 +81,7 @@ REDIRECT_MAP = {
     # cloud redirects
     "cloud/index.md": "https://docs.langchain.com/oss/python/langgraph/overview",
     "cloud/how-tos/index.md": "https://docs.langchain.com/langsmith/home",
-    "cloud/concepts/api.md": "https://docs.langchain.com/langsmith/langgraph-server",
+    "cloud/concepts/api.md": "https://docs.langchain.com/langsmith/agent-server",
     "cloud/concepts/cloud.md": "https://docs.langchain.com/langsmith/cloud",
     "cloud/faq/studio.md": "https://docs.langchain.com/langsmith/studio",
     "cloud/how-tos/human_in_the_loop_edit_state.md": "https://docs.langchain.com/langsmith/add-human-in-the-loop",
@@ -111,8 +111,8 @@ REDIRECT_MAP = {
     "tutorials/introduction.ipynb": "https://docs.langchain.com/oss/python/langgraph/overview",
     "agents/deployment.md": "https://docs.langchain.com/oss/python/langgraph/local-server",
     # deployment redirects
-    "how-tos/deploy-self-hosted.md": "https://docs.langchain.com/langsmith/hosting",
-    "concepts/self_hosted.md": "https://docs.langchain.com/langsmith/hosting",
+    "how-tos/deploy-self-hosted.md": "https://docs.langchain.com/langsmith/platform-setup",
+    "concepts/self_hosted.md": "https://docs.langchain.com/langsmith/platform-setup",
     "tutorials/deployment.md": "https://docs.langchain.com/langsmith/deployments",
     # assistant redirects
     "cloud/how-tos/assistant_versioning.md": "https://docs.langchain.com/langsmith/configuration-cloud",
@@ -129,16 +129,30 @@ REDIRECT_MAP = {
     "how-tos/human_in_the_loop/edit-graph-state.ipynb": "https://docs.langchain.com/oss/python/langgraph/use-time-travel",
 
     # LGP mintlify migration redirects
+    "examples/index.md": "https://docs.langchain.com/oss/python/learn",
+    "guides/index.md": "https://docs.langchain.com/oss/python/langgraph/overview",
+    "concepts/index.md": "https://docs.langchain.com/oss/python/langgraph/overview",
+    "tutorials/index.md": "https://docs.langchain.com/oss/python/learn",
+    "llms-txt-overview.md": "https://docs.langchain.com/llms.txt",
+    "tutorials/rag/langgraph_adaptive_rag.md": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+    "tutorials/multi_agent/multi-agent-collaboration.ipynb": "https://docs.langchain.com/oss/python/langchain/multi-agent",
+    "how-tos/create-react-agent-manage-message-history.ipynb": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+    "how-tos/many-tools.ipynb": "https://docs.langchain.com/oss/python/langchain/tools",
+    "tutorials/customer-support/customer-support.ipynb": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+    "how-tos/react-agent-structured-output.ipynb": "https://docs.langchain.com/oss/python/langchain/agents#structured-output",
+    "tutorials/code_assistant/langgraph_code_assistant.ipynb": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+    "tutorials/multi_agent/hierarchical_agent_teams.ipynb": "https://docs.langchain.com/oss/python/langchain/supervisor",
     "tutorials/auth/getting_started.md": "https://docs.langchain.com/langsmith/auth",
     "tutorials/auth/resource_auth.md": "https://docs.langchain.com/langsmith/resource-auth",
     "tutorials/auth/add_auth_server.md": "https://docs.langchain.com/langsmith/add-auth-server",
     "how-tos/use-remote-graph.md": "https://docs.langchain.com/langsmith/use-remote-graph",
     "how-tos/autogen-integration.md": "https://docs.langchain.com/langsmith/autogen-integration",
+    "how-tos/human_in_the_loop/wait-user-input.ipynb": "https://docs.langchain.com/oss/python/langgraph/interrupts",
     "cloud/how-tos/use_stream_react.md": "https://docs.langchain.com/langsmith/use-stream-react",
     "cloud/how-tos/generative_ui_react.md": "https://docs.langchain.com/langsmith/generative-ui-react",
     "concepts/langgraph_platform.md": "https://docs.langchain.com/langsmith/deployments",
     "concepts/langgraph_components.md": "https://docs.langchain.com/langsmith/components",
-    "concepts/langgraph_server.md": "https://docs.langchain.com/langsmith/langgraph-server",
+    "concepts/langgraph_server.md": "https://docs.langchain.com/langsmith/agent-server",
     "concepts/langgraph_data_plane.md": "https://docs.langchain.com/langsmith/data-plane",
     "concepts/langgraph_control_plane.md": "https://docs.langchain.com/langsmith/control-plane",
     "concepts/langgraph_cli.md": "https://docs.langchain.com/langsmith/cli",
@@ -180,7 +194,7 @@ REDIRECT_MAP = {
     "cloud/concepts/data_storage_and_privacy.md": "https://docs.langchain.com/langsmith/data-storage-and-privacy",
     "cloud/deployment/semantic_search.md": "https://docs.langchain.com/langsmith/semantic-search",
     "how-tos/ttl/configure_ttl.md": "https://docs.langchain.com/langsmith/configure-ttl",
-    "concepts/deployment_options.md": "https://docs.langchain.com/langsmith/hosting",
+    "concepts/deployment_options.md": "https://docs.langchain.com/langsmith/platform-setup",
     "cloud/quick_start.md": "https://docs.langchain.com/langsmith/deployment-quickstart",
     "cloud/deployment/setup.md": "https://docs.langchain.com/langsmith/setup-app-requirements-txt",
     "cloud/deployment/setup_pyproject.md": "https://docs.langchain.com/langsmith/setup-pyproject",
@@ -201,7 +215,7 @@ REDIRECT_MAP = {
     "cloud/deployment/egress.md": "https://docs.langchain.com/langsmith/env-var",
     "cloud/how-tos/streaming.md": "https://docs.langchain.com/langsmith/streaming",
     "cloud/reference/api/api_ref.md": "https://docs.langchain.com/langsmith/server-api-ref",
-    "cloud/reference/langgraph_server_changelog.md": "https://docs.langchain.com/langsmith/langgraph-server-changelog",
+    "cloud/reference/langgraph_server_changelog.md": "https://docs.langchain.com/langsmith/agent-server-changelog",
     "cloud/reference/api/api_ref_control_plane.md": "https://docs.langchain.com/langsmith/api-ref-control-plane",
     "cloud/reference/cli.md": "https://docs.langchain.com/langsmith/cli",
     "cloud/reference/env_var.md": "https://docs.langchain.com/langsmith/env-var",
@@ -219,12 +233,15 @@ REDIRECT_MAP = {
     "tutorials/get-started/6-time-travel.md": "https://docs.langchain.com/oss/python/langgraph/quickstart",
     "tutorials/langsmith/local-server.md": "https://docs.langchain.com/oss/python/langgraph/local-server",
     "tutorials/workflows.md": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+    "tutorials/plan-and-execute/plan-and-execute.ipynb": "https://docs.langchain.com/oss/python/langchain/middleware/built-in#to-do-list",
+    "tutorials/langgraph-platform/local-server/local-server.md": "https://docs.langchain.com/langsmith/local-server",
     "concepts/agentic_concepts.md": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
     "guides/index.md": "https://docs.langchain.com/oss/python/langchain/overview",
     "agents/overview.md": "https://docs.langchain.com/oss/python/langchain/agents",
     "agents/run_agents.md": "https://docs.langchain.com/oss/python/langgraph/quickstart",
     "concepts/low_level.md": "https://docs.langchain.com/oss/python/langgraph/graph-api",
     "how-tos/graph-api.md": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+    "how-tos/react-agent-from-scratch.ipynb": "https://docs.langchain.com/oss/python/langchain/quickstart",
     "concepts/functional_api.md": "https://docs.langchain.com/oss/python/langgraph/functional-api",
     "how-tos/use-functional-api.md": "https://docs.langchain.com/oss/python/langgraph/functional-api",
     "concepts/pregel.md": "https://docs.langchain.com/oss/python/langgraph/pregel",
@@ -282,8 +299,8 @@ REDIRECT_MAP = {
     "reference/supervisor.md": "https://reference.langchain.com/python/langgraph/supervisor/",
     "reference/swarm.md": "https://reference.langchain.com/python/langgraph/swarm/",
     "reference/mcp.md": "https://reference.langchain.com/python/langgraph/mcp/",
-    "cloud/reference/sdk/python_sdk_ref.md": "https://reference.langchain.com/python/platform/python_sdk/",
-    "reference/remote_graph.md": "https://reference.langchain.com/python/platform/remote_graph/",
+    "cloud/reference/sdk/python_sdk_ref.md": "https://reference.langchain.com/python/langsmith/deployment/sdk/",
+    "reference/remote_graph.md": "https://reference.langchain.com/python/langsmith/deployment/remote_graph/",
 
     # additional exclude-search entries from mkdocs.yml
     "additional-resources/index.md": "https://docs.langchain.com/oss/python/langchain/overview",
@@ -294,8 +311,8 @@ REDIRECT_MAP = {
     "cloud/deployment/custom_docker.md": "https://docs.langchain.com/langsmith/custom-docker",
     "cloud/deployment/egress.md": "https://docs.langchain.com/langsmith/env-var",
     "cloud/deployment/graph_rebuild.md": "https://docs.langchain.com/langsmith/graph-rebuild",
-    "cloud/deployment/self_hosted_control_plane.md": "https://docs.langchain.com/langsmith/hosting",
-    "cloud/deployment/self_hosted_data_plane.md": "https://docs.langchain.com/langsmith/hosting",
+    "cloud/deployment/self_hosted_control_plane.md": "https://docs.langchain.com/langsmith/platform-setup",
+    "cloud/deployment/self_hosted_data_plane.md": "https://docs.langchain.com/langsmith/platform-setup",
     "cloud/deployment/semantic_search.md": "https://docs.langchain.com/langsmith/semantic-search",
     "cloud/deployment/setup_javascript.md": "https://docs.langchain.com/langsmith/setup-javascript",
     "cloud/deployment/setup_pyproject.md": "https://docs.langchain.com/langsmith/setup-pyproject",
@@ -331,7 +348,7 @@ REDIRECT_MAP = {
     "cloud/reference/api/api_ref.md": "https://docs.langchain.com/langsmith/server-api-ref",
     "cloud/reference/cli.md": "https://docs.langchain.com/langsmith/cli",
     "cloud/reference/env_var.md": "https://docs.langchain.com/langsmith/env-var",
-    "cloud/reference/langgraph_server_changelog.md": "https://docs.langchain.com/langsmith/langgraph-server-changelog",
+    "cloud/reference/langgraph_server_changelog.md": "https://docs.langchain.com/langsmith/agent-server-changelog",
     "cloud/reference/sdk/js_ts_sdk_ref.md": "https://reference.langchain.com/javascript/modules/langsmith.html",
     "concepts/application_structure.md": "https://docs.langchain.com/langsmith/application-structure",
     "concepts/assistants.md": "https://docs.langchain.com/langsmith/assistants",
@@ -345,9 +362,9 @@ REDIRECT_MAP = {
     "concepts/langgraph_control_plane.md": "https://docs.langchain.com/langsmith/control-plane",
     "concepts/langgraph_data_plane.md": "https://docs.langchain.com/langsmith/data-plane",
     "concepts/langgraph_platform.md": "https://docs.langchain.com/langsmith/home",
-    "concepts/langgraph_self_hosted_control_plane.md": "https://docs.langchain.com/langsmith/hosting",
-    "concepts/langgraph_self_hosted_data_plane.md": "https://docs.langchain.com/langsmith/hosting",
-    "concepts/langgraph_server.md": "https://docs.langchain.com/langsmith/langgraph-server",
+    "concepts/langgraph_self_hosted_control_plane.md": "https://docs.langchain.com/langsmith/platform-setup",
+    "concepts/langgraph_self_hosted_data_plane.md": "https://docs.langchain.com/langsmith/platform-setup",
+    "concepts/langgraph_server.md": "https://docs.langchain.com/langsmith/agent-server",
     "concepts/langgraph_standalone_container.md": "https://docs.langchain.com/langsmith/docker",
     "concepts/langgraph_studio.md": "https://docs.langchain.com/langsmith/studio",
     "concepts/plans.md": "https://docs.langchain.com/langsmith/home",
@@ -793,6 +810,17 @@ def on_post_build(config):
     # Track which paths have explicit redirects
     redirected_paths = set()
 
+    # Collect all existing HTML files in the site
+    all_html_files = set()
+    for root, dirs, files in os.walk(site_dir):
+        for file in files:
+            if file.endswith(".html"):
+                # Get relative path from site_dir
+                html_path = os.path.relpath(os.path.join(root, file), site_dir)
+                # Normalize path separators to forward slashes
+                html_path = html_path.replace(os.sep, "/")
+                all_html_files.add(html_path)
+
     # Process explicit redirects from REDIRECT_MAP
     for page_old, page_new in REDIRECT_MAP.items():
         # Convert .ipynb to .md for path calculation
@@ -847,6 +875,24 @@ def on_post_build(config):
 
             _write_html(site_dir, old_html_path, new_html_path)
 
+    # Create catch-all redirects for any HTML files not explicitly redirected
+    catchall_url = "https://docs.langchain.com/oss/python/langgraph/overview"
+    for html_file in all_html_files:
+        # Skip if this file is already explicitly redirected
+        if html_file in redirected_paths:
+            continue
+
+        # Skip the root index.html (we handle that separately)
+        if html_file == "index.html":
+            continue
+
+        # Skip reference documentation (keep those accessible)
+        if html_file.startswith("reference/"):
+            continue
+
+        # Create redirect for this unmapped file
+        _write_html(site_dir, html_file, catchall_url)
+
     # Create root index.html redirect
     root_redirect_html = """<!doctype html>
 <html lang="en">

libs/checkpoint-postgres/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

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

@@ -97,7 +97,7 @@ class PostgresSaver(BasePostgresSaver):
                 strict=False,
             ):
                 cur.execute(migration)
-                cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
+                cur.execute("INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,))
         if self.pipe:
             self.pipe.sync()
 

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

@@ -102,7 +102,9 @@ class AsyncPostgresSaver(BasePostgresSaver):
                 strict=False,
             ):
                 await cur.execute(migration)
-                await cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
+                await cur.execute(
+                    "INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,)
+                )
         if self.pipe:
             await self.pipe.sync()
 

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

@@ -81,7 +81,7 @@ MIGRATIONS = [
     """
     CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_writes_thread_id_idx ON checkpoint_writes(thread_id);
     """,
-    """ALTER TABLE checkpoint_writes ADD COLUMN task_path TEXT NOT NULL DEFAULT '';""",
+    """ALTER TABLE checkpoint_writes ADD COLUMN IF NOT EXISTS task_path TEXT NOT NULL DEFAULT '';""",
 ]
 
 SELECT_SQL = """

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

@@ -77,7 +77,7 @@ MIGRATIONS = [
     CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_writes_thread_id_idx ON checkpoint_writes(thread_id);
     """,
     """
-    ALTER TABLE checkpoint_writes ADD COLUMN task_path TEXT NOT NULL DEFAULT '';
+    ALTER TABLE checkpoint_writes ADD COLUMN IF NOT EXISTS task_path TEXT NOT NULL DEFAULT '';
     """,
 ]
 
@@ -252,7 +252,7 @@ class ShallowPostgresSaver(BasePostgresSaver):
                 strict=False,
             ):
                 cur.execute(migration)
-                cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
+                cur.execute("INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,))
         if self.pipe:
             self.pipe.sync()
 
@@ -614,7 +614,9 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
                 strict=False,
             ):
                 await cur.execute(migration)
-                await cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
+                await cur.execute(
+                    "INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,)
+                )
         if self.pipe:
             await self.pipe.sync()
 

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

@@ -91,7 +91,12 @@ WHERE expires_at IS NOT NULL;
 VECTOR_MIGRATIONS: Sequence[Migration] = [
     Migration(
         """
-CREATE EXTENSION IF NOT EXISTS vector;
+DO $$
+BEGIN
+    IF NOT EXISTS (SELECT 1 FROM pg_extension WHERE extname = 'vector') THEN
+        CREATE EXTENSION vector;
+    END IF;
+END $$;
 """,
     ),
     Migration(

libs/checkpoint-postgres/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.0"
+version = "3.0.1"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"
@@ -19,7 +19,10 @@ dependencies = [
 ]
 
 [project.urls]
-Repository = "https://www.github.com/langchain-ai/langgraph"
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-postgres"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [dependency-groups]
 test = [

libs/checkpoint-postgres/uv.lock

@@ -234,7 +234,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "3.0.0"
+version = "3.0.1"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -244,7 +244,7 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "langchain-core", specifier = ">=0.2.38" },
-    { name = "ormsgpack", specifier = ">=1.10.0" },
+    { name = "ormsgpack", specifier = ">=1.12.0" },
 ]
 
 [package.metadata.requires-dev]
@@ -281,7 +281,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.0"
+version = "3.0.1"
 source = { editable = "." }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -505,57 +505,57 @@ wheels = [
 
 [[package]]
 name = "ormsgpack"
-version = "1.11.0"
+version = "1.12.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/65/f8/224c342c0e03e131aaa1a1f19aa2244e167001783a433f4eed10eedd834b/ormsgpack-1.11.0.tar.gz", hash = "sha256:7c9988e78fedba3292541eb3bb274fa63044ef4da2ddb47259ea70c05dee4206", size = 49357, upload-time = "2025-10-08T17:29:15.621Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/67/d5ef41c3b4a94400be801984ef7c7fc9623e1a82b643e74eeec367e7462b/ormsgpack-1.12.0.tar.gz", hash = "sha256:94be818fdbb0285945839b88763b269987787cb2f7ef280cad5d6ec815b7e608", size = 49959, upload-time = "2025-11-04T18:30:10.083Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ff/3d/6996193cb2babc47fc92456223bef7d141065357ad4204eccf313f47a7b3/ormsgpack-1.11.0-cp310-cp310-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:03d4e658dd6e1882a552ce1d13cc7b49157414e7d56a4091fbe7823225b08cba", size = 367965, upload-time = "2025-10-08T17:28:06.736Z" },
-    { url = "https://files.pythonhosted.org/packages/35/89/c83b805dd9caebb046f4ceeed3706d0902ed2dbbcf08b8464e89f2c52e05/ormsgpack-1.11.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1bb67eb913c2b703f0ed39607fc56e50724dd41f92ce080a586b4d6149eb3fe4", size = 195209, upload-time = "2025-10-08T17:28:08.395Z" },
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 ]
 
 [[package]]

libs/checkpoint-sqlite/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

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

@@ -329,8 +329,9 @@ class SqliteSaver(BaseCheckpointSaver[str]):
         FROM checkpoints
         {where}
         ORDER BY checkpoint_id DESC"""
-        if limit:
-            query += f" LIMIT {limit}"
+        if limit is not None:
+            query += " LIMIT ?"
+            param_values = (*param_values, limit)
         with self.cursor(transaction=False) as cur, closing(self.conn.cursor()) as wcur:
             cur.execute(query, param_values)
             for (

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

@@ -425,8 +425,9 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         FROM checkpoints
         {where}
         ORDER BY checkpoint_id DESC"""
-        if limit:
-            query += f" LIMIT {limit}"
+        if limit is not None:
+            query += " LIMIT ?"
+            params = (*params, limit)
         async with (
             self.lock,
             self.conn.execute(query, params) as cur,

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

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import json
+import re
 from collections.abc import Sequence
 from typing import Any
 
@@ -8,6 +9,23 @@ from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import get_checkpoint_id
 
 
+def _validate_filter_key(key: str) -> None:
+    """Validate that a filter key is safe for use in SQL queries.
+
+    Args:
+        key: The filter key to validate
+
+    Raises:
+        ValueError: If the key contains invalid characters that could enable SQL injection
+    """
+    # Allow alphanumeric characters, underscores, dots, and hyphens
+    # This covers typical JSON property names while preventing SQL injection
+    if not re.match(r"^[a-zA-Z0-9_.-]+$", key):
+        raise ValueError(
+            f"Invalid filter key: '{key}'. Filter keys must contain only alphanumeric characters, underscores, dots, and hyphens."
+        )
+
+
 def _metadata_predicate(
     metadata_filter: dict[str, Any],
 ) -> tuple[Sequence[str], Sequence[Any]]:
@@ -43,6 +61,7 @@ def _metadata_predicate(
 
     # process metadata query
     for query_key, query_value in metadata_filter.items():
+        _validate_filter_key(query_key)
         operator, param_value = _where_value(query_value)
         predicates.append(
             f"json_extract(CAST(metadata AS TEXT), '$.{query_key}') {operator}"

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

@@ -404,12 +404,9 @@ class BaseSqliteStore:
                         # SQLite json_extract returns unquoted string values
                         if isinstance(value, str):
                             filter_conditions.append(
-                                "json_extract(value, '$."
-                                + key
-                                + "') = '"
-                                + value.replace("'", "''")
-                                + "'"
+                                "json_extract(value, '$." + key + "') = ?"
                             )
+                            filter_params.append(value)
                         elif value is None:
                             filter_conditions.append(
                                 "json_extract(value, '$." + key + "') IS NULL"
@@ -423,9 +420,11 @@ class BaseSqliteStore:
                                 + ("1" if value else "0")
                             )
                         elif isinstance(value, (int, float)):
+                            # Use parameterized query to handle special floats and large integers
                             filter_conditions.append(
-                                "json_extract(value, '$." + key + "') = " + str(value)
+                                "json_extract(value, '$." + key + "') = ?"
                             )
+                            filter_params.append(float(value))
                         else:
                             # Complex objects (list, dict, …) – compare JSON text
                             filter_conditions.append(
@@ -636,85 +635,66 @@ class BaseSqliteStore:
         # We need to properly format values for SQLite JSON extraction comparison
         if op == "$eq":
             if isinstance(value, str):
-                # Direct string comparison with proper quoting for unquoted json_extract result
-                return (
-                    f"json_extract(value, '$.{key}') = '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') = ?", [value]
             elif value is None:
                 return f"json_extract(value, '$.{key}') IS NULL", []
             elif isinstance(value, bool):
                 # SQLite JSON stores booleans as integers
                 return f"json_extract(value, '$.{key}') = {1 if value else 0}", []
             elif isinstance(value, (int, float)):
-                return f"json_extract(value, '$.{key}') = {value}", []
+                # Convert to float to handle inf, -inf, nan, and very large integers
+                # SQLite REAL can handle these cases better than INTEGER
+                return f"json_extract(value, '$.{key}') = ?", [float(value)]
             else:
                 return f"json_extract(value, '$.{key}') = ?", [orjson.dumps(value)]
         elif op == "$gt":
             # For numeric values, SQLite needs to compare as numbers, not strings
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) > {value}", []
+                # Convert to float to handle special values and very large integers
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) > ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') > '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') > ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') > ?", [orjson.dumps(value)]
         elif op == "$gte":
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) >= {value}", []
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) >= ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') >= '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') >= ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') >= ?", [orjson.dumps(value)]
         elif op == "$lt":
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) < {value}", []
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) < ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') < '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') < ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') < ?", [orjson.dumps(value)]
         elif op == "$lte":
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) <= {value}", []
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) <= ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') <= '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') <= ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') <= ?", [orjson.dumps(value)]
         elif op == "$ne":
             if isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') != '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') != ?", [value]
             elif value is None:
                 return f"json_extract(value, '$.{key}') IS NOT NULL", []
             elif isinstance(value, bool):
                 return f"json_extract(value, '$.{key}') != {1 if value else 0}", []
             elif isinstance(value, (int, float)):
-                return f"json_extract(value, '$.{key}') != {value}", []
+                # Convert to float for consistency
+                return f"json_extract(value, '$.{key}') != ?", [float(value)]
             else:
                 return f"json_extract(value, '$.{key}') != ?", [orjson.dumps(value)]
         else:
@@ -874,85 +854,66 @@ class SqliteStore(BaseSqliteStore, BaseStore):
         # We need to properly format values for SQLite JSON extraction comparison
         if op == "$eq":
             if isinstance(value, str):
-                # Direct string comparison with proper quoting for unquoted json_extract result
-                return (
-                    f"json_extract(value, '$.{key}') = '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') = ?", [value]
             elif value is None:
                 return f"json_extract(value, '$.{key}') IS NULL", []
             elif isinstance(value, bool):
                 # SQLite JSON stores booleans as integers
                 return f"json_extract(value, '$.{key}') = {1 if value else 0}", []
             elif isinstance(value, (int, float)):
-                return f"json_extract(value, '$.{key}') = {value}", []
+                # Convert to float to handle inf, -inf, nan, and very large integers
+                # SQLite REAL can handle these cases better than INTEGER
+                return f"json_extract(value, '$.{key}') = ?", [float(value)]
             else:
                 return f"json_extract(value, '$.{key}') = ?", [orjson.dumps(value)]
         elif op == "$gt":
             # For numeric values, SQLite needs to compare as numbers, not strings
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) > {value}", []
+                # Convert to float to handle special values and very large integers
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) > ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') > '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') > ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') > ?", [orjson.dumps(value)]
         elif op == "$gte":
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) >= {value}", []
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) >= ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') >= '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') >= ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') >= ?", [orjson.dumps(value)]
         elif op == "$lt":
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) < {value}", []
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) < ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') < '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') < ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') < ?", [orjson.dumps(value)]
         elif op == "$lte":
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) <= {value}", []
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) <= ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') <= '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') <= ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') <= ?", [orjson.dumps(value)]
         elif op == "$ne":
             if isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') != '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') != ?", [value]
             elif value is None:
                 return f"json_extract(value, '$.{key}') IS NOT NULL", []
             elif isinstance(value, bool):
                 return f"json_extract(value, '$.{key}') != {1 if value else 0}", []
             elif isinstance(value, (int, float)):
-                return f"json_extract(value, '$.{key}') != {value}", []
+                # Convert to float for consistency
+                return f"json_extract(value, '$.{key}') != ?", [float(value)]
             else:
                 return f"json_extract(value, '$.{key}') != ?", [orjson.dumps(value)]
         else:

libs/checkpoint-sqlite/pyproject.toml

@@ -18,7 +18,10 @@ dependencies = [
 ]
 
 [project.urls]
-Repository = "https://www.github.com/langchain-ai/langgraph"
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-sqlite"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [dependency-groups]
 test = [

libs/checkpoint-sqlite/tests/test_aiosqlite.py

@@ -113,4 +113,78 @@ class TestAsyncSqliteSaver:
                 search_results_5[1].config["configurable"]["checkpoint_ns"],
             } == {"", "inner"}
 
-            # TODO: test before and limit params
+            # Test limit param
+            search_results_6 = [
+                c
+                async for c in saver.alist(
+                    {"configurable": {"thread_id": "thread-2"}}, limit=1
+                )
+            ]
+            assert len(search_results_6) == 1
+            assert search_results_6[0].config["configurable"]["thread_id"] == "thread-2"
+
+            # Test before param
+            search_results_7 = [
+                c async for c in saver.alist(None, before=search_results_5[1].config)
+            ]
+            assert len(search_results_7) == 1
+            assert search_results_7[0].config["configurable"]["thread_id"] == "thread-1"
+
+    async def test_limit_parameter_sql_injection_prevention(self) -> None:
+        """Test that the limit parameter properly uses parameterized queries to prevent SQL injection."""
+        async with AsyncSqliteSaver.from_conn_string(":memory:") as saver:
+            # Setup: Create multiple checkpoints
+            for i in range(5):
+                config: RunnableConfig = {
+                    "configurable": {
+                        "thread_id": f"thread-{i}",
+                        "checkpoint_ns": "",
+                    }
+                }
+                checkpoint = empty_checkpoint()
+                metadata: CheckpointMetadata = {"index": i}
+                await saver.aput(config, checkpoint, metadata, {})
+
+            # Test that limit works correctly with valid integer
+            results = [c async for c in saver.alist(None, limit=2)]
+            assert len(results) == 2
+
+            # Test that limit=0 returns no results
+            results = [c async for c in saver.alist(None, limit=0)]
+            assert len(results) == 0
+
+            # Test that limit=None returns all results
+            results = [c async for c in saver.alist(None, limit=None)]
+            assert len(results) == 5
+
+            # Test explicit SQL injection attempt via limit parameter
+            # Even if type checking is bypassed and a malicious string is passed,
+            # the parameterized query will treat it as a value, not SQL code
+            # This would cause an error (can't convert string to int for LIMIT),
+            # which is the correct secure behavior
+            malicious_limits = [
+                "1; DROP TABLE checkpoints; --",
+                "1 OR 1=1",
+                "999999 UNION SELECT * FROM checkpoints",
+            ]
+
+            for malicious_limit in malicious_limits:
+                # The parameterized query should safely reject non-integer limits
+                # or convert them in a way that prevents SQL injection
+                try:
+                    # Bypass type checking by casting
+                    results = [
+                        c
+                        async for c in saver.alist(None, limit=malicious_limit)  # type: ignore
+                    ]
+                    # If it doesn't raise an error, it should at least not execute the injection
+                    # SQLite's parameter binding will try to convert the string to an integer
+                    # which will either fail or treat it as 0
+                except Exception:
+                    # Expected: SQLite should reject invalid limit values
+                    pass
+
+            # Verify the checkpoints table still exists and has all data
+            # (would have been dropped if injection succeeded)
+            results = [c async for c in saver.alist(None, limit=None)]
+            assert len(results) == 5

libs/checkpoint-sqlite/tests/test_sqlite.py

@@ -182,3 +182,120 @@ class TestSqliteSaver:
             with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"):
                 async for _ in saver.alist(self.config_1):
                     pass
+
+    def test_metadata_predicate_sql_injection_prevention(self) -> None:
+        """Test that _metadata_predicate rejects malicious filter keys."""
+        # Test various SQL injection payloads
+        malicious_keys = [
+            "x') OR '1'='1",  # Boolean-based injection
+            "x') OR 1=1 --",  # Comment-based injection
+            "x') UNION SELECT 1,2,3,4,5,6,7 --",  # UNION-based injection
+            "access') = 'public' OR '1'='1' OR json_extract(value, '$.",  # Complex injection
+            "'; DROP TABLE checkpoints; --",  # Destructive injection
+        ]
+
+        for malicious_key in malicious_keys:
+            with pytest.raises(ValueError, match="Invalid filter key"):
+                _metadata_predicate({malicious_key: "dummy"})
+
+    def test_checkpoint_search_sql_injection_prevention(self) -> None:
+        """Test that SQL injection via malicious filter keys is prevented in checkpoint search."""
+        with SqliteSaver.from_conn_string(":memory:") as saver:
+            # Setup: Create checkpoints with different metadata
+            config_public: RunnableConfig = {
+                "configurable": {
+                    "thread_id": "thread-public",
+                    "checkpoint_ns": "",
+                }
+            }
+            config_private: RunnableConfig = {
+                "configurable": {
+                    "thread_id": "thread-private",
+                    "checkpoint_ns": "",
+                }
+            }
+
+            checkpoint_public = empty_checkpoint()
+            checkpoint_private = empty_checkpoint()
+
+            metadata_public: CheckpointMetadata = {
+                "access": "public",
+                "data": "public information",
+            }
+            metadata_private: CheckpointMetadata = {
+                "access": "private",
+                "data": "secret information",
+                "password": "secret123",
+            }
+
+            saver.put(config_public, checkpoint_public, metadata_public, {})
+            saver.put(config_private, checkpoint_private, metadata_private, {})
+
+            # Normal query - should return only public checkpoint
+            normal_results = list(saver.list(None, filter={"access": "public"}))
+            assert len(normal_results) == 1
+            assert normal_results[0].metadata["access"] == "public"
+
+            # SQL injection attempt should raise ValueError
+            malicious_key = (
+                "access') = 'public' OR '1'='1' OR json_extract(metadata, '$."
+            )
+
+            with pytest.raises(ValueError, match="Invalid filter key"):
+                list(saver.list(None, filter={malicious_key: "dummy"}))
+
+    def test_limit_parameter_sql_injection_prevention(self) -> None:
+        """Test that the limit parameter properly uses parameterized queries to prevent SQL injection."""
+        with SqliteSaver.from_conn_string(":memory:") as saver:
+            # Setup: Create multiple checkpoints
+            for i in range(5):
+                config: RunnableConfig = {
+                    "configurable": {
+                        "thread_id": f"thread-{i}",
+                        "checkpoint_ns": "",
+                    }
+                }
+                checkpoint = empty_checkpoint()
+                metadata: CheckpointMetadata = {"index": i}
+                saver.put(config, checkpoint, metadata, {})
+
+            # Test that limit works correctly with valid integer
+            results = list(saver.list(None, limit=2))
+            assert len(results) == 2
+
+            # Test that limit=0 returns no results
+            results = list(saver.list(None, limit=0))
+            assert len(results) == 0
+
+            # Test that limit=None returns all results
+            results = list(saver.list(None, limit=None))
+            assert len(results) == 5
+
+            # Test explicit SQL injection attempt via limit parameter
+            # Even if type checking is bypassed and a malicious string is passed,
+            # the parameterized query will treat it as a value, not SQL code
+            # This would cause an error (can't convert string to int for LIMIT),
+            # which is the correct secure behavior
+            malicious_limits = [
+                "1; DROP TABLE checkpoints; --",
+                "1 OR 1=1",
+                "999999 UNION SELECT * FROM checkpoints",
+            ]
+
+            for malicious_limit in malicious_limits:
+                # The parameterized query should safely reject non-integer limits
+                # or convert them in a way that prevents SQL injection
+                try:
+                    # Bypass type checking by casting
+                    results = list(saver.list(None, limit=malicious_limit))  # type: ignore
+                    # If it doesn't raise an error, it should at least not execute the injection
+                    # SQLite's parameter binding will try to convert the string to an integer
+                    # which will either fail or treat it as 0
+                except Exception:
+                    # Expected: SQLite should reject invalid limit values
+                    pass
+
+            # Verify the checkpoints table still exists and has all data
+            # (would have been dropped if injection succeeded)
+            results = list(saver.list(None, limit=None))
+            assert len(results) == 5

libs/checkpoint-sqlite/tests/test_store.py

@@ -1069,6 +1069,110 @@ def test_sql_injection_vulnerability(store: SqliteStore) -> None:
         store.search(("docs",), filter={malicious_key: "dummy"})
 
 
+def test_sql_injection_filter_values(store: SqliteStore) -> None:
+    """Test that SQL injection via malicious filter values is properly escaped."""
+    # Setup: Create documents with different access levels
+    store.put(("docs",), "doc1", {"access": "public", "title": "Public Document"})
+    store.put(("docs",), "doc2", {"access": "private", "title": "Private Document"})
+    store.put(("docs",), "doc3", {"access": "secret", "title": "Secret Document"})
+
+    # Test 1: Basic SQL injection attempt with single quote
+    malicious_value = "public' OR '1'='1"
+    results = store.search(("docs",), filter={"access": malicious_value})
+    # Should return 0 results because the malicious value is escaped and won't match anything
+    assert len(results) == 0, "SQL injection via string value should be blocked"
+
+    # Test 2: SQL injection with comment
+    malicious_value = "public'; --"
+    results = store.search(("docs",), filter={"access": malicious_value})
+    assert len(results) == 0, "SQL comment injection should be blocked"
+
+    # Test 3: UNION injection attempt
+    malicious_value = "public' UNION SELECT * FROM store --"
+    results = store.search(("docs",), filter={"access": malicious_value})
+    assert len(results) == 0, "UNION injection should be blocked"
+
+    # Test 4: Parameterized queries handle strings with null bytes and SQL injection attempts safely
+    malicious_value = "public\x00' OR '1'='1"
+    results = store.search(("docs",), filter={"access": malicious_value})
+    assert len(results) == 0, (
+        "Parameterized queries treat injection attempts as literal strings"
+    )
+
+    # Test 5: Multiple single quotes
+    malicious_value = "''''"
+    results = store.search(("docs",), filter={"access": malicious_value})
+    assert len(results) == 0, "Multiple quotes should be handled safely"
+
+    # Test 6: Legitimate value with single quote should work
+    store.put(("docs",), "doc4", {"title": "O'Brien's Document", "access": "public"})
+    results = store.search(("docs",), filter={"title": "O'Brien's Document"})
+    assert len(results) == 1, "Legitimate single quotes should work"
+    assert results[0].value["title"] == "O'Brien's Document"
+
+    # Test 7: Unicode characters with injection attempt
+    malicious_value = "public' OR 'א'='א"
+    results = store.search(("docs",), filter={"access": malicious_value})
+    assert len(results) == 0, "Unicode-based injection should be blocked"
+
+
+def test_numeric_filter_safety(store: SqliteStore) -> None:
+    """Test that numeric filter values are handled safely."""
+    # Setup: Create documents with numeric fields
+    store.put(("items",), "item1", {"price": 10, "quantity": 5})
+    store.put(("items",), "item2", {"price": 20, "quantity": 3})
+    store.put(("items",), "item3", {"price": 30, "quantity": 1})
+
+    # Test 1: Normal numeric comparison
+    results = store.search(("items",), filter={"price": {"$gt": 15}})
+    assert len(results) == 2
+    assert all(r.value["price"] > 15 for r in results)
+
+    # Test 2: Special float values (infinity)
+    results = store.search(("items",), filter={"price": {"$lt": float("inf")}})
+    assert len(results) == 3, "All finite values should be less than infinity"
+
+    # Test 3: Special float values (negative infinity)
+    results = store.search(("items",), filter={"price": {"$gt": float("-inf")}})
+    assert len(results) == 3, (
+        "All finite values should be greater than negative infinity"
+    )
+
+    # Test 4: NaN handling - NaN comparisons should not cause errors
+    try:
+        results = store.search(("items",), filter={"price": {"$eq": float("nan")}})
+        # NaN never equals anything, including itself, so should return 0 results
+        assert len(results) == 0
+    except Exception as e:
+        pytest.fail(f"NaN handling should not raise exception: {e}")
+
+    # Test 5: Very large numbers
+    results = store.search(("items",), filter={"price": {"$lt": 10**100}})
+    assert len(results) == 3, "Very large numbers should be handled safely"
+
+    # Test 6: Negative numbers
+    store.put(("items",), "item4", {"price": -10, "quantity": 0})
+    results = store.search(("items",), filter={"price": {"$lt": 0}})
+    assert len(results) == 1
+    assert results[0].key == "item4"
+
+
+def test_boolean_filter_safety(store: SqliteStore) -> None:
+    """Test that boolean filter values are handled safely."""
+    store.put(("flags",), "flag1", {"active": True, "name": "Feature A"})
+    store.put(("flags",), "flag2", {"active": False, "name": "Feature B"})
+    store.put(("flags",), "flag3", {"active": True, "name": "Feature C"})
+
+    # Test boolean filters
+    results = store.search(("flags",), filter={"active": True})
+    assert len(results) == 2
+    assert all(r.value["active"] is True for r in results)
+
+    results = store.search(("flags",), filter={"active": False})
+    assert len(results) == 1
+    assert results[0].value["active"] is False
+
+
 @pytest.mark.parametrize("distance_type", VECTOR_TYPES)
 def test_non_ascii(
     fake_embeddings: CharacterEmbeddings,

libs/checkpoint-sqlite/uv.lock

@@ -246,7 +246,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "3.0.0"
+version = "3.0.1"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -256,7 +256,7 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "langchain-core", specifier = ">=0.2.38" },
-    { name = "ormsgpack", specifier = ">=1.10.0" },
+    { name = "ormsgpack", specifier = ">=1.12.0" },
 ]
 
 [package.metadata.requires-dev]
@@ -511,57 +511,57 @@ wheels = [
 
 [[package]]
 name = "ormsgpack"
-version = "1.11.0"
+version = "1.12.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/65/f8/224c342c0e03e131aaa1a1f19aa2244e167001783a433f4eed10eedd834b/ormsgpack-1.11.0.tar.gz", hash = "sha256:7c9988e78fedba3292541eb3bb274fa63044ef4da2ddb47259ea70c05dee4206", size = 49357, upload-time = "2025-10-08T17:29:15.621Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/67/d5ef41c3b4a94400be801984ef7c7fc9623e1a82b643e74eeec367e7462b/ormsgpack-1.12.0.tar.gz", hash = "sha256:94be818fdbb0285945839b88763b269987787cb2f7ef280cad5d6ec815b7e608", size = 49959, upload-time = "2025-11-04T18:30:10.083Z" }
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 [[package]]

libs/checkpoint/README.md

@@ -38,8 +38,10 @@ Each checkpointer should conform to `langgraph.checkpoint.base.BaseCheckpointSav
 - `.put_writes` - Store intermediate writes linked to a checkpoint (i.e. pending writes).
 - `.get_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `checkpoint_id`).
 - `.list` - List checkpoints that match a given configuration and filter criteria.
+- `.delete_thread()` - Delete all checkpoints and writes associated with a thread.
+- `.get_next_version()` - Generate the next version ID for a channel.
 
-If the checkpointer will be used with asynchronous graph execution (i.e. executing the graph via `.ainvoke`, `.astream`, `.abatch`), checkpointer must implement asynchronous versions of the above methods (`.aput`, `.aput_writes`, `.aget_tuple`, `.alist`).
+If the checkpointer will be used with asynchronous graph execution (i.e. executing the graph via `.ainvoke`, `.astream`, `.abatch`), checkpointer must implement asynchronous versions of the above methods (`.aput`, `.aput_writes`, `.aget_tuple`, `.alist`). Similarly, the checkpointer must implement `.adelete_thread()` if asynchronous thread cleanup is desired. The base class provides a default implementation of `.get_next_version()` that generates an integer sequence starting from 1, but this method should be overridden for custom versioning schemes.
 
 ## Usage
 

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

@@ -60,23 +60,28 @@ class Checkpoint(TypedDict):
     """State snapshot at a given point in time."""
 
     v: int
-    """The version of the checkpoint format. Currently 1."""
+    """The version of the checkpoint format. Currently `1`."""
     id: str
-    """The ID of the checkpoint. This is both unique and monotonically
-    increasing, so can be used for sorting checkpoints from first to last."""
+    """The ID of the checkpoint.
+    
+    This is both unique and monotonically increasing, so can be used for sorting
+    checkpoints from first to last."""
     ts: str
     """The timestamp of the checkpoint in ISO 8601 format."""
     channel_values: dict[str, Any]
     """The values of the channels at the time of the checkpoint.
+    
     Mapping from channel name to deserialized channel snapshot value.
     """
     channel_versions: ChannelVersions
     """The versions of the channels at the time of the checkpoint.
+    
     The keys are channel names and the values are monotonically increasing
     version strings for each channel.
     """
     versions_seen: dict[str, ChannelVersions]
     """Map from node ID to map from channel name to version seen.
+    
     This keeps track of the versions of the channels that each node has seen.
     Used to determine which nodes to execute next.
     """
@@ -352,8 +357,9 @@ class BaseCheckpointSaver(Generic[V]):
     def get_next_version(self, current: V | None, channel: None) -> V:
         """Generate the next version ID for a channel.
 
-        Default is to use integer versions, incrementing by `1`. If you override, you can use `str`/`int`/`float`
-        versions, as long as they are monotonically increasing.
+        Default is to use integer versions, incrementing by `1`.
+
+        If you override, you can use `str`/`int`/`float` versions, as long as they are monotonically increasing.
 
         Args:
             current: The current version identifier (`int`, `float`, or `str`).

libs/checkpoint/langgraph/checkpoint/memory/__init__.py

@@ -33,7 +33,7 @@ class InMemorySaver(
 ):
     """An in-memory checkpoint saver.
 
-    This checkpoint saver stores checkpoints in memory using a defaultdict.
+    This checkpoint saver stores checkpoints in memory using a `defaultdict`.
 
     Note:
         Only use `InMemorySaver` for debugging or testing purposes.
@@ -44,22 +44,23 @@ class InMemorySaver(
     Args:
         serde: The serializer to use for serializing and deserializing checkpoints.
 
-    Examples:
+    Example:
+        ```python
+        import asyncio
 
-            import asyncio
+        from langgraph.checkpoint.memory import InMemorySaver
+        from langgraph.graph import StateGraph
 
-            from langgraph.checkpoint.memory import InMemorySaver
-            from langgraph.graph import StateGraph
+        builder = StateGraph(int)
+        builder.add_node("add_one", lambda x: x + 1)
+        builder.set_entry_point("add_one")
+        builder.set_finish_point("add_one")
 
-            builder = StateGraph(int)
-            builder.add_node("add_one", lambda x: x + 1)
-            builder.set_entry_point("add_one")
-            builder.set_finish_point("add_one")
-
-            memory = InMemorySaver()
-            graph = builder.compile(checkpointer=memory)
-            coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}})
-            asyncio.run(coro)  # Output: 2
+        memory = InMemorySaver()
+        graph = builder.compile(checkpointer=memory)
+        coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}})
+        asyncio.run(coro)  # Output: 2
+        ```
     """
 
     # thread ID ->  checkpoint NS -> checkpoint ID -> checkpoint mapping
@@ -96,7 +97,8 @@ class InMemorySaver(
             self.stack.enter_context(self.blobs)  # type: ignore[arg-type]
 
     def __enter__(self) -> InMemorySaver:
-        return self.stack.__enter__()
+        self.stack.__enter__()
+        return self
 
     def __exit__(
         self,
@@ -107,7 +109,8 @@ class InMemorySaver(
         return self.stack.__exit__(exc_type, exc_value, traceback)
 
     async def __aenter__(self) -> InMemorySaver:
-        return self.stack.__enter__()
+        self.stack.__enter__()
+        return self
 
     async def __aexit__(
         self,

libs/checkpoint/langgraph/checkpoint/serde/base.py

@@ -15,9 +15,7 @@ class UntypedSerializerProtocol(Protocol):
 class SerializerProtocol(Protocol):
     """Protocol for serialization and deserialization of objects.
 
-    - `dumps`: Serialize an object to bytes.
     - `dumps_typed`: Serialize an object to a tuple `(type, bytes)`.
-    - `loads`: Deserialize an object from bytes.
     - `loads_typed`: Deserialize an object from a tuple `(type, bytes)`.
 
     Valid implementations include the `pickle`, `json` and `orjson` modules.
@@ -52,12 +50,13 @@ def maybe_add_typed_methods(
 
 class CipherProtocol(Protocol):
     """Protocol for encryption and decryption of data.
+
     - `encrypt`: Encrypt plaintext.
     - `decrypt`: Decrypt ciphertext.
     """
 
     def encrypt(self, plaintext: bytes) -> tuple[str, bytes]:
-        """Encrypt plaintext. Returns a tuple (cipher name, ciphertext)."""
+        """Encrypt plaintext. Returns a tuple `(cipher name, ciphertext)`."""
         ...
 
     def decrypt(self, ciphername: str, ciphertext: bytes) -> bytes:

libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py

@@ -41,10 +41,12 @@ logger = logging.getLogger(__name__)
 class JsonPlusSerializer(SerializerProtocol):
     """Serializer that uses ormsgpack, with optional fallbacks.
 
-    Security note: this serializer is intended for use within the BaseCheckpointSaver
-    class and called within the Pregel loop. It should not be used on untrusted
-    python objects. If an attacker can write directly to your checkpoint database,
-    they may be able to trigger code execution when data is deserialized.
+    !!! warning
+
+        Security note: This serializer is intended for use within the `BaseCheckpointSaver`
+        class and called within the Pregel loop. It should not be used on untrusted
+        python objects. If an attacker can write directly to your checkpoint database,
+        they may be able to trigger code execution when data is deserialized.
     """
 
     def __init__(
@@ -640,6 +642,7 @@ _option = (
     | ormsgpack.OPT_PASSTHROUGH_DATETIME
     | ormsgpack.OPT_PASSTHROUGH_ENUM
     | ormsgpack.OPT_PASSTHROUGH_UUID
+    | ormsgpack.OPT_REPLACE_SURROGATES
 )
 
 

libs/checkpoint/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint"
-version = "3.0.0"
+version = "3.0.1"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 requires-python = ">=3.10"
@@ -13,11 +13,14 @@ license = "MIT"
 license-files = ['LICENSE']
 dependencies = [
     "langchain-core>=0.2.38",
-    "ormsgpack>=1.10.0",
+    "ormsgpack>=1.12.0",
 ]
 
 [project.urls]
-Repository = "https://www.github.com/langchain-ai/langgraph"
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [dependency-groups]
 test = [

libs/checkpoint/tests/test_jsonplus.py

@@ -149,20 +149,20 @@ def test_serde_jsonplus() -> None:
         assert serde.loads_typed(serde.dumps_typed(value)) == value
 
     surrogates = [
-        "Hello\ud83d\ude00",
-        "Python\ud83d\udc0d",
-        "Surrogate\ud834\udd1e",
-        "Example\ud83c\udf89",
-        "String\ud83c\udfa7",
-        "With\ud83c\udf08",
-        "Surrogates\ud83d\ude0e",
-        "Embedded\ud83d\udcbb",
-        "In\ud83c\udf0e",
-        "The\ud83d\udcd6",
-        "Text\ud83d\udcac",
+        "Hello??",
+        "Python??",
+        "Surrogate??",
+        "Example??",
+        "String??",
+        "With??",
+        "Surrogates??",
+        "Embedded??",
+        "In??",
+        "The??",
+        "Text??",
         "收花🙄·到",
     ]
-    serde = JsonPlusSerializer(pickle_fallback=True)
+    serde = JsonPlusSerializer(pickle_fallback=False)
 
     assert serde.loads_typed(serde.dumps_typed(surrogates)) == surrogates
 

libs/checkpoint/tests/test_memory.py

@@ -188,7 +188,14 @@ class TestMemorySaver:
         assert len(search_results_4) == 0
 
 
-def test_memory_saver() -> None:
+async def test_memory_saver() -> None:
     from langgraph.checkpoint.memory import InMemorySaver
 
-    assert isinstance(InMemorySaver(), InMemorySaver)
+    memory_saver = InMemorySaver()
+    assert isinstance(memory_saver, InMemorySaver)
+
+    async with memory_saver as async_memory_saver:
+        assert async_memory_saver is memory_saver
+
+    with memory_saver as sync_memory_saver:
+        assert sync_memory_saver is memory_saver

libs/checkpoint/uv.lock

@@ -51,75 +51,100 @@ wheels = [
 
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+version = "2025.10.5"
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 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/e4/37/af0d2ef3967ac0d6113837b44a4f0bfe1328c2b9763bd5b1744520e5cfed/certifi-2025.10.5-py3-none-any.whl", hash = "sha256:0f212c2744a9bb6de0c56639a6f68afe01ecd92d91f14ae897c4fe7bbeeef0de", size = 163286, upload-time = "2025-10-05T04:12:14.03Z" },
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-version = "3.4.3"
+version = "3.4.4"
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 [[package]]

libs/cli/examples/graph_prerelease_reqs/agent.py

@@ -1,13 +1,12 @@
 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)]
+tools = []
 
 model_oai = ChatOpenAI(temperature=0)
 

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

@@ -5,5 +5,5 @@ description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "langgraph==0.6.0"
+  "langgraph==1.0.2"
 ]

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

@@ -5,5 +5,5 @@ description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "langchain-openai==0.3.0"
+  "langchain-openai==1.0.1"
 ]

libs/cli/examples/graph_prerelease_reqs/pyproject.toml

@@ -6,9 +6,9 @@ readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
   "langchain-openai==1.0.0a2",
-  "langgraph==1.0.0a2",
-  "langchain_community>=0.3.0",
+  "langchain-anthropic==1.0.0a5",
+  "langgraph==1.0.2"
 ]
 
 [tool.uv]
-prerelease = "allow"
+prerelease = "allow"

libs/cli/generate_schema.py

@@ -13,8 +13,9 @@ from pathlib import Path
 
 import msgspec
 
-from langgraph_cli.config import (
+from langgraph_cli.schemas import (
     AuthConfig,
+    CacheConfig,
     CheckpointerConfig,
     Config,
     ConfigurableHeaderConfig,
@@ -22,6 +23,7 @@ from langgraph_cli.config import (
     HttpConfig,
     IndexConfig,
     SecurityConfig,
+    SerdeConfig,
     StoreConfig,
     ThreadTTLConfig,
     TTLConfig,
@@ -110,8 +112,10 @@ def add_descriptions_to_schema(schema, cls):
                 SecurityConfig,
                 HttpConfig,
                 CorsConfig,
+                CacheConfig,
                 ThreadTTLConfig,
                 CheckpointerConfig,
+                SerdeConfig,
                 TTLConfig,
                 ConfigurableHeaderConfig,
             ]:

libs/cli/langgraph_cli/__init__.py

@@ -1 +1 @@
-__version__ = "0.4.4"
+__version__ = "0.4.7"

libs/cli/langgraph_cli/config.py

@@ -4,10 +4,12 @@ import pathlib
 import re
 import textwrap
 from collections import Counter
-from typing import Any, Literal, NamedTuple, TypedDict
+from typing import Literal, NamedTuple
 
 import click
 
+from langgraph_cli.schemas import Config, Distros
+
 MIN_NODE_VERSION = "20"
 DEFAULT_NODE_VERSION = "20"
 
@@ -17,504 +19,6 @@ DEFAULT_PYTHON_VERSION = "3.11"
 DEFAULT_IMAGE_DISTRO = "debian"
 
 
-Distros = Literal["debian", "wolfi", "bullseye", "bookworm"]
-MiddlewareOrders = Literal["auth_first", "middleware_first"]
-
-
-class TTLConfig(TypedDict, total=False):
-    """Configuration for TTL (time-to-live) behavior in the store."""
-
-    refresh_on_read: bool
-    """Default behavior for refreshing TTLs on read operations (`GET` and `SEARCH`).
-    
-    If `True`, TTLs will be refreshed on read operations (get/search) by default.
-    This can be overridden per-operation by explicitly setting `refresh_ttl`.
-    Defaults to `True` if not configured.
-    """
-    default_ttl: float | None
-    """Optional. Default TTL (time-to-live) in minutes for new items.
-    
-    If provided, all new items will have this TTL unless explicitly overridden.
-    If omitted, items will have no TTL by default.
-    """
-    sweep_interval_minutes: int | None
-    """Optional. Interval in minutes between TTL sweep iterations.
-    
-    If provided, the store will periodically delete expired items based on the TTL.
-    If omitted, no automatic sweeping will occur.
-    """
-
-
-class IndexConfig(TypedDict, total=False):
-    """Configuration for indexing documents for semantic search in the store.
-
-    This governs how text is converted into embeddings and stored for vector-based lookups.
-    """
-
-    dims: int
-    """Required. Dimensionality of the embedding vectors you will store.
-    
-    Must match the output dimension of your selected embedding model or custom embed function.
-    If mismatched, you will likely encounter shape/size errors when inserting or querying vectors.
-    
-    Common embedding model output dimensions:
-        - openai:text-embedding-3-large: 3072
-        - openai:text-embedding-3-small: 1536
-        - openai:text-embedding-ada-002: 1536
-        - cohere:embed-english-v3.0: 1024
-        - cohere:embed-english-light-v3.0: 384
-        - cohere:embed-multilingual-v3.0: 1024
-        - cohere:embed-multilingual-light-v3.0: 384
-    """
-
-    embed: str
-    """Required. Identifier or reference to the embedding model or a custom embedding function.
-    
-    The format can vary:
-      - "<provider>:<model_name>" for recognized providers (e.g., "openai:text-embedding-3-large")
-      - "path/to/module.py:function_name" for your own local embedding function
-      - "my_custom_embed" if it's a known alias in your system
-
-     Examples:
-        - "openai:text-embedding-3-large"
-        - "cohere:embed-multilingual-v3.0"
-        - "src/app.py:embeddings"
-    
-    Note: Must return embeddings of dimension `dims`.
-    """
-
-    fields: list[str] | None
-    """Optional. List of JSON fields to extract before generating embeddings.
-    
-    Defaults to ["$"], which means the entire JSON object is embedded as one piece of text.
-    If you provide multiple fields (e.g. ["title", "content"]), each is extracted and embedded separately,
-    often saving token usage if you only care about certain parts of the data.
-    
-    Example:
-        fields=["title", "abstract", "author.biography"]
-    """
-
-
-class StoreConfig(TypedDict, total=False):
-    """Configuration for the built-in long-term memory store.
-
-    This store can optionally perform semantic search. If you omit `index`,
-    the store will just handle traditional (non-embedded) data without vector lookups.
-    """
-
-    index: IndexConfig | None
-    """Optional. Defines the vector-based semantic search configuration.
-    
-    If provided, the store will:
-      - Generate embeddings according to `index.embed`
-      - Enforce the embedding dimension given by `index.dims`
-      - Embed only specified JSON fields (if any) from `index.fields`
-    
-    If omitted, no vector index is initialized.
-    """
-
-    ttl: TTLConfig | None
-    """Optional. Defines the TTL (time-to-live) behavior configuration.
-    
-    If provided, the store will apply TTL settings according to the configuration.
-    If omitted, no TTL behavior is configured.
-    """
-
-
-class ThreadTTLConfig(TypedDict, total=False):
-    """Configure a default TTL for checkpointed data within threads."""
-
-    strategy: Literal["delete"]
-    """Strategy to use for deleting checkpointed data.
-    
-    Choices:
-      - "delete": Delete all checkpoints for a thread after TTL expires.
-    """
-    default_ttl: float | None
-    """Default TTL (time-to-live) in minutes for checkpointed data."""
-    sweep_interval_minutes: int | None
-    """Interval in minutes between sweep iterations.
-    If omitted, a default interval will be used (typically ~ 5 minutes)."""
-
-
-class CheckpointerConfig(TypedDict, total=False):
-    """Configuration for the built-in checkpointer, which handles checkpointing of state.
-
-    If omitted, no checkpointer is set up (the object store will still be present, however).
-    """
-
-    ttl: ThreadTTLConfig | None
-    """Optional. Defines the TTL (time-to-live) behavior configuration.
-    
-    If provided, the checkpointer will apply TTL settings according to the configuration.
-    If omitted, no TTL behavior is configured.
-    """
-
-
-class SecurityConfig(TypedDict, total=False):
-    """Configuration for OpenAPI security definitions and requirements.
-
-    Useful for specifying global or path-level authentication and authorization flows
-    (e.g., OAuth2, API key headers, etc.).
-    """
-
-    securitySchemes: dict[str, dict[str, Any]]
-    """Describe each security scheme recognized by your OpenAPI spec.
-    
-    Keys are scheme names (e.g. "OAuth2", "ApiKeyAuth") and values are their definitions.
-    Example:
-        {
-            "OAuth2": {
-                "type": "oauth2",
-                "flows": {
-                    "password": {
-                        "tokenUrl": "/token",
-                        "scopes": {"read": "Read data", "write": "Write data"}
-                    }
-                }
-            }
-        }
-    """
-    security: list[dict[str, list[str]]]
-    """Global security requirements across all endpoints.
-    
-    Each element in the list maps a security scheme (e.g. "OAuth2") to a list of scopes (e.g. ["read", "write"]).
-    Example:
-        [
-            {"OAuth2": ["read", "write"]},
-            {"ApiKeyAuth": []}
-        ]
-    """
-    # path => {method => security}
-    paths: dict[str, dict[str, list[dict[str, list[str]]]]]
-    """Path-specific security overrides.
-    
-    Keys are path templates (e.g., "/items/{item_id}"), mapping to:
-      - Keys that are HTTP methods (e.g., "GET", "POST"),
-      - Values are lists of security definitions (just like `security`) for that method.
-    
-    Example:
-        {
-            "/private_data": {
-                "GET": [{"OAuth2": ["read"]}],
-                "POST": [{"OAuth2": ["write"]}]
-            }
-        }
-    """
-
-
-class AuthConfig(TypedDict, total=False):
-    """Configuration for custom authentication logic and how it integrates into the OpenAPI spec."""
-
-    path: str
-    """Required. Path to an instance of the Auth() class that implements custom authentication.
-    
-    Format: "path/to/file.py:my_auth"
-    """
-    disable_studio_auth: bool
-    """Optional. Whether to disable LangSmith API-key authentication for requests originating the Studio. 
-    
-    Defaults to False, meaning that if a particular header is set, the server will verify the `x-api-key` header
-    value is a valid API key for the deployment's workspace. If `True`, all requests will go through your custom
-    authentication logic, regardless of origin of the request.
-    """
-    openapi: SecurityConfig
-    """The security configuration to include in your server's OpenAPI spec.
-    
-    Example (OAuth2):
-        {
-            "securitySchemes": {
-                "OAuth2": {
-                    "type": "oauth2",
-                    "flows": {
-                        "password": {
-                            "tokenUrl": "/token",
-                            "scopes": {"me": "Read user info", "items": "Manage items"}
-                        }
-                    }
-                }
-            },
-            "security": [
-                {"OAuth2": ["me"]}
-            ]
-        }
-    """
-
-
-class CorsConfig(TypedDict, total=False):
-    """Specifies Cross-Origin Resource Sharing (CORS) rules for your server.
-
-    If omitted, defaults are typically very restrictive (often no cross-origin requests).
-    Configure carefully if you want to allow usage from browsers hosted on other domains.
-    """
-
-    allow_origins: list[str]
-    """Optional. List of allowed origins (e.g., "https://example.com").
-    
-    Default is often an empty list (no external origins). 
-    Use "*" only if you trust all origins, as that bypasses most restrictions.
-    """
-    allow_methods: list[str]
-    """Optional. HTTP methods permitted for cross-origin requests (e.g. ["GET", "POST"]).
-    
-    Default might be ["GET", "POST", "OPTIONS"] depending on your server framework.
-    """
-    allow_headers: list[str]
-    """Optional. HTTP headers that can be used in cross-origin requests (e.g. ["Content-Type", "Authorization"])."""
-    allow_credentials: bool
-    """Optional. If `True`, cross-origin requests can include credentials (cookies, auth headers).
-    
-    Default False to avoid accidentally exposing secured endpoints to untrusted sites.
-    """
-    allow_origin_regex: str
-    """Optional. A regex pattern for matching allowed origins, used if you have dynamic subdomains.
-    
-    Example: "^https://.*\\.mycompany\\.com$"
-    """
-    expose_headers: list[str]
-    """Optional. List of headers that browsers are allowed to read from the response in cross-origin contexts."""
-    max_age: int
-    """Optional. How many seconds the browser may cache preflight responses.
-    
-    Default might be 600 (10 minutes). Larger values reduce preflight requests but can cause stale configurations.
-    """
-
-
-class ConfigurableHeaderConfig(TypedDict):
-    """Customize which headers to include as configurable values in your runs.
-
-    By default, omits x-api-key, x-tenant-id, and x-service-key.
-
-    Exclusions (if provided) take precedence.
-
-    Each value can be a raw string with an optional wildcard.
-    """
-
-    includes: list[str] | None
-    """Headers to include (if not also matches against an 'exludes' pattern.
-
-    Examples:
-        - 'user-agent'
-        - 'x-configurable-*'
-    """
-    excludes: list[str] | None
-    """Headers to exclude. Applied before the 'includes' checks.
-
-    Examples:
-        - 'x-api-key'
-        - '*key*'
-        - '*token*'
-    """
-
-
-class HttpConfig(TypedDict, total=False):
-    """Configuration for the built-in HTTP server that powers your deployment's routes and endpoints."""
-
-    app: str
-    """Optional. Import path to a custom Starlette/FastAPI application to mount.
-    
-    Format: "path/to/module.py:app_var"
-    If provided, it can override or extend the default routes.
-    """
-    disable_assistants: bool
-    """Optional. If `True`, /assistants routes are removed from the server.
-    
-    Default is False (meaning /assistants is enabled).
-    """
-    disable_threads: bool
-    """Optional. If `True`, /threads routes are removed.
-    
-    Default is False.
-    """
-    disable_runs: bool
-    """Optional. If `True`, /runs routes are removed.
-    
-    Default is False.
-    """
-    disable_store: bool
-    """Optional. If `True`, /store routes are removed, disabling direct store interactions via HTTP.
-    
-    Default is False.
-    """
-    disable_mcp: bool
-    """Optional. If `True`, /mcp routes are removed, disabling the MCP server.
-    
-    Default is False.
-    """
-    disable_meta: bool
-    """Optional. Remove meta endpoints.
-    
-    Set to True to disable the following endpoints: /openapi.json, /info, /metrics, /docs.
-    This will also make the /ok endpoint skip any DB or other checks, always returning {"ok": True}.
-    
-    Default is False.
-    """
-    cors: CorsConfig | None
-    """Optional. Defines CORS restrictions. If omitted, no special rules are set and 
-    cross-origin behavior depends on default server settings.
-    """
-    configurable_headers: ConfigurableHeaderConfig | None
-    """Optional. Defines how headers are treated for a run's configuration.
-
-    You can include or exclude headers as configurable values to condition your
-    agent's behavior or permissions on a request's headers."""
-    logging_headers: ConfigurableHeaderConfig | None
-    """Optional. Defines which headers are excluded from logging."""
-    middleware_order: MiddlewareOrders | None
-    """Optional. Defines the order in which to apply server customizations.
-
-    Choices:
-      - "auth_first": Authentication hooks (custom or default) are evaluated
-      before custom middleware.
-      - "middleware_first": Custom middleware is evaluated
-      before authentication hooks (custom or default).
-
-    Default is `middleware_first`.
-    """
-    enable_custom_route_auth: bool
-    """Optional. If `True`, authentication is enabled for custom routes,
-    not just the routes that are protected by default.
-    (Routes protected by default include /assistants, /threads, and /runs).
-
-    Default is False. This flag only affects authentication behavior
-    if `app` is provided and contains custom routes.
-    """
-
-
-class Config(TypedDict, total=False):
-    """Top-level config for langgraph-cli or similar deployment tooling."""
-
-    python_version: str
-    """Optional. Python version in 'major.minor' format (e.g. '3.11'). 
-    Must be at least 3.11 or greater for this deployment to function properly.
-    """
-
-    node_version: str | None
-    """Optional. Node.js version as a major version (e.g. '20'), if your deployment needs Node.
-    Must be >= 20 if provided.
-    """
-
-    api_version: str | None
-    """Optional. Which semantic version of the LangGraph API server to use.
-    
-    Defaults to latest. Check the
-    [changelog](https://docs.langchain.com/langgraph-platform/langgraph-server-changelog)
-    for more information."""
-
-    _INTERNAL_docker_tag: str | None
-    """Optional. Internal use only.
-    """
-
-    base_image: str | None
-    """Optional. Base image to use for the LangGraph API server.
-    
-    Defaults to langchain/langgraph-api or langchain/langgraphjs-api."""
-
-    image_distro: Distros | None
-    """Optional. Linux distribution for the base image.
-    
-    Must be one of 'wolfi', 'debian', 'bullseye', or 'bookworm'.
-    If omitted, defaults to 'debian' ('latest').
-    """
-
-    pip_config_file: str | None
-    """Optional. Path to a pip config file (e.g., "/etc/pip.conf" or "pip.ini") for controlling
-    package installation (custom indices, credentials, etc.).
-    
-    Only relevant if Python dependencies are installed via pip. If omitted, default pip settings are used.
-    """
-
-    pip_installer: str | None
-    """Optional. Python package installer to use ('auto', 'pip', 'uv').
-    
-    - 'auto' (default): Use uv for supported base images, otherwise pip
-    - 'pip': Force use of pip regardless of base image support
-    - 'uv': Force use of uv (will fail if base image doesn't support it)
-    """
-
-    dockerfile_lines: list[str]
-    """Optional. Additional Docker instructions that will be appended to your base Dockerfile.
-    
-    Useful for installing OS packages, setting environment variables, etc. 
-    Example:
-        dockerfile_lines=[
-            "RUN apt-get update && apt-get install -y libmagic-dev",
-            "ENV MY_CUSTOM_VAR=hello_world"
-        ]
-    """
-
-    dependencies: list[str]
-    """List of Python dependencies to install, either from PyPI or local paths.
-    
-    Examples:
-      - "." or "./src" if you have a local Python package
-      - str (aka "anthropic") for a PyPI package
-      - "git+https://github.com/org/repo.git@main" for a Git-based package
-    Defaults to an empty list, meaning no additional packages installed beyond your base environment.
-    """
-
-    graphs: dict[str, str]
-    """Optional. Named definitions of graphs, each pointing to a Python object.
-
-    
-    Graphs can be StateGraph, @entrypoint, or any other Pregel object OR they can point to (async) context
-    managers that accept a single configuration argument (of type RunnableConfig) and return a pregel object
-    (instance of Stategraph, etc.).
-    
-    Keys are graph names, values are "path/to/file.py:object_name".
-    Example:
-        {
-            "mygraph": "graphs/my_graph.py:graph_definition",
-            "anothergraph": "graphs/another.py:get_graph"
-        }
-    """
-
-    env: dict[str, str] | str
-    """Optional. Environment variables to set for your deployment.
-    
-    - If given as a dict, keys are variable names and values are their values.
-    - If given as a string, it must be a path to a file containing lines in KEY=VALUE format.
-    
-    Example as a dict:
-        env={"API_TOKEN": "abc123", "DEBUG": "true"}
-    Example as a file path:
-        env=".env"
-    """
-
-    store: StoreConfig | None
-    """Optional. Configuration for the built-in long-term memory store, including semantic search indexing.
-    
-    If omitted, no vector index is set up (the object store will still be present, however).
-    """
-
-    checkpointer: CheckpointerConfig | None
-    """Optional. Configuration for the built-in checkpointer, which handles checkpointing of state.
-    
-    If omitted, no checkpointer is set up (the object store will still be present, however).
-    """
-
-    auth: AuthConfig | None
-    """Optional. Custom authentication config, including the path to your Python auth logic and 
-    the OpenAPI security definitions it uses.
-    """
-
-    http: HttpConfig | None
-    """Optional. Configuration for the built-in HTTP server, controlling which custom routes are exposed
-    and how cross-origin requests are handled.
-    """
-
-    ui: dict[str, str] | None
-    """Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.
-    """
-
-    keep_pkg_tools: bool | list[str] | None
-    """Optional. Control whether to retain Python packaging tools in the final image.
-    
-    Allowed tools are: "pip", "setuptools", "wheel".
-    You can also set to true to include all packaging tools.
-    """
-
-
 _BUILD_TOOLS = ("pip", "setuptools", "wheel")
 
 
@@ -650,6 +154,7 @@ def validate_config(config: Config) -> Config:
         "env": config.get("env", {}),
         "store": config.get("store"),
         "auth": config.get("auth"),
+        "encryption": config.get("encryption"),
         "http": config.get("http"),
         "checkpointer": config.get("checkpointer"),
         "ui": config.get("ui"),
@@ -724,6 +229,14 @@ def validate_config(config: Config) -> Config:
                     f"Invalid auth.path format: '{auth_conf['path']}'. "
                     "Must be in format './path/to/file.py:attribute_name'"
                 )
+    # Validate encryption config
+    if encryption_conf := config.get("encryption"):
+        if "path" in encryption_conf:
+            if ":" not in encryption_conf["path"]:
+                raise ValueError(
+                    f"Invalid encryption.path format: '{encryption_conf['path']}'. "
+                    "Must be in format './path/to/file.py:attribute_name'"
+                )
     if http_conf := config.get("http"):
         if "app" in http_conf:
             if ":" not in http_conf["app"]:
@@ -1110,6 +623,49 @@ def _update_auth_path(
     )
 
 
+def _update_encryption_path(
+    config_path: pathlib.Path, config: Config, local_deps: LocalDeps
+) -> None:
+    """Update encryption.path to use Docker container paths."""
+    encryption_conf = config.get("encryption")
+    if not encryption_conf or not (path_str := encryption_conf.get("path")):
+        return
+
+    module_str, sep, attr_str = path_str.partition(":")
+    if not sep or not module_str.startswith("."):
+        return  # Already validated or absolute path
+
+    resolved = config_path.parent / module_str
+    if not resolved.exists():
+        raise FileNotFoundError(
+            f"Encryption file not found: {resolved} (from {path_str})"
+        )
+    if not resolved.is_file():
+        raise IsADirectoryError(f"Encryption path must be a file: {resolved}")
+
+    # Check faux packages first (higher priority)
+    for faux_path, (_, destpath) in local_deps.faux_pkgs.items():
+        if resolved.is_relative_to(faux_path):
+            new_path = f"{destpath}/{resolved.relative_to(faux_path)}:{attr_str}"
+            encryption_conf["path"] = new_path
+            return
+
+    # Check real packages
+    for real_path in local_deps.real_pkgs:
+        if resolved.is_relative_to(real_path):
+            new_path = (
+                f"/deps/{real_path.name}/{resolved.relative_to(real_path)}:{attr_str}"
+            )
+            encryption_conf["path"] = new_path
+            return
+
+    raise ValueError(
+        f"Encryption file '{resolved}' not covered by dependencies.\n"
+        "Add its parent directory to the 'dependencies' array in your config.\n"
+        f"Current dependencies: {config['dependencies']}"
+    )
+
+
 def _update_http_app_path(
     config_path: pathlib.Path, config: Config, local_deps: LocalDeps
 ) -> None:
@@ -1305,6 +861,8 @@ def python_config_to_docker(
     _update_graph_paths(config_path, config, local_deps)
     # Rewrite auth path, so it points to the correct location in the Docker container
     _update_auth_path(config_path, config, local_deps)
+    # Rewrite encryption path, so it points to the correct location in the Docker container
+    _update_encryption_path(config_path, config, local_deps)
     # Rewrite HTTP app path, so it points to the correct location in the Docker container
     _update_http_app_path(config_path, config, local_deps)
 
@@ -1395,6 +953,9 @@ ADD {relpath} /deps/{name}
     if (auth_config := config.get("auth")) is not None:
         env_vars.append(f"ENV LANGGRAPH_AUTH='{json.dumps(auth_config)}'")
 
+    if (encryption_config := config.get("encryption")) is not None:
+        env_vars.append(f"ENV LANGGRAPH_ENCRYPTION='{json.dumps(encryption_config)}'")
+
     if (http_config := config.get("http")) is not None:
         env_vars.append(f"ENV LANGGRAPH_HTTP='{json.dumps(http_config)}'")
 
@@ -1422,35 +983,51 @@ ADD {relpath} /deps/{name}
             ]
         )
     image_str = docker_tag(config, base_image, api_version)
-    docker_file_contents = [
-        f"FROM {image_str}",
-        "",
-        os.linesep.join(config["dockerfile_lines"]),
-        "",
-        installs,
-        "",
-        "# -- Installing all local dependencies --",
-        f"""RUN for dep in /deps/*; do \
+
+    # Prepare docker file contents
+    docker_file_contents = []
+
+    # Add syntax directive if we have additional contexts (requires BuildKit frontend.contexts capability)
+    if local_deps.additional_contexts:
+        docker_file_contents.extend(
+            [
+                "# syntax=docker/dockerfile:1.4",
+                "",
+            ]
+        )
+
+    # Add main dockerfile content
+    docker_file_contents.extend(
+        [
+            f"FROM {image_str}",
+            "",
+            os.linesep.join(config["dockerfile_lines"]),
+            "",
+            installs,
+            "",
+            "# -- Installing all local dependencies --",
+            f"""RUN for dep in /deps/*; do \
             echo "Installing $dep"; \
             if [ -d "$dep" ]; then \
                 echo "Installing $dep"; \
                 (cd "$dep" && {global_reqs_pip_install} -e .); \
             fi; \
         done""",
-        "# -- End of local dependencies install --",
-        os.linesep.join(env_vars),
-        "",
-        js_inst_str,
-        "",
-        # Add pip cleanup after all installations are complete
-        _get_pip_cleanup_lines(
-            install_cmd=install_cmd,
-            to_uninstall=build_tools_to_uninstall,
-            pip_installer=pip_installer,
-        ),
-        "",
-        f"WORKDIR {local_deps.working_dir}" if local_deps.working_dir else "",
-    ]
+            "# -- End of local dependencies install --",
+            os.linesep.join(env_vars),
+            "",
+            js_inst_str,
+            "",
+            # Add pip cleanup after all installations are complete
+            _get_pip_cleanup_lines(
+                install_cmd=install_cmd,
+                to_uninstall=build_tools_to_uninstall,
+                pip_installer=pip_installer,
+            ),
+            "",
+            f"WORKDIR {local_deps.working_dir}" if local_deps.working_dir else "",
+        ]
+    )
 
     additional_contexts: dict[str, str] = {}
     for p in local_deps.additional_contexts:
@@ -1502,6 +1079,9 @@ def node_config_to_docker(
     if (auth_config := config.get("auth")) is not None:
         env_vars.append(f"ENV LANGGRAPH_AUTH='{json.dumps(auth_config)}'")
 
+    if (encryption_config := config.get("encryption")) is not None:
+        env_vars.append(f"ENV LANGGRAPH_ENCRYPTION='{json.dumps(encryption_config)}'")
+
     if (http_config := config.get("http")) is not None:
         env_vars.append(f"ENV LANGGRAPH_HTTP='{json.dumps(http_config)}'")
 

libs/cli/langgraph_cli/schemas.py

@@ -0,0 +1,639 @@
+from typing import Any, Literal, TypedDict
+
+Distros = Literal["debian", "wolfi", "bullseye", "bookworm"]
+MiddlewareOrders = Literal["auth_first", "middleware_first"]
+
+
+class TTLConfig(TypedDict, total=False):
+    """Configuration for TTL (time-to-live) behavior in the store."""
+
+    refresh_on_read: bool
+    """Default behavior for refreshing TTLs on read operations (`GET` and `SEARCH`).
+    
+    If `True`, TTLs will be refreshed on read operations (get/search) by default.
+    This can be overridden per-operation by explicitly setting `refresh_ttl`.
+    Defaults to `True` if not configured.
+    """
+    default_ttl: float | None
+    """Optional. Default TTL (time-to-live) in minutes for new items.
+    
+    If provided, all new items will have this TTL unless explicitly overridden.
+    If omitted, items will have no TTL by default.
+    """
+    sweep_interval_minutes: int | None
+    """Optional. Interval in minutes between TTL sweep iterations.
+    
+    If provided, the store will periodically delete expired items based on the TTL.
+    If omitted, no automatic sweeping will occur.
+    """
+
+
+class IndexConfig(TypedDict, total=False):
+    """Configuration for indexing documents for semantic search in the store.
+
+    This governs how text is converted into embeddings and stored for vector-based lookups.
+    """
+
+    dims: int
+    """Required. Dimensionality of the embedding vectors you will store.
+    
+    Must match the output dimension of your selected embedding model or custom embed function.
+    If mismatched, you will likely encounter shape/size errors when inserting or querying vectors.
+    
+    Common embedding model output dimensions:
+        - openai:text-embedding-3-large: 3072
+        - openai:text-embedding-3-small: 1536
+        - openai:text-embedding-ada-002: 1536
+        - cohere:embed-english-v3.0: 1024
+        - cohere:embed-english-light-v3.0: 384
+        - cohere:embed-multilingual-v3.0: 1024
+        - cohere:embed-multilingual-light-v3.0: 384
+    """
+
+    embed: str
+    """Required. Identifier or reference to the embedding model or a custom embedding function.
+    
+    The format can vary:
+      - "<provider>:<model_name>" for recognized providers (e.g., "openai:text-embedding-3-large")
+      - "path/to/module.py:function_name" for your own local embedding function
+      - "my_custom_embed" if it's a known alias in your system
+
+     Examples:
+        - "openai:text-embedding-3-large"
+        - "cohere:embed-multilingual-v3.0"
+        - "src/app.py:embeddings"
+    
+    Note: Must return embeddings of dimension `dims`.
+    """
+
+    fields: list[str] | None
+    """Optional. List of JSON fields to extract before generating embeddings.
+    
+    Defaults to ["$"], which means the entire JSON object is embedded as one piece of text.
+    If you provide multiple fields (e.g. ["title", "content"]), each is extracted and embedded separately,
+    often saving token usage if you only care about certain parts of the data.
+    
+    Example:
+        fields=["title", "abstract", "author.biography"]
+    """
+
+
+class StoreConfig(TypedDict, total=False):
+    """Configuration for the built-in long-term memory store.
+
+    This store can optionally perform semantic search. If you omit `index`,
+    the store will just handle traditional (non-embedded) data without vector lookups.
+    """
+
+    index: IndexConfig | None
+    """Optional. Defines the vector-based semantic search configuration.
+    
+    If provided, the store will:
+      - Generate embeddings according to `index.embed`
+      - Enforce the embedding dimension given by `index.dims`
+      - Embed only specified JSON fields (if any) from `index.fields`
+    
+    If omitted, no vector index is initialized.
+    """
+
+    ttl: TTLConfig | None
+    """Optional. Defines the TTL (time-to-live) behavior configuration.
+    
+    If provided, the store will apply TTL settings according to the configuration.
+    If omitted, no TTL behavior is configured.
+    """
+
+
+class ThreadTTLConfig(TypedDict, total=False):
+    """Configure a default TTL for checkpointed data within threads."""
+
+    strategy: Literal["delete"]
+    """Strategy to use for deleting checkpointed data.
+    
+    Choices:
+      - "delete": Delete all checkpoints for a thread after TTL expires.
+    """
+    default_ttl: float | None
+    """Default TTL (time-to-live) in minutes for checkpointed data."""
+    sweep_interval_minutes: int | None
+    """Interval in minutes between sweep iterations.
+    If omitted, a default interval will be used (typically ~ 5 minutes)."""
+
+
+class SerdeConfig(TypedDict, total=False):
+    """Configuration for the built-in serde, which handles checkpointing of state.
+
+    If omitted, no serde is set up (the object store will still be present, however)."""
+
+    allowed_json_modules: list[list[str]] | bool | None
+    """Optional. List of allowed python modules to de-serialize custom objects from.
+    
+    If provided, only the specified modules will be allowed to be deserialized.
+    If omitted, no modules are allowed, and the object returned will simply be a json object OR
+    a deserialized langchain object.
+    
+    Example:
+    {...
+        "serde": {
+            "allowed_json_modules": [
+                ["my_agent", "my_file", "SomeType"],
+            ]
+        }
+    }
+
+    If you set this to True, any module will be allowed to be deserialized.
+
+    Example:
+    {...
+        "serde": {
+            "allowed_json_modules": true
+        }
+    }
+    
+    """
+    pickle_fallback: bool
+    """Optional. Whether to allow pickling as a fallback for deserialization.
+    
+    If True, pickling will be allowed as a fallback for deserialization.
+    If False, pickling will not be allowed as a fallback for deserialization.
+    Defaults to True if not configured."""
+
+
+class CheckpointerConfig(TypedDict, total=False):
+    """Configuration for the built-in checkpointer, which handles checkpointing of state.
+
+    If omitted, no checkpointer is set up (the object store will still be present, however).
+    """
+
+    ttl: ThreadTTLConfig | None
+    """Optional. Defines the TTL (time-to-live) behavior configuration.
+    
+    If provided, the checkpointer will apply TTL settings according to the configuration.
+    If omitted, no TTL behavior is configured.
+    """
+    serde: SerdeConfig | None
+    """Optional. Defines the serde configuration.
+    
+    If provided, the checkpointer will apply serde settings according to the configuration.
+    If omitted, no serde behavior is configured.
+
+    This configuration requires server version 0.5 or later to take effect.
+    """
+
+
+class SecurityConfig(TypedDict, total=False):
+    """Configuration for OpenAPI security definitions and requirements.
+
+    Useful for specifying global or path-level authentication and authorization flows
+    (e.g., OAuth2, API key headers, etc.).
+    """
+
+    securitySchemes: dict[str, dict[str, Any]]
+    """Describe each security scheme recognized by your OpenAPI spec.
+    
+    Keys are scheme names (e.g. "OAuth2", "ApiKeyAuth") and values are their definitions.
+    Example:
+        {
+            "OAuth2": {
+                "type": "oauth2",
+                "flows": {
+                    "password": {
+                        "tokenUrl": "/token",
+                        "scopes": {"read": "Read data", "write": "Write data"}
+                    }
+                }
+            }
+        }
+    """
+    security: list[dict[str, list[str]]]
+    """Global security requirements across all endpoints.
+    
+    Each element in the list maps a security scheme (e.g. "OAuth2") to a list of scopes (e.g. ["read", "write"]).
+    Example:
+        [
+            {"OAuth2": ["read", "write"]},
+            {"ApiKeyAuth": []}
+        ]
+    """
+    # path => {method => security}
+    paths: dict[str, dict[str, list[dict[str, list[str]]]]]
+    """Path-specific security overrides.
+    
+    Keys are path templates (e.g., "/items/{item_id}"), mapping to:
+      - Keys that are HTTP methods (e.g., "GET", "POST"),
+      - Values are lists of security definitions (just like `security`) for that method.
+    
+    Example:
+        {
+            "/private_data": {
+                "GET": [{"OAuth2": ["read"]}],
+                "POST": [{"OAuth2": ["write"]}]
+            }
+        }
+    """
+
+
+class CacheConfig(TypedDict, total=False):
+    cache_keys: list[str]
+    """Optional. List of header keys to use for caching.
+    
+    Example:
+        ["user_id", "workspace_id"]
+    """
+    ttl_seconds: int
+    """Optional. Time-to-live in seconds for cached items.
+    
+    Example:
+        3600
+    """
+    max_size: int
+    """Optional. Maximum size of the cache.
+    
+    Example:
+        100
+    """
+
+
+class AuthConfig(TypedDict, total=False):
+    """Configuration for custom authentication logic and how it integrates into the OpenAPI spec."""
+
+    path: str
+    """Required. Path to an instance of the Auth() class that implements custom authentication.
+    
+    Format: "path/to/file.py:my_auth"
+    """
+    disable_studio_auth: bool
+    """Optional. Whether to disable LangSmith API-key authentication for requests originating the Studio. 
+    
+    Defaults to False, meaning that if a particular header is set, the server will verify the `x-api-key` header
+    value is a valid API key for the deployment's workspace. If `True`, all requests will go through your custom
+    authentication logic, regardless of origin of the request.
+    """
+    openapi: SecurityConfig
+    """The security configuration to include in your server's OpenAPI spec.
+    
+    Example (OAuth2):
+        {
+            "securitySchemes": {
+                "OAuth2": {
+                    "type": "oauth2",
+                    "flows": {
+                        "password": {
+                            "tokenUrl": "/token",
+                            "scopes": {"me": "Read user info", "items": "Manage items"}
+                        }
+                    }
+                }
+            },
+            "security": [
+                {"OAuth2": ["me"]}
+            ]
+        }
+    """
+    cache: CacheConfig
+    """Optional. Cache configuration for the server.
+    
+    Example:
+        {
+            "cache_keys": ["user_id", "workspace_id"],
+            "ttl_seconds": 3600,
+            "max_size": 100
+        }
+    """
+
+
+class EncryptionConfig(TypedDict, total=False):
+    """Configuration for custom at-rest encryption logic.
+
+    Allows you to implement custom encryption for sensitive data stored in the database,
+    including metadata fields and checkpoint blobs.
+    """
+
+    path: str
+    """Required. Path to an instance of the Encryption() class that implements custom encryption handlers.
+
+    Format: "path/to/file.py:my_encryption"
+
+    Example:
+        {
+            "encryption": {
+                "path": "./encryption.py:my_encryption"
+            }
+        }
+    """
+
+
+class CorsConfig(TypedDict, total=False):
+    """Specifies Cross-Origin Resource Sharing (CORS) rules for your server.
+
+    If omitted, defaults are typically very restrictive (often no cross-origin requests).
+    Configure carefully if you want to allow usage from browsers hosted on other domains.
+    """
+
+    allow_origins: list[str]
+    """Optional. List of allowed origins (e.g., "https://example.com").
+    
+    Default is often an empty list (no external origins). 
+    Use "*" only if you trust all origins, as that bypasses most restrictions.
+    """
+    allow_methods: list[str]
+    """Optional. HTTP methods permitted for cross-origin requests (e.g. ["GET", "POST"]).
+    
+    Default might be ["GET", "POST", "OPTIONS"] depending on your server framework.
+    """
+    allow_headers: list[str]
+    """Optional. HTTP headers that can be used in cross-origin requests (e.g. ["Content-Type", "Authorization"])."""
+    allow_credentials: bool
+    """Optional. If `True`, cross-origin requests can include credentials (cookies, auth headers).
+    
+    Default False to avoid accidentally exposing secured endpoints to untrusted sites.
+    """
+    allow_origin_regex: str
+    """Optional. A regex pattern for matching allowed origins, used if you have dynamic subdomains.
+    
+    Example: "^https://.*\\.mycompany\\.com$"
+    """
+    expose_headers: list[str]
+    """Optional. List of headers that browsers are allowed to read from the response in cross-origin contexts."""
+    max_age: int
+    """Optional. How many seconds the browser may cache preflight responses.
+    
+    Default might be 600 (10 minutes). Larger values reduce preflight requests but can cause stale configurations.
+    """
+
+
+class ConfigurableHeaderConfig(TypedDict):
+    """Customize which headers to include as configurable values in your runs.
+
+    By default, omits x-api-key, x-tenant-id, and x-service-key.
+
+    Exclusions (if provided) take precedence.
+
+    Each value can be a raw string with an optional wildcard.
+    """
+
+    includes: list[str] | None
+    """Headers to include (if not also matches against an 'exludes' pattern.
+
+    Examples:
+        - 'user-agent'
+        - 'x-configurable-*'
+    """
+    excludes: list[str] | None
+    """Headers to exclude. Applied before the 'includes' checks.
+
+    Examples:
+        - 'x-api-key'
+        - '*key*'
+        - '*token*'
+    """
+
+
+class HttpConfig(TypedDict, total=False):
+    """Configuration for the built-in HTTP server that powers your deployment's routes and endpoints."""
+
+    app: str
+    """Optional. Import path to a custom Starlette/FastAPI application to mount.
+    
+    Format: "path/to/module.py:app_var"
+    If provided, it can override or extend the default routes.
+    """
+    disable_assistants: bool
+    """Optional. If `True`, /assistants routes are removed from the server.
+    
+    Default is False (meaning /assistants is enabled).
+    """
+    disable_threads: bool
+    """Optional. If `True`, /threads routes are removed.
+    
+    Default is False.
+    """
+    disable_runs: bool
+    """Optional. If `True`, /runs routes are removed.
+    
+    Default is False.
+    """
+    disable_store: bool
+    """Optional. If `True`, /store routes are removed, disabling direct store interactions via HTTP.
+    
+    Default is False.
+    """
+    disable_mcp: bool
+    """Optional. If `True`, /mcp routes are removed, disabling default support to expose the deployment as an MCP server.
+    
+    Default is False.
+    """
+    disable_a2a: bool
+    """Optional. If `True`, /a2a routes are removed, disabling default support to expose the deployment as an agent-to-agent (A2A) server.
+    
+    Default is False.
+    """
+    disable_meta: bool
+    """Optional. Remove meta endpoints.
+    
+    Set to True to disable the following endpoints: /openapi.json, /info, /metrics, /docs.
+    This will also make the /ok endpoint skip any DB or other checks, always returning {"ok": True}.
+    
+    Default is False.
+    """
+    disable_ui: bool
+    """Optional. If `True`, /ui routes are removed, disabling the UI server.
+    
+    Default is False.
+    """
+    disable_webhooks: bool
+    """Optional. If `True`, webhooks are disabled. Runs created with an associated webhook will
+    still be executed, but the webhook event will not be sent.
+    
+    Default is False.
+    """
+    cors: CorsConfig | None
+    """Optional. Defines CORS restrictions. If omitted, no special rules are set and 
+    cross-origin behavior depends on default server settings.
+    """
+    configurable_headers: ConfigurableHeaderConfig | None
+    """Optional. Defines how headers are treated for a run's configuration.
+
+    You can include or exclude headers as configurable values to condition your
+    agent's behavior or permissions on a request's headers."""
+    logging_headers: ConfigurableHeaderConfig | None
+    """Optional. Defines which headers are excluded from logging."""
+    middleware_order: MiddlewareOrders | None
+    """Optional. Defines the order in which to apply server customizations.
+
+    Choices:
+      - "auth_first": Authentication hooks (custom or default) are evaluated
+      before custom middleware.
+      - "middleware_first": Custom middleware is evaluated
+      before authentication hooks (custom or default).
+
+    Default is `middleware_first`.
+    """
+    enable_custom_route_auth: bool
+    """Optional. If `True`, authentication is enabled for custom routes,
+    not just the routes that are protected by default.
+    (Routes protected by default include /assistants, /threads, and /runs).
+
+    Default is False. This flag only affects authentication behavior
+    if `app` is provided and contains custom routes.
+    """
+    mount_prefix: str
+    """Optional. URL prefix to prepend to all the routes.
+    
+    Example:
+        "/api"
+    """
+
+
+class Config(TypedDict, total=False):
+    """Top-level config for langgraph-cli or similar deployment tooling."""
+
+    python_version: str
+    """Optional. Python version in 'major.minor' format (e.g. '3.11'). 
+    Must be at least 3.11 or greater for this deployment to function properly.
+    """
+
+    node_version: str | None
+    """Optional. Node.js version as a major version (e.g. '20'), if your deployment needs Node.
+    Must be >= 20 if provided.
+    """
+
+    api_version: str | None
+    """Optional. Which semantic version of the LangGraph API server to use.
+    
+    Defaults to latest. Check the
+    [changelog](https://docs.langchain.com/langgraph-platform/langgraph-server-changelog)
+    for more information."""
+
+    _INTERNAL_docker_tag: str | None
+    """Optional. Internal use only.
+    """
+
+    base_image: str | None
+    """Optional. Base image to use for the LangGraph API server.
+    
+    Defaults to langchain/langgraph-api or langchain/langgraphjs-api."""
+
+    image_distro: Distros | None
+    """Optional. Linux distribution for the base image.
+    
+    Must be one of 'wolfi', 'debian', 'bullseye', or 'bookworm'.
+    If omitted, defaults to 'debian' ('latest').
+    """
+
+    pip_config_file: str | None
+    """Optional. Path to a pip config file (e.g., "/etc/pip.conf" or "pip.ini") for controlling
+    package installation (custom indices, credentials, etc.).
+    
+    Only relevant if Python dependencies are installed via pip. If omitted, default pip settings are used.
+    """
+
+    pip_installer: str | None
+    """Optional. Python package installer to use ('auto', 'pip', 'uv').
+    
+    - 'auto' (default): Use uv for supported base images, otherwise pip
+    - 'pip': Force use of pip regardless of base image support
+    - 'uv': Force use of uv (will fail if base image doesn't support it)
+    """
+
+    dockerfile_lines: list[str]
+    """Optional. Additional Docker instructions that will be appended to your base Dockerfile.
+    
+    Useful for installing OS packages, setting environment variables, etc. 
+    Example:
+        dockerfile_lines=[
+            "RUN apt-get update && apt-get install -y libmagic-dev",
+            "ENV MY_CUSTOM_VAR=hello_world"
+        ]
+    """
+
+    dependencies: list[str]
+    """List of Python dependencies to install, either from PyPI or local paths.
+    
+    Examples:
+      - "." or "./src" if you have a local Python package
+      - str (aka "anthropic") for a PyPI package
+      - "git+https://github.com/org/repo.git@main" for a Git-based package
+    Defaults to an empty list, meaning no additional packages installed beyond your base environment.
+    """
+
+    graphs: dict[str, str]
+    """Optional. Named definitions of graphs, each pointing to a Python object.
+
+    
+    Graphs can be StateGraph, @entrypoint, or any other Pregel object OR they can point to (async) context
+    managers that accept a single configuration argument (of type RunnableConfig) and return a pregel object
+    (instance of Stategraph, etc.).
+    
+    Keys are graph names, values are "path/to/file.py:object_name".
+    Example:
+        {
+            "mygraph": "graphs/my_graph.py:graph_definition",
+            "anothergraph": "graphs/another.py:get_graph"
+        }
+    """
+
+    env: dict[str, str] | str
+    """Optional. Environment variables to set for your deployment.
+    
+    - If given as a dict, keys are variable names and values are their values.
+    - If given as a string, it must be a path to a file containing lines in KEY=VALUE format.
+    
+    Example as a dict:
+        env={"API_TOKEN": "abc123", "DEBUG": "true"}
+    Example as a file path:
+        env=".env"
+    """
+
+    store: StoreConfig | None
+    """Optional. Configuration for the built-in long-term memory store, including semantic search indexing.
+    
+    If omitted, no vector index is set up (the object store will still be present, however).
+    """
+
+    checkpointer: CheckpointerConfig | None
+    """Optional. Configuration for the built-in checkpointer, which handles checkpointing of state.
+    
+    If omitted, no checkpointer is set up (the object store will still be present, however).
+    """
+
+    auth: AuthConfig | None
+    """Optional. Custom authentication config, including the path to your Python auth logic and
+    the OpenAPI security definitions it uses.
+    """
+
+    encryption: EncryptionConfig | None
+    """Optional. Custom at-rest encryption config, including the path to your Python encryption logic.
+
+    Allows you to implement custom encryption for sensitive data stored in the database.
+    """
+
+    http: HttpConfig | None
+    """Optional. Configuration for the built-in HTTP server, controlling which custom routes are exposed
+    and how cross-origin requests are handled.
+    """
+
+    ui: dict[str, str] | None
+    """Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.
+    """
+
+    keep_pkg_tools: bool | list[str] | None
+    """Optional. Control whether to retain Python packaging tools in the final image.
+    
+    Allowed tools are: "pip", "setuptools", "wheel".
+    You can also set to true to include all packaging tools.
+    """
+
+
+__all__ = [
+    "Config",
+    "StoreConfig",
+    "CheckpointerConfig",
+    "AuthConfig",
+    "EncryptionConfig",
+    "HttpConfig",
+    "MiddlewareOrders",
+    "Distros",
+    "TTLConfig",
+    "IndexConfig",
+]

libs/cli/pyproject.toml

@@ -19,13 +19,16 @@ dependencies = [
 path = "langgraph_cli/__init__.py"
 [project.optional-dependencies]
 inmem = [
-    "langgraph-api>=0.3,<0.5.0 ; python_version >= '3.11'",
+    "langgraph-api>=0.4,<0.6.0 ; python_version >= '3.11'",
     "langgraph-runtime-inmem>=0.7 ; python_version >= '3.11'",
     "python-dotenv>=0.8.0",
 ]
 
 [project.urls]
-Repository = "https://www.github.com/langchain-ai/langgraph"
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/cli"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [project.scripts]
 langgraph = "langgraph_cli.cli:cli"
@@ -68,4 +71,4 @@ lint.select = [
   "UP", # pyupgrade
 ]
 lint.ignore = ["E501", "B008"]
-target-version = "py310"
+target-version = "py310"

libs/cli/python-monorepo-example/pyproject.toml

@@ -8,7 +8,7 @@ authors = [
 license = { text = "MIT" }
 requires-python = ">=3.11,<4.0"
 dependencies = [
-    "langgraph>=0.6.0,<0.7.0",
+    "langgraph>=0.6.0,<2",
     "langchain-core>=0.2.14",
 ]
 

libs/cli/schemas/schema.json

@@ -99,6 +99,17 @@
               },
               "description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
             },
+            "encryption": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/EncryptionConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Custom at-rest encryption config, including the path to your Python encryption logic.\n\nAllows you to implement custom encryption for sensitive data stored in the database.\n"
+            },
             "env": {
               "anyOf": [
                 {
@@ -292,6 +303,17 @@
               },
               "description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
             },
+            "encryption": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/EncryptionConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Custom at-rest encryption config, including the path to your Python encryption logic.\n\nAllows you to implement custom encryption for sensitive data stored in the database.\n"
+            },
             "env": {
               "anyOf": [
                 {
@@ -405,6 +427,10 @@
       "description": "Configuration for custom authentication logic and how it integrates into the OpenAPI spec.",
       "type": "object",
       "properties": {
+        "cache": {
+          "$ref": "#/$defs/CacheConfig",
+          "description": "Optional. Cache configuration for the server.\n"
+        },
         "disable_studio_auth": {
           "type": "boolean",
           "description": "Optional. Whether to disable LangSmith API-key authentication for requests originating the Studio.\n\nDefaults to False, meaning that if a particular header is set, the server will verify the `x-api-key` header\nvalue is a valid API key for the deployment's workspace. If `True`, all requests will go through your custom\nauthentication logic, regardless of origin of the request.\n"
@@ -420,6 +446,29 @@
       },
       "required": []
     },
+    "CacheConfig": {
+      "title": "CacheConfig",
+      "type": "object",
+      "properties": {
+        "cache_keys": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Optional. List of header keys to use for caching.\n"
+        },
+        "max_size": {
+          "type": "integer",
+          "description": "Optional. Maximum size of the cache.\n"
+        },
+        "ttl_seconds": {
+          "type": "integer",
+          "description": "Optional. Time-to-live in seconds for cached items.\n"
+        }
+      },
+      "required": [],
+      "description": "dict() -> new empty dictionary\ndict(mapping) -> new dictionary initialized from a mapping object's\n    (key, value) pairs\ndict(iterable) -> new dictionary initialized as if via:\n    d = {}\n    for k, v in iterable:\n        d[k] = v\ndict(**kwargs) -> new dictionary initialized with the name=value pairs\n    in the keyword argument list.  For example:  dict(one=1, two=2)"
+    },
     "SecurityConfig": {
       "title": "SecurityConfig",
       "description": "Configuration for OpenAPI security definitions and requirements.\n\nUseful for specifying global or path-level authentication and authorization flows\n(e.g., OAuth2, API key headers, etc.).",
@@ -472,6 +521,17 @@
       "description": "Configuration for the built-in checkpointer, which handles checkpointing of state.\n\nIf omitted, no checkpointer is set up (the object store will still be present, however).",
       "type": "object",
       "properties": {
+        "serde": {
+          "anyOf": [
+            {
+              "$ref": "#/$defs/SerdeConfig"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Optional. Defines the serde configuration.\n\nIf provided, the checkpointer will apply serde settings according to the configuration.\nIf omitted, no serde behavior is configured.\n\nThis configuration requires server version 0.5 or later to take effect.\n"
+        },
         "ttl": {
           "anyOf": [
             {
@@ -486,6 +546,38 @@
       },
       "required": []
     },
+    "SerdeConfig": {
+      "title": "SerdeConfig",
+      "description": "Configuration for the built-in serde, which handles checkpointing of state.\n\nIf omitted, no serde is set up (the object store will still be present, however).",
+      "type": "object",
+      "properties": {
+        "allowed_json_modules": {
+          "anyOf": [
+            {
+              "type": "array",
+              "items": {
+                "type": "array",
+                "items": {
+                  "type": "string"
+                }
+              }
+            },
+            {
+              "type": "boolean"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Optional. List of allowed python modules to de-serialize custom objects from.\n\nIf provided, only the specified modules will be allowed to be deserialized.\nIf omitted, no modules are allowed, and the object returned will simply be a json object OR\na deserialized langchain object.\n"
+        },
+        "pickle_fallback": {
+          "type": "boolean",
+          "description": "Optional. Whether to allow pickling as a fallback for deserialization.\n\nIf True, pickling will be allowed as a fallback for deserialization.\nIf False, pickling will not be allowed as a fallback for deserialization.\nDefaults to True if not configured."
+        }
+      },
+      "required": []
+    },
     "ThreadTTLConfig": {
       "title": "ThreadTTLConfig",
       "description": "Configure a default TTL for checkpointed data within threads.",
@@ -522,6 +614,17 @@
       },
       "required": []
     },
+    "EncryptionConfig": {
+      "title": "EncryptionConfig",
+      "description": "Configuration for custom at-rest encryption logic.\n\n    Allows you to implement custom encryption for sensitive data stored in the database,\n    including metadata fields and checkpoint blobs.",
+      "type": "object",
+      "properties": {
+        "path": {
+          "type": "string"
+        }
+      },
+      "required": []
+    },
     "HttpConfig": {
       "title": "HttpConfig",
       "description": "Configuration for the built-in HTTP server that powers your deployment's routes and endpoints.",
@@ -553,13 +656,17 @@
           ],
           "description": "Optional. Defines CORS restrictions. If omitted, no special rules are set and\ncross-origin behavior depends on default server settings.\n"
         },
+        "disable_a2a": {
+          "type": "boolean",
+          "description": "Optional. If `True`, /a2a routes are removed, disabling default support to expose the deployment as an agent-to-agent (A2A) server.\n\nDefault is False.\n"
+        },
         "disable_assistants": {
           "type": "boolean",
           "description": "Optional. If `True`, /assistants routes are removed from the server.\n\nDefault is False (meaning /assistants is enabled).\n"
         },
         "disable_mcp": {
           "type": "boolean",
-          "description": "Optional. If `True`, /mcp routes are removed, disabling the MCP server.\n\nDefault is False.\n"
+          "description": "Optional. If `True`, /mcp routes are removed, disabling default support to expose the deployment as an MCP server.\n\nDefault is False.\n"
         },
         "disable_meta": {
           "type": "boolean",
@@ -577,6 +684,14 @@
           "type": "boolean",
           "description": "Optional. If `True`, /threads routes are removed.\n\nDefault is False.\n"
         },
+        "disable_ui": {
+          "type": "boolean",
+          "description": "Optional. If `True`, /ui routes are removed, disabling the UI server.\n\nDefault is False.\n"
+        },
+        "disable_webhooks": {
+          "type": "boolean",
+          "description": "Optional. If `True`, webhooks are disabled. Runs created with an associated webhook will\nstill be executed, but the webhook event will not be sent.\n\nDefault is False.\n"
+        },
         "enable_custom_route_auth": {
           "type": "boolean",
           "description": "Optional. If `True`, authentication is enabled for custom routes,\nnot just the routes that are protected by default.\n(Routes protected by default include /assistants, /threads, and /runs).\n\nDefault is False. This flag only affects authentication behavior\nif `app` is provided and contains custom routes.\n"
@@ -605,6 +720,10 @@
             }
           ],
           "description": "Optional. Defines the order in which to apply server customizations.\n"
+        },
+        "mount_prefix": {
+          "type": "string",
+          "description": "Optional. URL prefix to prepend to all the routes.\n"
         }
       },
       "required": []

libs/cli/schemas/schema.v0.json

@@ -99,6 +99,17 @@
               },
               "description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
             },
+            "encryption": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/EncryptionConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Custom at-rest encryption config, including the path to your Python encryption logic.\n\nAllows you to implement custom encryption for sensitive data stored in the database.\n"
+            },
             "env": {
               "anyOf": [
                 {
@@ -292,6 +303,17 @@
               },
               "description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
             },
+            "encryption": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/EncryptionConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Custom at-rest encryption config, including the path to your Python encryption logic.\n\nAllows you to implement custom encryption for sensitive data stored in the database.\n"
+            },
             "env": {
               "anyOf": [
                 {
@@ -405,6 +427,10 @@
       "description": "Configuration for custom authentication logic and how it integrates into the OpenAPI spec.",
       "type": "object",
       "properties": {
+        "cache": {
+          "$ref": "#/$defs/CacheConfig",
+          "description": "Optional. Cache configuration for the server.\n"
+        },
         "disable_studio_auth": {
           "type": "boolean",
           "description": "Optional. Whether to disable LangSmith API-key authentication for requests originating the Studio.\n\nDefaults to False, meaning that if a particular header is set, the server will verify the `x-api-key` header\nvalue is a valid API key for the deployment's workspace. If `True`, all requests will go through your custom\nauthentication logic, regardless of origin of the request.\n"
@@ -420,6 +446,29 @@
       },
       "required": []
     },
+    "CacheConfig": {
+      "title": "CacheConfig",
+      "type": "object",
+      "properties": {
+        "cache_keys": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Optional. List of header keys to use for caching.\n"
+        },
+        "max_size": {
+          "type": "integer",
+          "description": "Optional. Maximum size of the cache.\n"
+        },
+        "ttl_seconds": {
+          "type": "integer",
+          "description": "Optional. Time-to-live in seconds for cached items.\n"
+        }
+      },
+      "required": [],
+      "description": "dict() -> new empty dictionary\ndict(mapping) -> new dictionary initialized from a mapping object's\n    (key, value) pairs\ndict(iterable) -> new dictionary initialized as if via:\n    d = {}\n    for k, v in iterable:\n        d[k] = v\ndict(**kwargs) -> new dictionary initialized with the name=value pairs\n    in the keyword argument list.  For example:  dict(one=1, two=2)"
+    },
     "SecurityConfig": {
       "title": "SecurityConfig",
       "description": "Configuration for OpenAPI security definitions and requirements.\n\nUseful for specifying global or path-level authentication and authorization flows\n(e.g., OAuth2, API key headers, etc.).",
@@ -472,6 +521,17 @@
       "description": "Configuration for the built-in checkpointer, which handles checkpointing of state.\n\nIf omitted, no checkpointer is set up (the object store will still be present, however).",
       "type": "object",
       "properties": {
+        "serde": {
+          "anyOf": [
+            {
+              "$ref": "#/$defs/SerdeConfig"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Optional. Defines the serde configuration.\n\nIf provided, the checkpointer will apply serde settings according to the configuration.\nIf omitted, no serde behavior is configured.\n\nThis configuration requires server version 0.5 or later to take effect.\n"
+        },
         "ttl": {
           "anyOf": [
             {
@@ -486,6 +546,38 @@
       },
       "required": []
     },
+    "SerdeConfig": {
+      "title": "SerdeConfig",
+      "description": "Configuration for the built-in serde, which handles checkpointing of state.\n\nIf omitted, no serde is set up (the object store will still be present, however).",
+      "type": "object",
+      "properties": {
+        "allowed_json_modules": {
+          "anyOf": [
+            {
+              "type": "array",
+              "items": {
+                "type": "array",
+                "items": {
+                  "type": "string"
+                }
+              }
+            },
+            {
+              "type": "boolean"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Optional. List of allowed python modules to de-serialize custom objects from.\n\nIf provided, only the specified modules will be allowed to be deserialized.\nIf omitted, no modules are allowed, and the object returned will simply be a json object OR\na deserialized langchain object.\n"
+        },
+        "pickle_fallback": {
+          "type": "boolean",
+          "description": "Optional. Whether to allow pickling as a fallback for deserialization.\n\nIf True, pickling will be allowed as a fallback for deserialization.\nIf False, pickling will not be allowed as a fallback for deserialization.\nDefaults to True if not configured."
+        }
+      },
+      "required": []
+    },
     "ThreadTTLConfig": {
       "title": "ThreadTTLConfig",
       "description": "Configure a default TTL for checkpointed data within threads.",
@@ -522,6 +614,17 @@
       },
       "required": []
     },
+    "EncryptionConfig": {
+      "title": "EncryptionConfig",
+      "description": "Configuration for custom at-rest encryption logic.\n\n    Allows you to implement custom encryption for sensitive data stored in the database,\n    including metadata fields and checkpoint blobs.",
+      "type": "object",
+      "properties": {
+        "path": {
+          "type": "string"
+        }
+      },
+      "required": []
+    },
     "HttpConfig": {
       "title": "HttpConfig",
       "description": "Configuration for the built-in HTTP server that powers your deployment's routes and endpoints.",
@@ -553,13 +656,17 @@
           ],
           "description": "Optional. Defines CORS restrictions. If omitted, no special rules are set and\ncross-origin behavior depends on default server settings.\n"
         },
+        "disable_a2a": {
+          "type": "boolean",
+          "description": "Optional. If `True`, /a2a routes are removed, disabling default support to expose the deployment as an agent-to-agent (A2A) server.\n\nDefault is False.\n"
+        },
         "disable_assistants": {
           "type": "boolean",
           "description": "Optional. If `True`, /assistants routes are removed from the server.\n\nDefault is False (meaning /assistants is enabled).\n"
         },
         "disable_mcp": {
           "type": "boolean",
-          "description": "Optional. If `True`, /mcp routes are removed, disabling the MCP server.\n\nDefault is False.\n"
+          "description": "Optional. If `True`, /mcp routes are removed, disabling default support to expose the deployment as an MCP server.\n\nDefault is False.\n"
         },
         "disable_meta": {
           "type": "boolean",
@@ -577,6 +684,14 @@
           "type": "boolean",
           "description": "Optional. If `True`, /threads routes are removed.\n\nDefault is False.\n"
         },
+        "disable_ui": {
+          "type": "boolean",
+          "description": "Optional. If `True`, /ui routes are removed, disabling the UI server.\n\nDefault is False.\n"
+        },
+        "disable_webhooks": {
+          "type": "boolean",
+          "description": "Optional. If `True`, webhooks are disabled. Runs created with an associated webhook will\nstill be executed, but the webhook event will not be sent.\n\nDefault is False.\n"
+        },
         "enable_custom_route_auth": {
           "type": "boolean",
           "description": "Optional. If `True`, authentication is enabled for custom routes,\nnot just the routes that are protected by default.\n(Routes protected by default include /assistants, /threads, and /runs).\n\nDefault is False. This flag only affects authentication behavior\nif `app` is provided and contains custom routes.\n"
@@ -605,6 +720,10 @@
             }
           ],
           "description": "Optional. Defines the order in which to apply server customizations.\n"
+        },
+        "mount_prefix": {
+          "type": "string",
+          "description": "Optional. URL prefix to prepend to all the routes.\n"
         }
       },
       "required": []

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

@@ -141,6 +141,7 @@ services:
             additional_contexts:
                 - cli_1: {str(pathlib.Path(__file__).parent.parent.parent.parent.absolute())}
             dockerfile_inline: |
+                # syntax=docker/dockerfile:1.4
                 FROM langchain/langgraph-api:3.11
                 # -- Adding local package . --
                 ADD . /deps/cli

libs/cli/tests/unit_tests/test_config.py

@@ -48,6 +48,7 @@ def test_validate_config():
         "env": {},
         "store": None,
         "auth": None,
+        "encryption": None,
         "checkpointer": None,
         "http": None,
         "ui": None,
@@ -74,6 +75,7 @@ def test_validate_config():
         "env": env,
         "store": None,
         "auth": None,
+        "encryption": None,
         "checkpointer": None,
         "http": None,
         "ui": None,
@@ -419,6 +421,7 @@ def test_config_to_docker_simple():
         "langchain/langgraph-api",
     )
     expected_docker_stdin = f"""\
+# syntax=docker/dockerfile:1.4
 FROM langchain/langgraph-api:3.11
 # -- Installing local requirements --
 COPY --from=outer-requirements.txt requirements.txt /deps/outer-graphs_reqs_a/graphs_reqs_a/requirements.txt
@@ -482,6 +485,7 @@ def test_config_to_docker_outside_path():
     )
     expected_docker_stdin = (
         """\
+# syntax=docker/dockerfile:1.4
 FROM langchain/langgraph-api:3.11
 # -- Adding non-package dependency unit_tests --
 ADD . /deps/outer-unit_tests/unit_tests
@@ -746,6 +750,57 @@ RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not foun
     assert additional_contexts == {}
 
 
+def test_config_to_docker_python_encryption():
+    # Test that encryption config is included in validation
+    graphs = {"agent": "./agent.py:graph"}
+    validated = validate_config(
+        {
+            "python_version": "3.11",
+            "graphs": graphs,
+            "dependencies": ["."],
+            "encryption": {"path": "./encryption.py:encryption"},
+        }
+    )
+
+    # Verify that encryption config is preserved after validation
+    assert validated.get("encryption") is not None
+    assert validated["encryption"]["path"] == "./encryption.py:encryption"
+
+
+def test_config_to_docker_python_encryption_bad_path():
+    # Test that invalid encryption path format raises ValueError
+    graphs = {"agent": "./agent.py:graph"}
+    with pytest.raises(ValueError, match="Invalid encryption.path format"):
+        validate_config(
+            {
+                "python_version": "3.11",
+                "graphs": graphs,
+                "dependencies": ["."],
+                "encryption": {"path": "./encryption.py"},  # Missing :attribute
+            }
+        )
+
+
+def test_config_to_docker_python_encryption_formatted():
+    # Test that encryption config is properly formatted in Docker output
+    graphs = {"agent": "./graphs/agent.py:graph"}
+    actual_docker_stdin, additional_contexts = config_to_docker(
+        PATH_TO_CONFIG,
+        validate_config(
+            {
+                "python_version": "3.11",
+                "dependencies": ["."],
+                "graphs": graphs,
+                "encryption": {"path": "./agent.py:my_encryption"},
+            }
+        ),
+        "langchain/langgraph-api",
+    )
+    # Verify that LANGGRAPH_ENCRYPTION is in the docker output with the correct path
+    assert "LANGGRAPH_ENCRYPTION=" in actual_docker_stdin
+    assert "/deps/outer-unit_tests/unit_tests/agent.py:my_encryption" in actual_docker_stdin
+
+
 def test_config_to_docker_nodejs_internal_docker_tag():
     graphs = {"agent": "./graphs/agent.js:graph"}
     actual_docker_stdin, additional_contexts = config_to_docker(

libs/langgraph/.claude/settings.local.json

@@ -1,16 +0,0 @@
-{
-  "permissions": {
-    "allow": [
-      "Bash(rg:*)",
-      "Bash(python:*)",
-      "Bash(grep:*)",
-      "Bash(sed:*)",
-      "Bash(awk:*)",
-      "Bash(uv run mypy:*)",
-      "Bash(uv run:*)",
-      "Bash(make test:*)",
-      "Bash(make test_parallel:*)"
-    ],
-    "deny": []
-  }
-}

libs/langgraph/README.md

@@ -67,7 +67,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 - [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
 
 > [!NOTE]
-> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
+> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).
 
 ## Additional resources
 

libs/langgraph/langgraph/channels/base.py

@@ -39,14 +39,19 @@ class BaseChannel(Generic[Value, Update, Checkpoint], ABC):
 
     def copy(self) -> Self:
         """Return a copy of the channel.
+
         By default, delegates to `checkpoint()` and `from_checkpoint()`.
-        Subclasses can override this method with a more efficient implementation."""
+
+        Subclasses can override this method with a more efficient implementation.
+        """
         return self.from_checkpoint(self.checkpoint())
 
     def checkpoint(self) -> Checkpoint | Any:
         """Return a serializable representation of the channel's current state.
+
         Raises `EmptyChannelError` if the channel is empty (never updated yet),
-        or doesn't support checkpoints."""
+        or doesn't support checkpoints.
+        """
         try:
             return self.get()
         except EmptyChannelError:
@@ -55,7 +60,9 @@ class BaseChannel(Generic[Value, Update, Checkpoint], ABC):
     @abstractmethod
     def from_checkpoint(self, checkpoint: Checkpoint | Any) -> Self:
         """Return a new identical channel, optionally initialized from a checkpoint.
-        If the checkpoint contains complex data structures, they should be copied."""
+
+        If the checkpoint contains complex data structures, they should be copied.
+        """
 
     # read methods
 
@@ -67,6 +74,7 @@ class BaseChannel(Generic[Value, Update, Checkpoint], ABC):
 
     def is_available(self) -> bool:
         """Return `True` if the channel is available (not empty), `False` otherwise.
+
         Subclasses should override this method to provide a more efficient
         implementation than calling `get()` and catching `EmptyChannelError`.
         """
@@ -83,21 +91,29 @@ class BaseChannel(Generic[Value, Update, Checkpoint], ABC):
         """Update the channel's value with the given sequence of updates.
         The order of the updates in the sequence is arbitrary.
         This method is called by Pregel for all channels at the end of each step.
+
         If there are no updates, it is called with an empty sequence.
+
         Raises `InvalidUpdateError` if the sequence of updates is invalid.
+
         Returns `True` if the channel was updated, `False` otherwise."""
 
     def consume(self) -> bool:
-        """Notify the channel that a subscribed task ran. By default, no-op.
-        A channel can use this method to modify its state, preventing the value
-        from being consumed again.
+        """Notify the channel that a subscribed task ran.
+
+        By default, no-op.
+
+        A channel can use this method to modify its state, preventing the value from being consumed again.
 
         Returns `True` if the channel was updated, `False` otherwise.
         """
         return False
 
     def finish(self) -> bool:
-        """Notify the channel that the Pregel run is finishing. By default, no-op.
+        """Notify the channel that the Pregel run is finishing.
+
+        By default, no-op.
+
         A channel can use this method to modify its state, preventing finish.
 
         Returns `True` if the channel was updated, `False` otherwise.

libs/langgraph/langgraph/config.py

@@ -32,8 +32,8 @@ def get_config() -> RunnableConfig:
 def get_store() -> BaseStore:
     """Access LangGraph store from inside a graph node or entrypoint task at runtime.
 
-    Can be called from inside any [StateGraph][langgraph.graph.StateGraph] node or
-    functional API [task][langgraph.func.task], as long as the StateGraph or the [entrypoint][langgraph.func.entrypoint]
+    Can be called from inside any [`StateGraph`][langgraph.graph.StateGraph] node or
+    functional API [`task`][langgraph.func.task], as long as the `StateGraph` or the [`entrypoint`][langgraph.func.entrypoint]
     was initialized with a store, e.g.:
 
     ```python
@@ -53,10 +53,10 @@ def get_store() -> BaseStore:
     !!! warning "Async with Python < 3.11"
 
         If you are using Python < 3.11 and are running LangGraph asynchronously,
-        `get_store()` won't work since it uses [contextvar](https://docs.python.org/3/library/contextvars.html) propagation (only available in [Python >= 3.11](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task)).
+        `get_store()` won't work since it uses [`contextvar`](https://docs.python.org/3/library/contextvars.html) propagation (only available in [Python >= 3.11](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task)).
 
 
-    Example: Using with StateGraph
+    Example: Using with `StateGraph`
         ```python
         from typing_extensions import TypedDict
         from langgraph.graph import StateGraph, START
@@ -124,17 +124,17 @@ def get_store() -> BaseStore:
 
 
 def get_stream_writer() -> StreamWriter:
-    """Access LangGraph [StreamWriter][langgraph.types.StreamWriter] from inside a graph node or entrypoint task at runtime.
+    """Access LangGraph [`StreamWriter`][langgraph.types.StreamWriter] from inside a graph node or entrypoint task at runtime.
 
-    Can be called from inside any [StateGraph][langgraph.graph.StateGraph] node or
-    functional API [task][langgraph.func.task].
+    Can be called from inside any [`StateGraph`][langgraph.graph.StateGraph] node or
+    functional API [`task`][langgraph.func.task].
 
     !!! warning "Async with Python < 3.11"
 
         If you are using Python < 3.11 and are running LangGraph asynchronously,
-        `get_stream_writer()` won't work since it uses [contextvar](https://docs.python.org/3/library/contextvars.html) propagation (only available in [Python >= 3.11](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task)).
+        `get_stream_writer()` won't work since it uses [`contextvar`](https://docs.python.org/3/library/contextvars.html) propagation (only available in [Python >= 3.11](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task)).
 
-    Example: Using with StateGraph
+    Example: Using with `StateGraph`
         ```python
         from typing_extensions import TypedDict
         from langgraph.graph import StateGraph, START

libs/langgraph/langgraph/func/__init__.py

@@ -151,13 +151,13 @@ def task(
 
 
         @task
-        def add_one(a: int) -> int:
+        def add_one_task(a: int) -> int:
             return a + 1
 
 
         @entrypoint()
         def add_one(numbers: list[int]) -> list[int]:
-            futures = [add_one(n) for n in numbers]
+            futures = [add_one_task(n) for n in numbers]
             results = [f.result() for f in futures]
             return results
 
@@ -173,13 +173,13 @@ def task(
 
 
         @task
-        async def add_one(a: int) -> int:
+        async def add_one_task(a: int) -> int:
             return a + 1
 
 
         @entrypoint()
         async def add_one(numbers: list[int]) -> list[int]:
-            futures = [add_one(n) for n in numbers]
+            futures = [add_one_task(n) for n in numbers]
             return asyncio.gather(*futures)
 
 
@@ -357,7 +357,7 @@ class entrypoint(Generic[ContextT]):
         my_workflow.invoke("hello", config)
         ```
 
-    Example: Using entrypoint.final to save a value
+    Example: Using `entrypoint.final` to save a value
         The `entrypoint.final` object allows you to return a value while saving
         a different value to the checkpoint. This value will be accessible
         in the next invocation of the entrypoint via the `previous` parameter, as

libs/langgraph/langgraph/graph/message.py

@@ -30,6 +30,7 @@ __all__ = (
     "add_messages",
     "MessagesState",
     "MessageGraph",
+    "REMOVE_ALL_MESSAGES",
 )
 
 Messages = list[MessageLikeRepresentation] | MessageLikeRepresentation
@@ -87,8 +88,8 @@ def add_messages(
         If a message in `right` has the same ID as a message in `left`, the
             message from `right` will replace the message from `left`.
 
-    Example:
-        ```python title="Basic usage"
+    Example: Basic usage
+        ```python
         from langchain_core.messages import AIMessage, HumanMessage
 
         msgs1 = [HumanMessage(content="Hello", id="1")]
@@ -97,14 +98,16 @@ def add_messages(
         # [HumanMessage(content='Hello', id='1'), AIMessage(content='Hi there!', id='2')]
         ```
 
-        ```python title="Overwrite existing message"
+    Example: Overwrite existing message
+        ```python
         msgs1 = [HumanMessage(content="Hello", id="1")]
         msgs2 = [HumanMessage(content="Hello again", id="1")]
         add_messages(msgs1, msgs2)
         # [HumanMessage(content='Hello again', id='1')]
         ```
 
-        ```python title="Use in a StateGraph"
+    Example: Use in a StateGraph
+        ```python
         from typing import Annotated
         from typing_extensions import TypedDict
         from langgraph.graph import StateGraph
@@ -123,7 +126,8 @@ def add_messages(
         # {'messages': [AIMessage(content='Hello', id=...)]}
         ```
 
-        ```python title="Use OpenAI message format"
+    Example: Use OpenAI message format
+        ```python
         from typing import Annotated
         from typing_extensions import TypedDict
         from langgraph.graph import StateGraph, add_messages

libs/langgraph/langgraph/graph/state.py

@@ -110,15 +110,24 @@ def _get_node_name(node: StateNode[Any, ContextT]) -> str:
 
 class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
     """A graph whose nodes communicate by reading and writing to a shared state.
-    The signature of each node is State -> Partial<State>.
+
+    The signature of each node is `State -> Partial<State>`.
 
     Each state key can optionally be annotated with a reducer function that
     will be used to aggregate the values of that key received from multiple nodes.
     The signature of a reducer function is `(Value, Value) -> Value`.
 
+    !!! warning
+
+        `StateGraph` is a builder class and cannot be used directly for execution.
+        You must first call `.compile()` to create an executable graph that supports
+        methods like `invoke()`, `stream()`, `astream()`, and `ainvoke()`. See the
+        `CompiledStateGraph` documentation for more details.
+
     Args:
         state_schema: The schema class that defines the state.
         context_schema: The schema class that defines the runtime context.
+
             Use this to expose immutable context data to your nodes, like `user_id`, `db_conn`, etc.
         input_schema: The schema class that defines the input to the graph.
         output_schema: The schema class that defines the output from the graph.
@@ -289,7 +298,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the state graph, input schema is inferred as the state schema.
+        """Add a new node to the `StateGraph`, input schema is inferred as the state schema.
         Will take the name of the function/runnable as the node name.
         """
         ...
@@ -307,7 +316,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the state graph, input schema is specified.
+        """Add a new node to the `StateGraph`, input schema is specified.
         Will take the name of the function/runnable as the node name.
         """
         ...
@@ -326,7 +335,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the state graph, input schema is inferred as the state schema."""
+        """Add a new node to the `StateGraph`, input schema is inferred as the state schema."""
         ...
 
     @overload
@@ -343,7 +352,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the state graph, input schema is specified."""
+        """Add a new node to the `StateGraph`, input schema is specified."""
         ...
 
     def add_node(
@@ -359,22 +368,27 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the state graph.
+        """Add a new node to the `StateGraph`.
 
         Args:
             node: The function or runnable this node will run.
+
                 If a string is provided, it will be used as the node name, and action will be used as the function or runnable.
             action: The action associated with the node.
                 Will be used as the node function or runnable if `node` is a string (node name).
             defer: Whether to defer the execution of the node until the run is about to end.
             metadata: The metadata associated with the node.
-            input_schema: The input schema for the node. (default: the graph's state schema)
+            input_schema: The input schema for the node. (Default: the graph's state schema)
             retry_policy: The retry policy for the node.
+
                 If a sequence is provided, the first matching policy will be applied.
             cache_policy: The cache policy for the node.
             destinations: Destinations that indicate where a node can route to.
-                This is useful for edgeless graphs with nodes that return `Command` objects.
+
+                Useful for edgeless graphs with nodes that return `Command` objects.
+
                 If a `dict` is provided, the keys will be used as the target node names and the values will be used as the labels for the edges.
+
                 If a `tuple` is provided, the values will be used as the target node names.
 
                 !!! note
@@ -416,7 +430,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             ```
 
         Returns:
-            Self: The instance of the state graph, allowing for method chaining.
+            Self: The instance of the `StateGraph`, allowing for method chaining.
         """
         if (retry := kwargs.get("retry", MISSING)) is not MISSING:
             warnings.warn(
@@ -571,7 +585,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             ValueError: If the start key is `'END'` or if the start key or end key is not present in the graph.
 
         Returns:
-            Self: The instance of the state graph, allowing for method chaining.
+            Self: The instance of the `StateGraph`, allowing for method chaining.
         """
         if self.compiled:
             logger.warning(
@@ -623,11 +637,14 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         Args:
             source: The starting node. This conditional edge will run when
                 exiting this node.
-            path: The callable that determines the next
-                node or nodes. If not specifying `path_map` it should return one or
-                more nodes. If it returns `'END'`, the graph will stop execution.
-            path_map: Optional mapping of paths to node
-                names. If omitted the paths returned by `path` should be node names.
+            path: The callable that determines the next node or nodes.
+
+                If not specifying `path_map` it should return one or more nodes.
+
+                If it returns `'END'`, the graph will stop execution.
+            path_map: Optional mapping of paths to node names.
+
+                If omitted the paths returned by `path` should be node names.
 
         Returns:
             Self: The instance of the graph, allowing for method chaining.
@@ -668,7 +685,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
 
         Args:
             nodes: A sequence of `StateNode` (callables that accept a `state` arg) or `(name, StateNode)` tuples.
+
                 If no names are provided, the name will be inferred from the node object (e.g. a `Runnable` or a `Callable` name).
+
                 Each node will be executed in the order provided.
 
         Raises:
@@ -676,7 +695,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             ValueError: If the sequence contains duplicate node names.
 
         Returns:
-            Self: The instance of the state graph, allowing for method chaining.
+            Self: The instance of the `StateGraph`, allowing for method chaining.
         """
         if len(nodes) < 1:
             raise ValueError("Sequence requires at least one node.")
@@ -725,11 +744,14 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         """Sets a conditional entry point in the graph.
 
         Args:
-            path: The callable that determines the next
-                node or nodes. If not specifying `path_map` it should return one or
-                more nodes. If it returns END, the graph will stop execution.
-            path_map: Optional mapping of paths to node
-                names. If omitted the paths returned by `path` should be node names.
+            path: The callable that determines the next node or nodes.
+
+                If not specifying `path_map` it should return one or more nodes.
+
+                If it returns END, the graph will stop execution.
+            path_map: Optional mapping of paths to node names.
+
+                If omitted the paths returned by `path` should be node names.
 
         Returns:
             Self: The instance of the graph, allowing for method chaining.
@@ -809,16 +831,19 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         debug: bool = False,
         name: str | None = None,
     ) -> CompiledStateGraph[StateT, ContextT, InputT, OutputT]:
-        """Compiles the state graph into a `CompiledStateGraph` object.
+        """Compiles the `StateGraph` into a `CompiledStateGraph` object.
 
         The compiled graph implements the `Runnable` interface and can be invoked,
         streamed, batched, and run asynchronously.
 
         Args:
             checkpointer: A checkpoint saver object or flag.
+
                 If provided, this `Checkpointer` serves as a fully versioned "short-term memory" for the graph,
                 allowing it to be paused, resumed, and replayed from any point.
+
                 If `None`, it may inherit the parent graph's checkpointer when used as a subgraph.
+
                 If `False`, it will not use or inherit any checkpointer.
             interrupt_before: An optional list of node names to interrupt before.
             interrupt_after: An optional list of node names to interrupt after.
@@ -826,7 +851,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             name: The name to use for the compiled graph.
 
         Returns:
-            CompiledStateGraph: The compiled state graph.
+            CompiledStateGraph: The compiled `StateGraph`.
         """
         # assign default values
         interrupt_before = interrupt_before or []

libs/langgraph/langgraph/graph/ui.py

@@ -81,9 +81,8 @@ def push_ui_message(
         metadata: Optional additional metadata about the UI message.
         message: Optional message object to associate with the UI message.
         state_key: Key in the graph state where the UI messages are stored.
-            Defaults to "ui".
         merge: Whether to merge props with existing UI message (True) or replace
-            them (False). Defaults to False.
+            them (False).
 
     Returns:
         The created UI message.

libs/langgraph/langgraph/pregel/_algo.py

@@ -258,9 +258,11 @@ def apply_writes(
         next_version = None
     else:
         next_version = get_next_version(
-            max(checkpoint["channel_versions"].values())
-            if checkpoint["channel_versions"]
-            else None,
+            (
+                max(checkpoint["channel_versions"].values())
+                if checkpoint["channel_versions"]
+                else None
+            ),
             None,
         )
 
@@ -491,6 +493,11 @@ def prepare_next_tasks(
 PUSH_TRIGGER = (PUSH,)
 
 
+class _TaskIDFn(Protocol):
+    def __call__(self, namespace: bytes, *parts: str | bytes) -> str:
+        pass
+
+
 def prepare_single_task(
     task_path: tuple[Any, ...],
     task_id_checksum: str | None,
@@ -520,250 +527,50 @@ def prepare_single_task(
     task_id_func = _xxhash_str if checkpoint["v"] > 1 else _uuid5_str
 
     if task_path[0] == PUSH and isinstance(task_path[-1], Call):
-        # (PUSH, parent task path, idx of PUSH write, id of parent task, Call)
-        task_path_t = cast(tuple[str, tuple, int, str, Call], task_path)
-        call = task_path_t[-1]
-        proc_ = get_runnable_for_task(call.func)
-        name = proc_.name
-        if name is None:
-            raise ValueError("`call` functions must have a `__name__` attribute")
-        # create task id
-        triggers: Sequence[str] = PUSH_TRIGGER
-        checkpoint_ns = f"{parent_ns}{NS_SEP}{name}" if parent_ns else name
-        task_id = task_id_func(
-            checkpoint_id_bytes,
-            checkpoint_ns,
-            str(step),
-            name,
-            PUSH,
-            task_path_str(task_path[1]),
-            str(task_path[2]),
+        return prepare_push_task_functional(
+            cast(tuple[str, tuple, int, str, Call], task_path),
+            task_id_checksum,
+            checkpoint=checkpoint,
+            checkpoint_id_bytes=checkpoint_id_bytes,
+            pending_writes=pending_writes,
+            channels=channels,
+            managed=managed,
+            config=config,
+            step=step,
+            stop=stop,
+            for_execution=for_execution,
+            store=store,
+            checkpointer=checkpointer,
+            manager=manager,
+            cache_policy=cache_policy,
+            retry_policy=retry_policy,
+            parent_ns=parent_ns,
+            task_id_func=task_id_func,
+        )
+
+    elif task_path[0] == PUSH:
+        return prepare_push_task_send(
+            cast(tuple[str, tuple], task_path),
+            task_id_checksum,
+            checkpoint=checkpoint,
+            checkpoint_id_bytes=checkpoint_id_bytes,
+            pending_writes=pending_writes,
+            channels=channels,
+            managed=managed,
+            config=config,
+            step=step,
+            processes=processes,
+            stop=stop,
+            for_execution=for_execution,
+            store=store,
+            checkpointer=checkpointer,
+            manager=manager,
+            cache_policy=cache_policy,
+            retry_policy=retry_policy,
+            parent_ns=parent_ns,
+            task_id_func=task_id_func,
         )
-        task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
-        # we append True to the task path to indicate that a call is being
-        # made, so we should not return interrupts from this task (responsibility lies with the parent)
-        task_path = (*task_path[:3], True)
-        metadata = {
-            "langgraph_step": step,
-            "langgraph_node": name,
-            "langgraph_triggers": triggers,
-            "langgraph_path": task_path,
-            "langgraph_checkpoint_ns": task_checkpoint_ns,
-        }
-        if task_id_checksum is not None:
-            assert task_id == task_id_checksum, f"{task_id} != {task_id_checksum}"
-        if for_execution:
-            writes: deque[tuple[str, Any]] = deque()
-            cache_policy = call.cache_policy or cache_policy
-            if cache_policy:
-                args_key = cache_policy.key_func(*call.input[0], **call.input[1])
-                cache_key: CacheKey | None = CacheKey(
-                    (
-                        CACHE_NS_WRITES,
-                        (identifier(call.func) or "__dynamic__"),
-                    ),
-                    xxh3_128_hexdigest(
-                        args_key.encode() if isinstance(args_key, str) else args_key,
-                    ),
-                    cache_policy.ttl,
-                )
-            else:
-                cache_key = None
-            scratchpad = _scratchpad(
-                config[CONF].get(CONFIG_KEY_SCRATCHPAD),
-                pending_writes,
-                task_id,
-                xxh3_128_hexdigest(task_checkpoint_ns.encode()),
-                config[CONF].get(CONFIG_KEY_RESUME_MAP),
-                step,
-                stop,
-            )
-            runtime = cast(
-                Runtime, configurable.get(CONFIG_KEY_RUNTIME, DEFAULT_RUNTIME)
-            )
-            runtime = runtime.override(store=store)
-            return PregelExecutableTask(
-                name,
-                call.input,
-                proc_,
-                writes,
-                patch_config(
-                    merge_configs(config, {"metadata": metadata}),
-                    run_name=name,
-                    callbacks=call.callbacks
-                    or (manager.get_child(f"graph:step:{step}") if manager else None),
-                    configurable={
-                        CONFIG_KEY_TASK_ID: task_id,
-                        # deque.extend is thread-safe
-                        CONFIG_KEY_SEND: writes.extend,
-                        CONFIG_KEY_READ: partial(
-                            local_read,
-                            scratchpad,
-                            channels,
-                            managed,
-                            PregelTaskWrites(task_path, name, writes, triggers),
-                        ),
-                        CONFIG_KEY_CHECKPOINTER: (
-                            checkpointer or configurable.get(CONFIG_KEY_CHECKPOINTER)
-                        ),
-                        CONFIG_KEY_CHECKPOINT_MAP: {
-                            **configurable.get(CONFIG_KEY_CHECKPOINT_MAP, {}),
-                            parent_ns: checkpoint["id"],
-                        },
-                        CONFIG_KEY_CHECKPOINT_ID: None,
-                        CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
-                        CONFIG_KEY_SCRATCHPAD: scratchpad,
-                        CONFIG_KEY_RUNTIME: runtime,
-                    },
-                ),
-                triggers,
-                call.retry_policy or retry_policy,
-                cache_key,
-                task_id,
-                task_path,
-            )
-        else:
-            return PregelTask(task_id, name, task_path)
-    elif task_path[0] == PUSH:
-        if len(task_path) == 2:
-            # SEND tasks, executed in superstep n+1
-            # (PUSH, idx of pending send)
-            idx = cast(int, task_path[1])
-            if not channels[TASKS].is_available():
-                return
-            sends: Sequence[Send] = channels[TASKS].get()
-            if idx < 0 or idx >= len(sends):
-                return
-            packet = sends[idx]
-            if not isinstance(packet, Send):
-                logger.warning(
-                    f"Ignoring invalid packet type {type(packet)} in pending sends"
-                )
-                return
 
-            if packet.node not in processes:
-                logger.warning(
-                    f"Ignoring unknown node name {packet.node} in pending sends"
-                )
-                return
-            # find process
-            proc = processes[packet.node]
-            proc_node = proc.node
-            if proc_node is None:
-                return
-            # create task id
-            triggers = PUSH_TRIGGER
-            checkpoint_ns = (
-                f"{parent_ns}{NS_SEP}{packet.node}" if parent_ns else packet.node
-            )
-            task_id = task_id_func(
-                checkpoint_id_bytes,
-                checkpoint_ns,
-                str(step),
-                packet.node,
-                PUSH,
-                str(idx),
-            )
-        else:
-            logger.warning(f"Ignoring invalid PUSH task path {task_path}")
-            return
-        task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
-        # we append False to the task path to indicate that a call is not being made
-        # so we should return interrupts from this task
-        task_path = (*task_path[:3], False)
-        metadata = {
-            "langgraph_step": step,
-            "langgraph_node": packet.node,
-            "langgraph_triggers": triggers,
-            "langgraph_path": task_path,
-            "langgraph_checkpoint_ns": task_checkpoint_ns,
-        }
-        if task_id_checksum is not None:
-            assert task_id == task_id_checksum, f"{task_id} != {task_id_checksum}"
-        if for_execution:
-            if proc.metadata:
-                metadata.update(proc.metadata)
-            writes = deque()
-            cache_policy = proc.cache_policy or cache_policy
-            if cache_policy:
-                args_key = cache_policy.key_func(packet.arg)
-                cache_key = CacheKey(
-                    (
-                        CACHE_NS_WRITES,
-                        (identifier(proc) or "__dynamic__"),
-                        packet.node,
-                    ),
-                    xxh3_128_hexdigest(
-                        args_key.encode() if isinstance(args_key, str) else args_key,
-                    ),
-                    cache_policy.ttl,
-                )
-            else:
-                cache_key = None
-            scratchpad = _scratchpad(
-                config[CONF].get(CONFIG_KEY_SCRATCHPAD),
-                pending_writes,
-                task_id,
-                xxh3_128_hexdigest(task_checkpoint_ns.encode()),
-                config[CONF].get(CONFIG_KEY_RESUME_MAP),
-                step,
-                stop,
-            )
-            runtime = cast(
-                Runtime, configurable.get(CONFIG_KEY_RUNTIME, DEFAULT_RUNTIME)
-            )
-            runtime = runtime.override(
-                store=store, previous=checkpoint["channel_values"].get(PREVIOUS, None)
-            )
-            additional_config: RunnableConfig = {
-                "metadata": metadata,
-                "tags": proc.tags,
-            }
-            return PregelExecutableTask(
-                packet.node,
-                packet.arg,
-                proc_node,
-                writes,
-                patch_config(
-                    merge_configs(config, additional_config),
-                    run_name=packet.node,
-                    callbacks=(
-                        manager.get_child(f"graph:step:{step}") if manager else None
-                    ),
-                    configurable={
-                        CONFIG_KEY_TASK_ID: task_id,
-                        # deque.extend is thread-safe
-                        CONFIG_KEY_SEND: writes.extend,
-                        CONFIG_KEY_READ: partial(
-                            local_read,
-                            scratchpad,
-                            channels,
-                            managed,
-                            PregelTaskWrites(task_path, packet.node, writes, triggers),
-                        ),
-                        CONFIG_KEY_CHECKPOINTER: (
-                            checkpointer or configurable.get(CONFIG_KEY_CHECKPOINTER)
-                        ),
-                        CONFIG_KEY_CHECKPOINT_MAP: {
-                            **configurable.get(CONFIG_KEY_CHECKPOINT_MAP, {}),
-                            parent_ns: checkpoint["id"],
-                        },
-                        CONFIG_KEY_CHECKPOINT_ID: None,
-                        CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
-                        CONFIG_KEY_SCRATCHPAD: scratchpad,
-                        CONFIG_KEY_RUNTIME: runtime,
-                    },
-                ),
-                triggers,
-                proc.retry_policy or retry_policy,
-                cache_key,
-                task_id,
-                task_path,
-                writers=proc.flat_writers,
-                subgraphs=proc.subgraphs,
-            )
-        else:
-            return PregelTask(task_id, packet.node, task_path)
     elif task_path[0] == PULL:
         # (PULL, node name)
         name = cast(str, task_path[1])
@@ -834,7 +641,7 @@ def prepare_single_task(
                 if node := proc.node:
                     if proc.metadata:
                         metadata.update(proc.metadata)
-                    writes = deque()
+                    writes: deque[tuple[str, Any]] = deque()
                     cache_policy = proc.cache_policy or cache_policy
                     if cache_policy:
                         args_key = cache_policy.key_func(val)
@@ -845,9 +652,11 @@ def prepare_single_task(
                                 name,
                             ),
                             xxh3_128_hexdigest(
-                                args_key.encode()
-                                if isinstance(args_key, str)
-                                else args_key,
+                                (
+                                    args_key.encode()
+                                    if isinstance(args_key, str)
+                                    else args_key
+                                ),
                             ),
                             cache_policy.ttl,
                         )
@@ -870,7 +679,9 @@ def prepare_single_task(
                         node,
                         writes,
                         patch_config(
-                            merge_configs(config, additional_config),
+                            merge_configs(
+                                config, cast(RunnableConfig, additional_config)
+                            ),
                             run_name=name,
                             callbacks=(
                                 manager.get_child(f"graph:step:{step}")
@@ -919,6 +730,297 @@ def prepare_single_task(
                 return PregelTask(task_id, name, task_path[:3])
 
 
+def prepare_push_task_functional(
+    task_path: tuple[str, tuple, int, str, Call],
+    # (PUSH, parent task path, idx of PUSH write, id of parent task, Call)
+    task_id_checksum: str | None,
+    *,
+    checkpoint: Checkpoint,
+    checkpoint_id_bytes: bytes,
+    pending_writes: list[PendingWrite],
+    channels: Mapping[str, BaseChannel],
+    managed: ManagedValueMapping,
+    config: RunnableConfig,
+    step: int,
+    stop: int,
+    for_execution: bool,
+    store: BaseStore | None = None,
+    checkpointer: BaseCheckpointSaver | None = None,
+    manager: None | ParentRunManager | AsyncParentRunManager = None,
+    cache_policy: CachePolicy | None = None,
+    retry_policy: Sequence[RetryPolicy] = (),
+    parent_ns: str,
+    # namespace: bytes, *parts: str | bytes
+    task_id_func: _TaskIDFn,
+) -> PregelTask | PregelExecutableTask:
+    """Prepare a push task with an attached caller. Used for the functional API."""
+    configurable = config.get(CONF, {})
+
+    call = task_path[-1]
+    proc_ = get_runnable_for_task(call.func)
+    name = proc_.name
+    if name is None:
+        raise ValueError("`call` functions must have a `__name__` attribute")
+    # create task id
+    triggers: Sequence[str] = PUSH_TRIGGER
+    checkpoint_ns = f"{parent_ns}{NS_SEP}{name}" if parent_ns else name
+    task_id = task_id_func(
+        checkpoint_id_bytes,
+        checkpoint_ns,
+        str(step),
+        name,
+        PUSH,
+        task_path_str(task_path[1]),
+        str(task_path[2]),
+    )
+    task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
+    # we append True to the task path to indicate that a call is being
+    # made, so we should not return interrupts from this task (responsibility lies with the parent)
+    in_progress_task_path = (*task_path[:3], True)
+    metadata = {
+        "langgraph_step": step,
+        "langgraph_node": name,
+        "langgraph_triggers": triggers,
+        "langgraph_path": in_progress_task_path,
+        "langgraph_checkpoint_ns": task_checkpoint_ns,
+    }
+    if task_id_checksum is not None:
+        assert task_id == task_id_checksum, f"{task_id} != {task_id_checksum}"
+    if for_execution:
+        writes: deque[tuple[str, Any]] = deque()
+        cache_policy = call.cache_policy or cache_policy
+        if cache_policy:
+            args_key = cache_policy.key_func(*call.input[0], **call.input[1])
+            cache_key: CacheKey | None = CacheKey(
+                (
+                    CACHE_NS_WRITES,
+                    (identifier(call.func) or "__dynamic__"),
+                ),
+                xxh3_128_hexdigest(
+                    args_key.encode() if isinstance(args_key, str) else args_key,
+                ),
+                cache_policy.ttl,
+            )
+        else:
+            cache_key = None
+        scratchpad = _scratchpad(
+            configurable.get(CONFIG_KEY_SCRATCHPAD),
+            pending_writes,
+            task_id,
+            xxh3_128_hexdigest(task_checkpoint_ns.encode()),
+            configurable.get(CONFIG_KEY_RESUME_MAP),
+            step,
+            stop,
+        )
+        runtime = cast(Runtime, configurable.get(CONFIG_KEY_RUNTIME, DEFAULT_RUNTIME))
+        runtime = runtime.override(store=store)
+        return PregelExecutableTask(
+            name,
+            call.input,
+            proc_,
+            writes,
+            patch_config(
+                merge_configs(config, {"metadata": metadata}),
+                run_name=name,
+                callbacks=call.callbacks
+                or (manager.get_child(f"graph:step:{step}") if manager else None),
+                configurable={
+                    CONFIG_KEY_TASK_ID: task_id,
+                    # deque.extend is thread-safe
+                    CONFIG_KEY_SEND: writes.extend,
+                    CONFIG_KEY_READ: partial(
+                        local_read,
+                        scratchpad,
+                        channels,
+                        managed,
+                        PregelTaskWrites(in_progress_task_path, name, writes, triggers),
+                    ),
+                    CONFIG_KEY_CHECKPOINTER: (
+                        checkpointer or configurable.get(CONFIG_KEY_CHECKPOINTER)
+                    ),
+                    CONFIG_KEY_CHECKPOINT_MAP: {
+                        **configurable.get(CONFIG_KEY_CHECKPOINT_MAP, {}),
+                        parent_ns: checkpoint["id"],
+                    },
+                    CONFIG_KEY_CHECKPOINT_ID: None,
+                    CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
+                    CONFIG_KEY_SCRATCHPAD: scratchpad,
+                    CONFIG_KEY_RUNTIME: runtime,
+                },
+            ),
+            triggers,
+            call.retry_policy or retry_policy,
+            cache_key,
+            task_id,
+            in_progress_task_path,
+        )
+    else:
+        return PregelTask(task_id, name, in_progress_task_path)
+
+
+def prepare_push_task_send(
+    task_path: tuple[str, tuple],
+    # (PUSH, parent task path)
+    task_id_checksum: str | None,
+    *,
+    checkpoint: Checkpoint,
+    checkpoint_id_bytes: bytes,
+    pending_writes: list[PendingWrite],
+    channels: Mapping[str, BaseChannel],
+    managed: ManagedValueMapping,
+    config: RunnableConfig,
+    step: int,
+    stop: int,
+    for_execution: bool,
+    store: BaseStore | None = None,
+    checkpointer: BaseCheckpointSaver | None = None,
+    manager: None | ParentRunManager | AsyncParentRunManager = None,
+    cache_policy: CachePolicy | None = None,
+    retry_policy: Sequence[RetryPolicy] = (),
+    parent_ns: str,
+    task_id_func: _TaskIDFn,
+    processes: Mapping[str, PregelNode],
+) -> PregelTask | PregelExecutableTask | None:
+    if len(task_path) == 2:
+        # SEND tasks, executed in superstep n+1
+        # (PUSH, idx of pending send)
+        idx = cast(int, task_path[1])
+        if not channels[TASKS].is_available():
+            return
+        sends: Sequence[Send] = channels[TASKS].get()
+        if idx < 0 or idx >= len(sends):
+            return
+        packet = sends[idx]
+        if not isinstance(packet, Send):
+            logger.warning(
+                f"Ignoring invalid packet type {type(packet)} in pending sends"
+            )
+            return
+
+        if packet.node not in processes:
+            logger.warning(f"Ignoring unknown node name {packet.node} in pending sends")
+            return
+        # find process
+        proc = processes[packet.node]
+        proc_node = proc.node
+        if proc_node is None:
+            return
+        # create task id
+        triggers = PUSH_TRIGGER
+        checkpoint_ns = (
+            f"{parent_ns}{NS_SEP}{packet.node}" if parent_ns else packet.node
+        )
+        task_id = task_id_func(
+            checkpoint_id_bytes,
+            checkpoint_ns,
+            str(step),
+            packet.node,
+            PUSH,
+            str(idx),
+        )
+    else:
+        logger.warning(f"Ignoring invalid PUSH task path {task_path}")
+        return
+    configurable = config.get(CONF, {})
+    task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
+    # we append False to the task path to indicate that a call is not being made
+    # so we should return interrupts from this task
+    translated_task_path = (*task_path[:3], False)
+    metadata = {
+        "langgraph_step": step,
+        "langgraph_node": packet.node,
+        "langgraph_triggers": triggers,
+        "langgraph_path": translated_task_path,
+        "langgraph_checkpoint_ns": task_checkpoint_ns,
+    }
+    if task_id_checksum is not None:
+        assert task_id == task_id_checksum, f"{task_id} != {task_id_checksum}"
+    if for_execution:
+        if proc.metadata:
+            metadata.update(proc.metadata)
+        writes: deque[tuple[str, Any]] = deque()
+        cache_policy = proc.cache_policy or cache_policy
+        if cache_policy:
+            args_key = cache_policy.key_func(packet.arg)
+            cache_key = CacheKey(
+                (
+                    CACHE_NS_WRITES,
+                    (identifier(proc) or "__dynamic__"),
+                    packet.node,
+                ),
+                xxh3_128_hexdigest(
+                    args_key.encode() if isinstance(args_key, str) else args_key,
+                ),
+                cache_policy.ttl,
+            )
+        else:
+            cache_key = None
+        scratchpad = _scratchpad(
+            config[CONF].get(CONFIG_KEY_SCRATCHPAD),
+            pending_writes,
+            task_id,
+            xxh3_128_hexdigest(task_checkpoint_ns.encode()),
+            config[CONF].get(CONFIG_KEY_RESUME_MAP),
+            step,
+            stop,
+        )
+        runtime = cast(Runtime, configurable.get(CONFIG_KEY_RUNTIME, DEFAULT_RUNTIME))
+        runtime = runtime.override(
+            store=store, previous=checkpoint["channel_values"].get(PREVIOUS, None)
+        )
+        additional_config: RunnableConfig = {
+            "metadata": metadata,
+            "tags": proc.tags,
+        }
+        return PregelExecutableTask(
+            packet.node,
+            packet.arg,
+            proc_node,
+            writes,
+            patch_config(
+                merge_configs(config, additional_config),
+                run_name=packet.node,
+                callbacks=(
+                    manager.get_child(f"graph:step:{step}") if manager else None
+                ),
+                configurable={
+                    CONFIG_KEY_TASK_ID: task_id,
+                    # deque.extend is thread-safe
+                    CONFIG_KEY_SEND: writes.extend,
+                    CONFIG_KEY_READ: partial(
+                        local_read,
+                        scratchpad,
+                        channels,
+                        managed,
+                        PregelTaskWrites(
+                            translated_task_path, packet.node, writes, triggers
+                        ),
+                    ),
+                    CONFIG_KEY_CHECKPOINTER: (
+                        checkpointer or configurable.get(CONFIG_KEY_CHECKPOINTER)
+                    ),
+                    CONFIG_KEY_CHECKPOINT_MAP: {
+                        **configurable.get(CONFIG_KEY_CHECKPOINT_MAP, {}),
+                        parent_ns: checkpoint["id"],
+                    },
+                    CONFIG_KEY_CHECKPOINT_ID: None,
+                    CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
+                    CONFIG_KEY_SCRATCHPAD: scratchpad,
+                    CONFIG_KEY_RUNTIME: runtime,
+                },
+            ),
+            triggers,
+            proc.retry_policy or retry_policy,
+            cache_key,
+            task_id,
+            translated_task_path,
+            writers=proc.flat_writers,
+            subgraphs=proc.subgraphs,
+        )
+    else:
+        return PregelTask(task_id, packet.node, translated_task_path)
+
+
 def checkpoint_null_version(
     checkpoint: Checkpoint,
 ) -> V | None:

libs/langgraph/langgraph/pregel/_loop.py

@@ -459,9 +459,6 @@ class PregelLoop:
     def tick(self) -> bool:
         """Execute a single iteration of the Pregel loop.
 
-        Args:
-            input_keys: The key(s) to read input from.
-
         Returns:
             True if more iterations are needed.
         """
@@ -769,6 +766,7 @@ class PregelLoop:
             ]
             self.checkpoint["channel_values"][TASKS] = sanitized_tasks
         # bail if no checkpointer
+
         if do_checkpoint and self._checkpointer_put_after_previous is not None:
             self.prev_checkpoint_config = (
                 self.checkpoint_config
@@ -938,8 +936,15 @@ class PregelLoop:
                 stream_modes = self.stream.modes if self.stream else []
                 if "updates" in stream_modes:
                     self._emit("updates", lambda: iter(interrupts))
-                elif "values" in stream_modes:
-                    self._emit("values", lambda: iter(interrupts))
+                if "values" in stream_modes:
+                    current_values = read_channels(self.channels, self.output_keys)
+                    # self.output_keys is a sequence, stream chunk contains entire state and interrupts
+                    if isinstance(current_values, dict):
+                        current_values[INTERRUPT] = interrupts[0][INTERRUPT]
+                        self._emit("values", lambda: iter([current_values]))
+                    # self.output_keys is a string, stream chunk contains only interrupts
+                    else:
+                        self._emit("values", lambda: iter(interrupts))
             elif writes[0][0] != ERROR:
                 self._emit(
                     "updates",

libs/langgraph/langgraph/pregel/main.py

@@ -210,8 +210,10 @@ class NodeBuilder:
         *channels: str,
         read: bool = True,
     ) -> Self:
-        """Add channels to subscribe to. Node will be invoked when any of these
-        channels are updated, with a dict of the channel values as input.
+        """Add channels to subscribe to.
+
+        Node will be invoked when any of these channels are updated, with a dict of the
+        channel values as input.
 
         Args:
             channels: Channel name(s) to subscribe to
@@ -270,8 +272,8 @@ class NodeBuilder:
         """Add channel writes.
 
         Args:
-            *channels: Channel names to write to
-            **kwargs: Channel name and value mappings
+            *channels: Channel names to write to.
+            **kwargs: Channel name and value mappings.
 
         Returns:
             Self for chaining
@@ -375,12 +377,12 @@ class Pregel(
     ### Advanced channels: Context and BinaryOperatorAggregate
 
     - `Context`: exposes the value of a context manager, managing its lifecycle.
-      Useful for accessing external resources that require setup and/or teardown. eg.
-      `client = Context(httpx.Client)`
+        Useful for accessing external resources that require setup and/or teardown. e.g.
+        `client = Context(httpx.Client)`
     - `BinaryOperatorAggregate`: stores a persistent value, updated by applying
-       a binary operator to the current value and each update
-       sent to the channel, useful for computing aggregates over multiple steps. eg.
-      `total = BinaryOperatorAggregate(int, operator.add)`
+        a binary operator to the current value and each update
+        sent to the channel, useful for computing aggregates over multiple steps. e.g.
+        `total = BinaryOperatorAggregate(int, operator.add)`
 
     ## Examples
 
@@ -495,7 +497,7 @@ class Pregel(
         {"c": ["foofoo", "foofoofoofoo"]}
         ```
 
-    Example: Using a BinaryOperatorAggregate channel
+    Example: Using a `BinaryOperatorAggregate` channel
         ```python
         from langgraph.channels import EphemeralValue, BinaryOperatorAggregate
         from langgraph.pregel import Pregel, NodeBuilder
@@ -541,8 +543,9 @@ class Pregel(
 
     Example: Introducing a cycle
         This example demonstrates how to introduce a cycle in the graph, by having
-        a chain write to a channel it subscribes to. Execution will continue
-        until a None value is written to the channel.
+        a chain write to a channel it subscribes to.
+
+        Execution will continue until a `None` value is written to the channel.
 
         ```python
         from langgraph.channels import EphemeralValue
@@ -1426,7 +1429,8 @@ class Pregel(
         Args:
             config: The config to apply the updates to.
             supersteps: A list of supersteps, each including a list of updates to apply sequentially to a graph state.
-                        Each update is a tuple of the form `(values, as_node, task_id)` where `task_id` is optional.
+
+                Each update is a tuple of the form `(values, as_node, task_id)` where `task_id` is optional.
 
         Raises:
             ValueError: If no checkpointer is set or no updates are provided.
@@ -1702,7 +1706,9 @@ class Pregel(
             # we use the task id generated by prepare_next_tasks
             node_to_task_ids: dict[str, deque[str]] = defaultdict(deque)
             if saved is not None and saved.pending_writes is not None:
-                # tasks for this checkpoint
+                # we call prepare_next_tasks to discover the task IDs that
+                # would have been generated, so we can reuse them and
+                # properly populate task.result in state history
                 next_tasks = prepare_next_tasks(
                     checkpoint,
                     saved.pending_writes,
@@ -1721,32 +1727,6 @@ class Pregel(
                 for t in next_tasks.values():
                     node_to_task_ids[t.name].append(t.id)
 
-                # apply null writes
-                if null_writes := [
-                    w[1:] for w in saved.pending_writes or [] if w[0] == NULL_TASK_ID
-                ]:
-                    apply_writes(
-                        checkpoint,
-                        channels,
-                        [PregelTaskWrites((), INPUT, null_writes, [])],
-                        checkpointer.get_next_version,
-                        self.trigger_to_nodes,
-                    )
-                # apply writes
-                for tid, k, v in saved.pending_writes:
-                    if k in (ERROR, INTERRUPT):
-                        continue
-                    if tid not in next_tasks:
-                        continue
-                    next_tasks[tid].writes.append((k, v))
-                if tasks := [t for t in next_tasks.values() if t.writes]:
-                    apply_writes(
-                        checkpoint,
-                        channels,
-                        tasks,
-                        checkpointer.get_next_version,
-                        self.trigger_to_nodes,
-                    )
             valid_updates: list[tuple[str, dict[str, Any] | None, str | None]] = []
             if len(updates) == 1:
                 values, as_node, task_id = updates[0]
@@ -1893,7 +1873,8 @@ class Pregel(
         Args:
             config: The config to apply the updates to.
             supersteps: A list of supersteps, each including a list of updates to apply sequentially to a graph state.
-                        Each update is a tuple of the form `(values, as_node, task_id)` where `task_id` is optional.
+
+                Each update is a tuple of the form `(values, as_node, task_id)` where `task_id` is optional.
 
         Raises:
             ValueError: If no checkpointer is set or no updates are provided.
@@ -2167,7 +2148,9 @@ class Pregel(
             # we use the task id generated by prepare_next_tasks
             node_to_task_ids: dict[str, deque[str]] = defaultdict(deque)
             if saved is not None and saved.pending_writes is not None:
-                # tasks for this checkpoint
+                # we call prepare_next_tasks to discover the task IDs that
+                # would have been generated, so we can reuse them and
+                # properly populate task.result in state history
                 next_tasks = prepare_next_tasks(
                     checkpoint,
                     saved.pending_writes,
@@ -2186,31 +2169,6 @@ class Pregel(
                 for t in next_tasks.values():
                     node_to_task_ids[t.name].append(t.id)
 
-                # apply null writes
-                if null_writes := [
-                    w[1:] for w in saved.pending_writes or [] if w[0] == NULL_TASK_ID
-                ]:
-                    apply_writes(
-                        checkpoint,
-                        channels,
-                        [PregelTaskWrites((), INPUT, null_writes, [])],
-                        checkpointer.get_next_version,
-                        self.trigger_to_nodes,
-                    )
-                for tid, k, v in saved.pending_writes:
-                    if k in (ERROR, INTERRUPT):
-                        continue
-                    if tid not in next_tasks:
-                        continue
-                    next_tasks[tid].writes.append((k, v))
-                if tasks := [t for t in next_tasks.values() if t.writes]:
-                    apply_writes(
-                        checkpoint,
-                        channels,
-                        tasks,
-                        checkpointer.get_next_version,
-                        self.trigger_to_nodes,
-                    )
             valid_updates: list[tuple[str, dict[str, Any] | None, str | None]] = []
             if len(updates) == 1:
                 values, as_node, task_id = updates[0]
@@ -2401,7 +2359,7 @@ class Pregel(
             validate_keys(output_keys, self.channels)
         interrupt_before = interrupt_before or self.interrupt_before_nodes
         interrupt_after = interrupt_after or self.interrupt_after_nodes
-        if not isinstance(stream_mode, list):
+        if isinstance(stream_mode, str):
             stream_modes = {stream_mode}
         else:
             stream_modes = set(stream_mode)
@@ -2467,6 +2425,7 @@ class Pregel(
             context: The static context to use for the run.
                 !!! version-added "Added in version 0.6.0"
             stream_mode: The mode to stream output, defaults to `self.stream_mode`.
+
                 Options are:
 
                 - `"values"`: Emit all values in the state after each step, including interrupts.
@@ -2475,31 +2434,36 @@ class Pregel(
                     If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately.
                 - `"custom"`: Emit custom data from inside nodes or tasks using `StreamWriter`.
                 - `"messages"`: Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks.
-                    Will be emitted as 2-tuples `(LLM token, metadata)`.
+                    - Will be emitted as 2-tuples `(LLM token, metadata)`.
                 - `"checkpoints"`: Emit an event when a checkpoint is created, in the same format as returned by `get_state()`.
                 - `"tasks"`: Emit events when tasks start and finish, including their results and errors.
+                - `"debug"`: Emit debug events with as much information as possible for each step.
 
                 You can pass a list as the `stream_mode` parameter to stream multiple modes at once.
                 The streamed outputs will be tuples of `(mode, data)`.
 
-                See [LangGraph streaming guide](https://langchain-ai.github.io/langgraph/how-tos/streaming/) for more details.
-            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes. Does not affect the output of the graph in any way.
+                See [LangGraph streaming guide](https://docs.langchain.com/oss/python/langgraph/streaming) for more details.
+            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes.
+
+                Does not affect the output of the graph in any way.
             output_keys: The keys to stream, defaults to all non-context channels.
             interrupt_before: Nodes to interrupt before, defaults to all nodes in the graph.
             interrupt_after: Nodes to interrupt after, defaults to all nodes in the graph.
             durability: The durability mode for the graph execution, defaults to `"async"`.
+
                 Options are:
 
                 - `"sync"`: Changes are persisted synchronously before the next step starts.
                 - `"async"`: Changes are persisted asynchronously while the next step executes.
                 - `"exit"`: Changes are persisted only when the graph exits.
-            subgraphs: Whether to stream events from inside subgraphs, defaults to False.
+            subgraphs: Whether to stream events from inside subgraphs, defaults to `False`.
+
                 If `True`, the events will be emitted as tuples `(namespace, data)`,
                 or `(namespace, mode, data)` if `stream_mode` is a list,
                 where `namespace` is a tuple with the path to the node where a subgraph is invoked,
                 e.g. `("parent_node:<task_id>", "child_node:<task_id>")`.
 
-                See [LangGraph streaming guide](https://langchain-ai.github.io/langgraph/how-tos/streaming/) for more details.
+                See [LangGraph streaming guide](https://docs.langchain.com/oss/python/langgraph/streaming) for more details.
 
         Yields:
             The output of each step in the graph. The output shape depends on the `stream_mode`.
@@ -2735,6 +2699,7 @@ class Pregel(
             context: The static context to use for the run.
                 !!! version-added "Added in version 0.6.0"
             stream_mode: The mode to stream output, defaults to `self.stream_mode`.
+
                 Options are:
 
                 - `"values"`: Emit all values in the state after each step, including interrupts.
@@ -2743,30 +2708,36 @@ class Pregel(
                     If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately.
                 - `"custom"`: Emit custom data from inside nodes or tasks using `StreamWriter`.
                 - `"messages"`: Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks.
-                    Will be emitted as 2-tuples `(LLM token, metadata)`.
+                    - Will be emitted as 2-tuples `(LLM token, metadata)`.
+                - `"checkpoints"`: Emit an event when a checkpoint is created, in the same format as returned by `get_state()`.
+                - `"tasks"`: Emit events when tasks start and finish, including their results and errors.
                 - `"debug"`: Emit debug events with as much information as possible for each step.
 
                 You can pass a list as the `stream_mode` parameter to stream multiple modes at once.
                 The streamed outputs will be tuples of `(mode, data)`.
 
-                See [LangGraph streaming guide](https://langchain-ai.github.io/langgraph/how-tos/streaming/) for more details.
-            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes. Does not affect the output of the graph in any way.
+                See [LangGraph streaming guide](https://docs.langchain.com/oss/python/langgraph/streaming) for more details.
+            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes.
+
+                Does not affect the output of the graph in any way.
             output_keys: The keys to stream, defaults to all non-context channels.
             interrupt_before: Nodes to interrupt before, defaults to all nodes in the graph.
             interrupt_after: Nodes to interrupt after, defaults to all nodes in the graph.
             durability: The durability mode for the graph execution, defaults to `"async"`.
+
                 Options are:
 
                 - `"sync"`: Changes are persisted synchronously before the next step starts.
                 - `"async"`: Changes are persisted asynchronously while the next step executes.
                 - `"exit"`: Changes are persisted only when the graph exits.
-            subgraphs: Whether to stream events from inside subgraphs, defaults to False.
+            subgraphs: Whether to stream events from inside subgraphs, defaults to `False`.
+
                 If `True`, the events will be emitted as tuples `(namespace, data)`,
                 or `(namespace, mode, data)` if `stream_mode` is a list,
                 where `namespace` is a tuple with the path to the node where a subgraph is invoked,
                 e.g. `("parent_node:<task_id>", "child_node:<task_id>")`.
 
-                See [LangGraph streaming guide](https://langchain-ai.github.io/langgraph/how-tos/streaming/) for more details.
+                See [LangGraph streaming guide](https://docs.langchain.com/oss/python/langgraph/streaming) for more details.
 
         Yields:
             The output of each step in the graph. The output shape depends on the `stream_mode`.
@@ -3069,11 +3040,14 @@ class Pregel(
             context: The static context to use for the run.
                 !!! version-added "Added in version 0.6.0"
             stream_mode: The stream mode for the graph run.
-            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes. Does not affect the output of the graph in any way.
+            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes.
+
+                Does not affect the output of the graph in any way.
             output_keys: The output keys to retrieve from the graph run.
             interrupt_before: The nodes to interrupt the graph run before.
             interrupt_after: The nodes to interrupt the graph run after.
             durability: The durability mode for the graph execution, defaults to `"async"`.
+
                 Options are:
 
                 - `"sync"`: Changes are persisted synchronously before the next step starts.
@@ -3148,31 +3122,33 @@ class Pregel(
         durability: Durability | None = None,
         **kwargs: Any,
     ) -> dict[str, Any] | Any:
-        """Asynchronously invoke the graph on a single input.
+        """Asynchronously run the graph with a single input and config.
 
         Args:
-            input: The input data for the computation. It can be a dictionary or any other type.
-            config: The configuration for the computation.
+            input: The input data for the graph. It can be a dictionary or any other type.
+            config: The configuration for the graph run.
             context: The static context to use for the run.
                 !!! version-added "Added in version 0.6.0"
-            stream_mode: The stream mode for the computation.
-            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes. Does not affect the output of the graph in any way.
-            output_keys: The output keys to include in the result.
-            interrupt_before: The nodes to interrupt before.
-            interrupt_after: The nodes to interrupt after.
+            stream_mode: The stream mode for the graph run.
+            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes.
+
+                Does not affect the output of the graph in any way.
+            output_keys: The output keys to retrieve from the graph run.
+            interrupt_before: The nodes to interrupt the graph run before.
+            interrupt_after: The nodes to interrupt the graph run after.
             durability: The durability mode for the graph execution, defaults to `"async"`.
+
                 Options are:
 
                 - `"sync"`: Changes are persisted synchronously before the next step starts.
                 - `"async"`: Changes are persisted asynchronously while the next step executes.
                 - `"exit"`: Changes are persisted only when the graph exits.
-            **kwargs: Additional keyword arguments.
+            **kwargs: Additional keyword arguments to pass to the graph run.
 
         Returns:
-            The result of the computation. If `stream_mode` is `"values"`, it returns the latest value.
-            If `stream_mode` is `"chunks"`, it returns a list of chunks.
+            The output of the graph run. If `stream_mode` is `"values"`, it returns the latest output.
+            If `stream_mode` is not `"values"`, it returns a list of output chunks.
         """
-
         output_keys = output_keys if output_keys is not None else self.output_channels
 
         latest: dict[str, Any] | Any = None

libs/langgraph/langgraph/pregel/remote.py

@@ -725,9 +725,10 @@ class RemoteGraph(PregelProtocol):
             input = None
         else:
             command = None
+        thread_id = sanitized_config.get("configurable", {}).pop("thread_id", None)
 
         for chunk in sync_client.runs.stream(
-            thread_id=sanitized_config["configurable"].get("thread_id"),
+            thread_id=thread_id,
             assistant_id=self.assistant_id,
             input=input,
             command=command,
@@ -834,9 +835,10 @@ class RemoteGraph(PregelProtocol):
             input = None
         else:
             command = None
+        thread_id = sanitized_config.get("configurable", {}).pop("thread_id", None)
 
         async for chunk in client.runs.stream(
-            thread_id=sanitized_config["configurable"].get("thread_id"),
+            thread_id=thread_id,
             assistant_id=self.assistant_id,
             input=input,
             command=command,

libs/langgraph/langgraph/runtime.py

@@ -111,7 +111,7 @@ class Runtime(Generic[ContextT]):
             stream_writer=other.stream_writer
             if other.stream_writer is not _no_op_stream_writer
             else self.stream_writer,
-            previous=other.previous or self.previous,
+            previous=self.previous if other.previous is None else other.previous,
         )
 
     def override(

libs/langgraph/langgraph/types.py

@@ -55,6 +55,7 @@ __all__ = (
     "Command",
     "Durability",
     "interrupt",
+    "Overwrite",
 )
 
 Durability = Literal["sync", "async", "exit"]
@@ -283,26 +284,32 @@ class Send:
         node (str): The name of the target node to send the message to.
         arg (Any): The state or message to send to the target node.
 
-    Examples:
-        >>> from typing import Annotated
-        >>> import operator
-        >>> class OverallState(TypedDict):
-        ...     subjects: list[str]
-        ...     jokes: Annotated[list[str], operator.add]
-        >>> from langgraph.types import Send
-        >>> from langgraph.graph import END, START
-        >>> def continue_to_jokes(state: OverallState):
-        ...     return [Send("generate_joke", {"subject": s}) for s in state["subjects"]]
-        >>> from langgraph.graph import StateGraph
-        >>> builder = StateGraph(OverallState)
-        >>> builder.add_node("generate_joke", lambda state: {"jokes": [f"Joke about {state['subject']}"]})
-        >>> builder.add_conditional_edges(START, continue_to_jokes)
-        >>> builder.add_edge("generate_joke", END)
-        >>> graph = builder.compile()
-        >>>
-        >>> # Invoking with two subjects results in a generated joke for each
-        >>> graph.invoke({"subjects": ["cats", "dogs"]})
-        {'subjects': ['cats', 'dogs'], 'jokes': ['Joke about cats', 'Joke about dogs']}
+    !!! example
+
+        ```python
+        from typing import Annotated
+        from langgraph.types import Send
+        from langgraph.graph import END, START
+        from langgraph.graph import StateGraph
+        import operator
+
+        class OverallState(TypedDict):
+            subjects: list[str]
+            jokes: Annotated[list[str], operator.add]
+
+        def continue_to_jokes(state: OverallState):
+            return [Send("generate_joke", {"subject": s}) for s in state["subjects"]]
+
+        builder = StateGraph(OverallState)
+        builder.add_node("generate_joke", lambda state: {"jokes": [f"Joke about {state['subject']}"]})
+        builder.add_conditional_edges(START, continue_to_jokes)
+        builder.add_edge("generate_joke", END)
+        graph = builder.compile()
+
+        # Invoking with two subjects results in a generated joke for each
+        graph.invoke({"subjects": ["cats", "dogs"]})
+        # {'subjects': ['cats', 'dogs'], 'jokes': ['Joke about cats', 'Joke about dogs']}
+        ```
     """
 
     __slots__ = ("node", "arg")
@@ -342,10 +349,8 @@ N = TypeVar("N", bound=Hashable)
 class Command(Generic[N], ToolOutputMixin):
     """One or more commands to update the graph's state and send messages to nodes.
 
-    !!! version-added "Added in version 0.2.24"
-
     Args:
-        graph: graph to send the command to. Supported values are:
+        graph: Graph to send the command to. Supported values are:
 
             - `None`: the current graph
             - `Command.PARENT`: closest parent graph
@@ -415,7 +420,8 @@ def interrupt(value: Any) -> Any:
     To use an `interrupt`, you must enable a checkpointer, as the feature relies
     on persisting the graph state.
 
-    Example:
+    !!! example
+
         ```python
         import uuid
         from typing import Optional
@@ -520,38 +526,42 @@ def interrupt(value: Any) -> Any:
 
 @dataclass(slots=True)
 class Overwrite:
-    """Bypass a reducer and write the wrapped value directly to a BinaryOperatorAggregate channel.
+    """Bypass a reducer and write the wrapped value directly to a `BinaryOperatorAggregate` channel.
 
-    Receiving multiple Overwrite values for the same channel in a single super-step will raise an InvalidUpdateError.
+    Receiving multiple `Overwrite` values for the same channel in a single super-step
+    will raise an `InvalidUpdateError`.
 
-    Example:
-        >>> from typing import Annotated
-        >>> import operator
-        >>> from langgraph.graph import StateGraph
-        >>> from langgraph.types import Overwrite
-        >>>
-        >>> class State(TypedDict):
-        ...     messages: Annotated[list, operator.add]
-        >>>
-        >>> def node_a(state: TypedDict):
-        ...     # Normal update: uses the reducer (operator.add)
-        ...     return {"messages": ["a"]}
-        >>>
-        >>> def node_b(state: State):
-        ...     # Overwrite: bypasses the reducer and replaces the entire value
-        ...     return {"messages": Overwrite(value=["b"])}
-        >>>
-        >>> builder = StateGraph(State)
-        >>> builder.add_node("node_a", node_a)
-        >>> builder.add_node("node_b", node_b)
-        >>> builder.set_entry_point("node_a")
-        >>> builder.add_edge("node_a", "node_b")
-        >>> graph = builder.compile()
-        >>>
-        >>> # Without Overwrite in node_b, messages would be ["START", "a", "b"]
-        >>> # With Overwrite, messages is just ["b"]
-        >>> result = graph.invoke({"messages": ["START"]})
-        >>> assert result == {"messages": ["b"]}
+    !!! example
+
+        ```python
+        from typing import Annotated
+        import operator
+        from langgraph.graph import StateGraph
+        from langgraph.types import Overwrite
+
+        class State(TypedDict):
+            messages: Annotated[list, operator.add]
+
+        def node_a(state: TypedDict):
+            # Normal update: uses the reducer (operator.add)
+            return {"messages": ["a"]}
+
+        def node_b(state: State):
+            # Overwrite: bypasses the reducer and replaces the entire value
+            return {"messages": Overwrite(value=["b"])}
+
+        builder = StateGraph(State)
+        builder.add_node("node_a", node_a)
+        builder.add_node("node_b", node_b)
+        builder.set_entry_point("node_a")
+        builder.add_edge("node_a", "node_b")
+        graph = builder.compile()
+
+        # Without Overwrite in node_b, messages would be ["START", "a", "b"]
+        # With Overwrite, messages is just ["b"]
+        result = graph.invoke({"messages": ["START"]})
+        assert result == {"messages": ["b"]}
+        ```
     """
 
     value: Any

libs/langgraph/langgraph/typing.py

@@ -31,13 +31,13 @@ ContextT_contra = TypeVar(
 )
 
 InputT = TypeVar("InputT", bound=StateLike, default=StateT)
-"""Type variable used to represent the input to a state graph.
+"""Type variable used to represent the input to a `StateGraph`.
 
 Defaults to `StateT`.
 """
 
 OutputT = TypeVar("OutputT", bound=StateLike, default=StateT)
-"""Type variable used to represent the output of a state graph.
+"""Type variable used to represent the output of a `StateGraph`.
 
 Defaults to `StateT`.
 """

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "1.0.2"
+version = "1.0.4"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.10"
@@ -32,8 +32,15 @@ dependencies = [
     "pydantic>=2.7.4",
 ]
 
+
 [project.urls]
-Repository = "https://www.github.com/langchain-ai/langgraph"
+Homepage = "https://docs.langchain.com/oss/python/langgraph/overview"
+Documentation = "https://reference.langchain.com/python/langgraph/"
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/langgraph"
+Changelog = "https://github.com/langchain-ai/langgraph/releases"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [dependency-groups]
 test = [

libs/langgraph/tests/test_large_cases.py

@@ -3271,7 +3271,7 @@ def test_root_graph(
                     content="result for query",
                     name="search_api",
                     tool_call_id="tool_call123",
-                    id="00000000-0000-4000-8000-000000000024",
+                    id="00000000-0000-4000-8000-000000000004",
                 )
             ]
         },
@@ -3294,7 +3294,7 @@ def test_root_graph(
                     content="result for another",
                     name="search_api",
                     tool_call_id="tool_call456",
-                    id="00000000-0000-4000-8000-000000000030",
+                    id="00000000-0000-4000-8000-000000000005",
                 )
             ]
         },
@@ -3898,7 +3898,7 @@ def test_root_graph(
         "__root__": [
             HumanMessage(
                 content="what is weather in sf",
-                id="00000000-0000-4000-8000-000000000051",
+                id="00000000-0000-4000-8000-000000000008",
             ),
             AIMessage(
                 content="",
@@ -3918,7 +3918,7 @@ def test_root_graph(
             ),
             AIMessage(content="answer", id="ai2"),
             AIMessage(
-                content="an extra message", id="00000000-0000-4000-8000-000000000066"
+                content="an extra message", id="00000000-0000-4000-8000-000000000010"
             ),
             HumanMessage(content="what is weather in la"),
         ],

libs/langgraph/tests/test_pregel.py

@@ -8422,23 +8422,55 @@ def test_null_resume_disallowed_with_multiple_interrupts(
     }
 
 
-def test_interrupt_stream_mode_values():
-    """Test that interrupts are surfaced when steam_mode='values'"""
+def test_interrupt_stream_mode_values(sync_checkpointer: BaseCheckpointSaver):
+    """Test that interrupts are surfaced on 'values' stream mode"""
 
     class State(TypedDict):
+        robot_input: str
         human_input: str
 
+    def robot_input_node(state: State) -> State:
+        return {"robot_input": "beep boop i am a robot"}
+
     def human_input_node(state: State) -> Command:
         human_input = interrupt("interrupt")
         return Command(update={"human_input": human_input})
 
     builder = StateGraph(State)
+    builder.add_node(robot_input_node)
     builder.add_node(human_input_node)
-    builder.add_edge(START, "human_input_node")
-    app = builder.compile()
+    builder.add_edge(START, "robot_input_node")
+    builder.add_edge("robot_input_node", "human_input_node")
+    app = builder.compile(checkpointer=sync_checkpointer)
+    config = {"configurable": {"thread_id": str(uuid.uuid4())}}
 
-    result = [*app.stream(State(), stream_mode="values")]
-    assert "__interrupt__" in result[-1]
+    result = [*app.stream(State(), config, stream_mode=["updates", "values"])]
+    assert len(result) == 4
+    assert result == [
+        ("updates", {"robot_input_node": {"robot_input": "beep boop i am a robot"}}),
+        ("values", {"robot_input": "beep boop i am a robot"}),
+        ("updates", {"__interrupt__": (Interrupt(value="interrupt", id=AnyStr()),)}),
+        (
+            "values",
+            {
+                "robot_input": "beep boop i am a robot",
+                "__interrupt__": (Interrupt(value="interrupt", id=AnyStr()),),
+            },
+        ),
+    ]
+    resume_result = [
+        *app.stream(
+            Command(resume="i am a human"), config, stream_mode=["updates", "values"]
+        )
+    ]
+    assert resume_result == [
+        ("values", {"robot_input": "beep boop i am a robot"}),
+        ("updates", {"human_input_node": {"human_input": "i am a human"}}),
+        (
+            "values",
+            {"robot_input": "beep boop i am a robot", "human_input": "i am a human"},
+        ),
+    ]
 
 
 def test_supersteps_populate_task_results(
@@ -8805,3 +8837,43 @@ def test_overwrite_parallel_error(
         InvalidUpdateError, match="Can receive only one Overwrite value per super-step."
     ):
         graph.invoke({"messages": ["START"]}, config)
+
+
+def test_fork_does_not_apply_pending_writes(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Test that forking with update_state does not apply pending writes from original execution."""
+
+    class State(TypedDict):
+        value: Annotated[int, operator.add]
+
+    def node_a(state: State) -> State:
+        return {"value": 10}
+
+    def node_b(state: State) -> State:
+        return {"value": 100}
+
+    graph = (
+        StateGraph(State)
+        .add_node("node_a", node_a)
+        .add_node("node_b", node_b)
+        .add_edge(START, "node_a")
+        .add_edge("node_a", "node_b")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    thread1 = {"configurable": {"thread_id": "1"}}
+    graph.invoke({"value": 1}, thread1)
+
+    history = list(graph.get_state_history(thread1))
+    checkpoint_before_a = next(s for s in history if s.next == ("node_a",))
+
+    fork_config = graph.update_state(
+        checkpoint_before_a.config, {"value": 20}, as_node="node_a"
+    )
+
+    # Continue from fork (should run node_b)
+    result = graph.invoke(None, fork_config)
+
+    # Should be: 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
+    assert result == {"value": 121}

libs/langgraph/tests/test_pregel_async.py

@@ -9196,6 +9196,64 @@ async def test_astream_waiter_cleanup_on_cancel(
     assert all(t.done() for t in recorded_tasks)
 
 
+@NEEDS_CONTEXTVARS
+async def test_interrupt_stream_mode_values(async_checkpointer: BaseCheckpointSaver):
+    """Test that interrupts are surfaced on 'values' stream mode"""
+
+    class State(TypedDict):
+        robot_input: str
+        human_input: str
+
+    def robot_input_node(state: State) -> State:
+        return {"robot_input": "beep boop i am a robot"}
+
+    def human_input_node(state: State) -> Command:
+        human_input = interrupt("interrupt")
+        return Command(update={"human_input": human_input})
+
+    builder = StateGraph(State)
+    builder.add_node(robot_input_node)
+    builder.add_node(human_input_node)
+    builder.add_edge(START, "robot_input_node")
+    builder.add_edge("robot_input_node", "human_input_node")
+    app = builder.compile(checkpointer=async_checkpointer)
+    config = {"configurable": {"thread_id": str(uuid.uuid4())}}
+
+    result = [
+        (mode, e)
+        async for mode, e in app.astream(
+            State(), config, stream_mode=["updates", "values"]
+        )
+    ]
+    assert len(result) == 4
+    assert result == [
+        ("updates", {"robot_input_node": {"robot_input": "beep boop i am a robot"}}),
+        ("values", {"robot_input": "beep boop i am a robot"}),
+        ("updates", {"__interrupt__": (Interrupt(value="interrupt", id=AnyStr()),)}),
+        (
+            "values",
+            {
+                "robot_input": "beep boop i am a robot",
+                "__interrupt__": (Interrupt(value="interrupt", id=AnyStr()),),
+            },
+        ),
+    ]
+    resume_result = [
+        (mode, e)
+        async for mode, e in app.astream(
+            Command(resume="i am a human"), config, stream_mode=["updates", "values"]
+        )
+    ]
+    assert resume_result == [
+        ("values", {"robot_input": "beep boop i am a robot"}),
+        ("updates", {"human_input_node": {"human_input": "i am a human"}}),
+        (
+            "values",
+            {"robot_input": "beep boop i am a robot", "human_input": "i am a human"},
+        ),
+    ]
+
+
 async def test_supersteps_populate_task_results(
     async_checkpointer: BaseCheckpointSaver,
 ) -> None:
@@ -9249,3 +9307,41 @@ async def test_supersteps_populate_task_results(
 
     assert bulk_start_result == ref_start_result == {"num": 1, "text": "one"}
     assert bulk_double_result == ref_double_result == {"num": 2, "text": "oneone"}
+
+
+async def test_fork_does_not_apply_pending_writes(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Test that forking with aupdate_state does not apply pending writes from original execution."""
+
+    class State(TypedDict):
+        value: Annotated[int, operator.add]
+
+    def node_a(state: State) -> State:
+        return {"value": 10}
+
+    def node_b(state: State) -> State:
+        return {"value": 100}
+
+    graph = (
+        StateGraph(State)
+        .add_node("node_a", node_a)
+        .add_node("node_b", node_b)
+        .add_edge(START, "node_a")
+        .add_edge("node_a", "node_b")
+        .compile(checkpointer=async_checkpointer)
+    )
+
+    thread1 = {"configurable": {"thread_id": "1"}}
+    await graph.ainvoke({"value": 1}, thread1)
+
+    history = [c async for c in graph.aget_state_history(thread1)]
+    checkpoint_before_a = next(s for s in history if s.next == ("node_a",))
+
+    fork_config = await graph.aupdate_state(
+        checkpoint_before_a.config, {"value": 20}, as_node="node_a"
+    )
+    result = await graph.ainvoke(None, fork_config)
+
+    # 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
+    assert result == {"value": 121}

libs/langgraph/tests/test_remote_graph.py

@@ -840,6 +840,46 @@ def test_invoke():
     assert result == {"messages": [{"type": "human", "content": "world"}]}
 
 
+def test_invoke_sanitizes_thread_id():
+    # Ensure that invoking with thread_id passes thread_id as a top-level arg
+    # and removes it from the config body.
+    mock_sync_client = MagicMock()
+    mock_sync_client.runs.stream.return_value = []
+    remote_pregel = RemoteGraph("test_graph_id", sync_client=mock_sync_client)
+
+    config = {"configurable": {"thread_id": "thread_1"}}
+    remote_pregel.invoke(
+        {"input": {"messages": [{"type": "human", "content": "hello"}]}}, config
+    )
+
+    assert mock_sync_client.runs.stream.called
+    _, kwargs = mock_sync_client.runs.stream.call_args
+    assert kwargs.get("thread_id") == "thread_1"
+    passed_config = kwargs.get("config") or {}
+    assert "configurable" in passed_config
+    assert "thread_id" not in passed_config["configurable"]
+    assert not passed_config["configurable"]
+
+
+def test_stream_sanitizes_thread_id():
+    # Ensure that streaming with thread_id passes thread_id as a top-level arg
+    # and removes it from the config body.
+    mock_sync_client = MagicMock()
+    mock_sync_client.runs.stream.return_value = []
+    remote_pregel = RemoteGraph("test_graph_id", sync_client=mock_sync_client)
+
+    config = {"configurable": {"thread_id": "thread_2"}}
+    list(remote_pregel.stream({"input": {"messages": []}}, config))
+
+    assert mock_sync_client.runs.stream.called
+    _, kwargs = mock_sync_client.runs.stream.call_args
+    assert kwargs.get("thread_id") == "thread_2"
+    passed_config = kwargs.get("config") or {}
+    assert "configurable" in passed_config
+    assert "thread_id" not in passed_config["configurable"]
+    assert not passed_config["configurable"]
+
+
 @pytest.mark.anyio
 async def test_ainvoke():
     # set up test
@@ -879,8 +919,8 @@ async def test_langgraph_cloud_integration():
     from langgraph.graph import END, START, MessagesState, StateGraph
 
     # create RemotePregel instance
-    client = get_client()
-    sync_client = get_sync_client()
+    client = get_client(url="http://localhost:8123")
+    sync_client = get_sync_client(url="http://localhost:8123")
     remote_pregel = RemoteGraph(
         "agent",
         client=client,

libs/prebuilt/.claude/settings.local.json

@@ -0,0 +1,21 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(make test:*)",
+      "Bash(uv run pytest:*)",
+      "Bash(LANGGRAPH_TEST_FAST=0 make start-services:*)",
+      "Bash(LANGGRAPH_TEST_FAST=0 uv run:*)",
+      "Bash(EXIT_CODE=$?)",
+      "Bash(make stop-services:*)",
+      "Bash(exit $EXIT_CODE)",
+      "Read(//Users/sydney_runkle/oss/langgraph/**)",
+      "Bash(python3:*)",
+      "Bash(find:*)",
+      "Bash(python -m pytest:*)",
+      "Bash(python:*)",
+      "Read(//tmp/**)"
+    ],
+    "deny": [],
+    "ask": []
+  }
+}

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -63,7 +63,7 @@ class AgentState(TypedDict):
 
 
 @deprecated(
-    "AgentStatePydantic has been moved to `langchain.agents`. Please update your import to `from langchain.agents import AgentStatePydantic`.",
+    "AgentStatePydantic has been deprecated in favor of AgentState in `langchain.agents`.",
     category=LangGraphDeprecatedSinceV10,
 )
 class AgentStatePydantic(BaseModel):
@@ -78,11 +78,11 @@ with warnings.catch_warnings():
     warnings.filterwarnings(
         "ignore",
         category=LangGraphDeprecatedSinceV10,
-        message="AgentState has been moved to langchain.agents.*",
+        message="AgentState has been moved to `langchain.agents`.*",
     )
 
     @deprecated(
-        "AgentStateWithStructuredResponse has been moved to `langchain.agents`. Please update your import to `from langchain.agents import AgentStateWithStructuredResponse`.",
+        "AgentStateWithStructuredResponse has been deprecated in favor of AgentState in `langchain.agents`.",
         category=LangGraphDeprecatedSinceV10,
     )
     class AgentStateWithStructuredResponse(AgentState):
@@ -95,11 +95,11 @@ with warnings.catch_warnings():
     warnings.filterwarnings(
         "ignore",
         category=LangGraphDeprecatedSinceV10,
-        message="AgentStatePydantic has been moved to langchain.agents.*",
+        message="AgentStatePydantic has been deprecated in favor of AgentState in `langchain.agents`.",
     )
 
     @deprecated(
-        "AgentStateWithStructuredResponsePydantic has been moved to `langchain.agents`. Please update your import to `from langchain.agents import AgentStateWithStructuredResponsePydantic`.",
+        "AgentStateWithStructuredResponsePydantic has been deprecated in favor of AgentState in `langchain.agents`.",
         category=LangGraphDeprecatedSinceV10,
     )
     class AgentStateWithStructuredResponsePydantic(AgentStatePydantic):
@@ -309,37 +309,40 @@ def create_react_agent(
         model: The language model for the agent. Supports static and dynamic
             model selection.
 
-            - **Static model**: A chat model instance (e.g., `ChatOpenAI()`) or
-              string identifier (e.g., `"openai:gpt-4"`)
+            - **Static model**: A chat model instance (e.g.,
+                [`ChatOpenAI`][langchain_openai.ChatOpenAI]) or string identifier (e.g.,
+                `"openai:gpt-4"`)
             - **Dynamic model**: A callable with signature
-              `(state, runtime) -> BaseChatModel` that returns different models
-              based on runtime context
-              If the model has tools bound via `.bind_tools()` or other configurations,
-              the return type should be a Runnable[LanguageModelInput, BaseMessage]
-              Coroutines are also supported, allowing for asynchronous model selection.
+                `(state, runtime) -> BaseChatModel` that returns different models
+                based on runtime context
+
+                If the model has tools bound via `bind_tools` or other configurations,
+                the return type should be a `Runnable[LanguageModelInput, BaseMessage]`
+                Coroutines are also supported, allowing for asynchronous model selection.
 
             Dynamic functions receive graph state and runtime, enabling
             context-dependent model selection. Must return a `BaseChatModel`
             instance. For tool calling, bind tools using `.bind_tools()`.
             Bound tools must be a subset of the `tools` parameter.
 
-            Dynamic model example:
-            ```python
-            from dataclasses import dataclass
+            !!! example "Dynamic model"
 
-            @dataclass
-            class ModelContext:
-                model_name: str = "gpt-3.5-turbo"
+                ```python
+                from dataclasses import dataclass
 
-            # Instantiate models globally
-            gpt4_model = ChatOpenAI(model="gpt-4")
-            gpt35_model = ChatOpenAI(model="gpt-3.5-turbo")
+                @dataclass
+                class ModelContext:
+                    model_name: str = "gpt-3.5-turbo"
 
-            def select_model(state: AgentState, runtime: Runtime[ModelContext]) -> ChatOpenAI:
-                model_name = runtime.context.model_name
-                model = gpt4_model if model_name == "gpt-4" else gpt35_model
-                return model.bind_tools(tools)
-            ```
+                # Instantiate models globally
+                gpt4_model = ChatOpenAI(model="gpt-4")
+                gpt35_model = ChatOpenAI(model="gpt-3.5-turbo")
+
+                def select_model(state: AgentState, runtime: Runtime[ModelContext]) -> ChatOpenAI:
+                    model_name = runtime.context.model_name
+                    model = gpt4_model if model_name == "gpt-4" else gpt35_model
+                    return model.bind_tools(tools)
+                ```
 
             !!! note "Dynamic Model Requirements"
 
@@ -351,23 +354,26 @@ def create_react_agent(
             If an empty list is provided, the agent will consist of a single LLM node without tool calling.
         prompt: An optional prompt for the LLM. Can take a few different forms:
 
-            - str: This is converted to a SystemMessage and added to the beginning of the list of messages in state["messages"].
-            - SystemMessage: this is added to the beginning of the list of messages in state["messages"].
-            - Callable: This function should take in full graph state and the output is then passed to the language model.
-            - Runnable: This runnable should take in full graph state and the output is then passed to the language model.
+            - `str`: This is converted to a `SystemMessage` and added to the beginning of the list of messages in `state["messages"]`.
+            - `SystemMessage`: this is added to the beginning of the list of messages in `state["messages"]`.
+            - `Callable`: This function should take in full graph state and the output is then passed to the language model.
+            - `Runnable`: This runnable should take in full graph state and the output is then passed to the language model.
 
         response_format: An optional schema for the final agent output.
 
             If provided, output will be formatted to match the given schema and returned in the 'structured_response' state key.
+
             If not provided, `structured_response` will not be present in the output state.
+
             Can be passed in as:
 
-                - an OpenAI function/tool schema,
-                - a JSON Schema,
-                - a TypedDict class,
-                - or a Pydantic class.
-                - a tuple (prompt, schema), where schema is one of the above.
-                    The prompt will be used together with the model that is being used to generate the structured response.
+            - An OpenAI function/tool schema,
+            - A JSON Schema,
+            - A TypedDict class,
+            - A Pydantic class.
+            - A tuple `(prompt, schema)`, where schema is one of the above.
+                The prompt will be used together with the model that is being used to
+                generate the structured response.
 
             !!! Important
                 `response_format` requires the model to support `.with_structured_output`
@@ -428,13 +434,16 @@ def create_react_agent(
         store: An optional store object. This is used for persisting data
             across multiple threads (e.g., multiple conversations / users).
         interrupt_before: An optional list of node names to interrupt before.
-            Should be one of the following: "agent", "tools".
+            Should be one of the following: `"agent"`, `"tools"`.
+
             This is useful if you want to add a user confirmation or other interrupt before taking an action.
         interrupt_after: An optional list of node names to interrupt after.
-            Should be one of the following: "agent", "tools".
+            Should be one of the following: `"agent"`, `"tools"`.
+
             This is useful if you want to return directly or run additional processing on an output.
         debug: A flag indicating whether to enable debug mode.
         version: Determines the version of the graph to create.
+
             Can be one of:
 
             - `"v1"`: The tool node processes a single message. All tool
@@ -443,7 +452,7 @@ def create_react_agent(
                 Tool calls are distributed across multiple instances of the tool
                 node using the [Send](https://langchain-ai.github.io/langgraph/concepts/low_level/#send)
                 API.
-        name: An optional name for the CompiledStateGraph.
+        name: An optional name for the `CompiledStateGraph`.
             This name will be automatically used when adding ReAct agent graph to another graph as a subgraph node -
             particularly useful for building multi-agent systems.
 
@@ -453,14 +462,14 @@ def create_react_agent(
 
 
     Returns:
-        A compiled LangChain runnable that can be used for chat interactions.
+        A compiled LangChain `Runnable` that can be used for chat interactions.
 
     The "agent" node calls the language model with the messages list (after applying the prompt).
     If the resulting AIMessage contains `tool_calls`, the graph will then call the ["tools"][langgraph.prebuilt.tool_node.ToolNode].
     The "tools" node executes the tools (1 tool per `tool_call`) and adds the responses to the messages list
     as `ToolMessage` objects. The agent node then calls the language model again.
     The process repeats until no more `tool_calls` are present in the response.
-    The agent then returns the full list of messages as a dictionary containing the key "messages".
+    The agent then returns the full list of messages as a dictionary containing the key `'messages'`.
 
     ``` mermaid
         sequenceDiagram

libs/prebuilt/langgraph/prebuilt/interrupt.py

@@ -36,7 +36,7 @@ class ActionRequest(TypedDict):
     Contains the action type and any associated arguments needed for the action.
 
     Attributes:
-        action: The type or name of action being requested (e.g., "Approve XYZ action")
+        action: The type or name of action being requested (e.g., `"Approve XYZ action"`)
         args: Key-value pairs of arguments needed for the action
     """
 
@@ -89,14 +89,16 @@ class HumanResponse(TypedDict):
 
     Attributes:
         type: The type of response:
-            - "accept": Approves the current state without changes
-            - "ignore": Skips/ignores the current step
-            - "response": Provides text feedback or instructions
-            - "edit": Modifies the current state/content
+
+            - `'accept'`: Approves the current state without changes
+            - `'ignore'`: Skips/ignores the current step
+            - `'response'`: Provides text feedback or instructions
+            - `'edit'`: Modifies the current state/content
         args: The response payload:
-            - None: For ignore/accept actions
-            - str: For text responses
-            - ActionRequest: For edit actions with updated content
+
+            - `None`: For ignore/accept actions
+            - `str`: For text responses
+            - `ActionRequest`: For edit actions with updated content
     """
 
     type: Literal["accept", "ignore", "response", "edit"]

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -6,6 +6,7 @@ Tools are functions that models can call to interact with external systems,
 APIs, databases, or perform computations.
 
 The module implements design patterns for:
+
 - Parallel execution of multiple tool calls for efficiency
 - Robust error handling with customizable error messages
 - State injection for tools that need access to graph state
@@ -13,11 +14,13 @@ The module implements design patterns for:
 - Command-based state updates for advanced control flow
 
 Key Components:
-    `ToolNode`: Main class for executing tools in LangGraph workflows
-    `InjectedState`: Annotation for injecting graph state into tools
-    `InjectedStore`: Annotation for injecting persistent store into tools
-    `ToolRuntime`: Runtime information for tools, bundling together state, context, config, stream_writer, tool_call_id, and store
-    `tools_condition`: Utility function for conditional routing based on tool calls
+
+- `ToolNode`: Main class for executing tools in LangGraph workflows
+- `InjectedState`: Annotation for injecting graph state into tools
+- `InjectedStore`: Annotation for injecting persistent store into tools
+- `ToolRuntime`: Runtime information for tools, bundling together `state`, `context`,
+    `config`, `stream_writer`, `tool_call_id`, and `store`
+- `tools_condition`: Utility function for conditional routing based on tool calls
 
 Typical Usage:
     ```python
@@ -139,6 +142,25 @@ class ToolCallRequest:
     state: Any
     runtime: ToolRuntime
 
+    def __setattr__(self, name: str, value: Any) -> None:
+        """Raise deprecation warning when setting attributes directly.
+
+        Direct attribute assignment is deprecated. Use the `override()` method instead.
+        """
+        import warnings
+
+        # Allow setting attributes during initialization
+        if not hasattr(self, "__dataclass_fields__") or not hasattr(self, name):
+            object.__setattr__(self, name, value)
+        else:
+            warnings.warn(
+                f"Setting attribute '{name}' on ToolCallRequest is deprecated. "
+                "Use the override() method instead to create a new instance with modified values.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            object.__setattr__(self, name, value)
+
     def override(
         self, **overrides: Unpack[_ToolCallRequestOverrides]
     ) -> ToolCallRequest:
@@ -199,8 +221,9 @@ Examples:
 
     ```python
     def handler(request, execute):
-        request.tool_call["args"]["value"] *= 2
-        return execute(request)
+        modified_call = {**request.tool_call, "args": {**request.tool_call["args"], "value": request.tool_call["args"]["value"] * 2}}
+        modified_request = request.override(tool_call=modified_call)
+        return execute(modified_request)
     ```
 
     Retry on error (execute multiple times):
@@ -476,9 +499,7 @@ def _infer_handled_types(handler: Callable[..., str]) -> tuple[type[Exception],
 
 def _filter_validation_errors(
     validation_error: ValidationError,
-    tool_to_state_args: dict[str, str | None],
-    tool_to_store_arg: str | None,
-    tool_to_runtime_arg: str | None,
+    injected_args: _InjectedArgs | None,
 ) -> list[ErrorDetails]:
     """Filter validation errors to only include LLM-controlled arguments.
 
@@ -493,25 +514,28 @@ def _filter_validation_errors(
 
     Args:
         validation_error: The Pydantic ValidationError raised during tool invocation.
-        tool_to_state_args: Mapping of state argument names to state field names.
-        tool_to_store_arg: Name of the store argument, if any.
-        tool_to_runtime_arg: Name of the runtime argument, if any.
+        injected_args: The _InjectedArgs structure containing all injected arguments,
+            or None if there are no injected arguments.
 
     Returns:
         List of ErrorDetails containing only errors for LLM-controlled arguments,
         with system-injected argument values removed from the input field.
     """
-    injected_args = set(tool_to_state_args.keys())
-    if tool_to_store_arg:
-        injected_args.add(tool_to_store_arg)
-    if tool_to_runtime_arg:
-        injected_args.add(tool_to_runtime_arg)
+    # Collect all injected argument names
+    injected_arg_names: set[str] = set()
+    if injected_args:
+        if injected_args.state:
+            injected_arg_names.update(injected_args.state.keys())
+        if injected_args.store:
+            injected_arg_names.add(injected_args.store)
+        if injected_args.runtime:
+            injected_arg_names.add(injected_args.runtime)
 
     filtered_errors: list[ErrorDetails] = []
     for error in validation_error.errors():
         # Check if error location contains any injected argument
         # error['loc'] is a tuple like ('field_name',) or ('field_name', 'nested_field')
-        if error["loc"] and error["loc"][0] not in injected_args:
+        if error["loc"] and error["loc"][0] not in injected_arg_names:
             # Create a copy of the error dict to avoid mutating the original
             error_copy: dict[str, Any] = {**error}
 
@@ -519,7 +543,7 @@ def _filter_validation_errors(
             if isinstance(error_copy.get("input"), dict):
                 input_dict = error_copy["input"]
                 input_copy = {
-                    k: v for k, v in input_dict.items() if k not in injected_args
+                    k: v for k, v in input_dict.items() if k not in injected_arg_names
                 }
                 error_copy["input"] = input_copy
 
@@ -529,6 +553,60 @@ def _filter_validation_errors(
     return filtered_errors
 
 
+@dataclass
+class _InjectedArgs:
+    """Internal structure for tracking injected arguments for a tool.
+
+    This data structure is built once during ToolNode initialization by analyzing
+    the tool's signature and args schema, then reused during execution for efficient
+    injection without repeated reflection.
+
+    The structure maps from tool parameter names to their injection sources, enabling
+    the ToolNode to know exactly which arguments need to be injected and where to
+    get their values from.
+
+    Attributes:
+        state: Mapping from tool parameter names to state field names for injection.
+            Keys are tool parameter names, values are either:
+            - str: Name of the state field to extract and inject
+            - None: Inject the entire state object
+            Empty dict if no state injection is needed.
+        store: Name of the tool parameter where the store should be injected,
+            or None if no store injection is needed.
+        runtime: Name of the tool parameter where the runtime should be injected,
+            or None if no runtime injection is needed.
+
+    Example:
+        For a tool with signature:
+        ```python
+        def my_tool(
+            x: int,
+            messages: Annotated[list, InjectedState("messages")],
+            full_state: Annotated[dict, InjectedState()],
+            store: Annotated[BaseStore, InjectedStore()],
+            runtime: ToolRuntime,
+        ) -> str:
+            ...
+        ```
+
+        The resulting `_InjectedArgs` would be:
+        ```python
+        _InjectedArgs(
+            state={
+                "messages": "messages",  # Extract state["messages"]
+                "full_state": None,      # Inject entire state
+            },
+            store="store",               # Inject into "store" parameter
+            runtime="runtime",           # Inject into "runtime" parameter
+        )
+        ```
+    """
+
+    state: dict[str, str | None]
+    store: str | None
+    runtime: str | None
+
+
 class ToolNode(RunnableCallable):
     """A node for executing tools in LangGraph workflows.
 
@@ -552,44 +630,52 @@ class ToolNode(RunnableCallable):
         Output format depends on input type and tool behavior:
 
         **For Regular tools**:
+
         - Dict input → `{"messages": [ToolMessage(...)]}`
         - List input → `[ToolMessage(...)]`
 
         **For Command tools**:
+
         - Returns `[Command(...)]` or mixed list with regular tool outputs
-        - Commands can update state, trigger navigation, or send messages
+        - `Command` can update state, trigger navigation, or send messages
 
     Args:
-        tools: A sequence of tools that can be invoked by this node. Supports:
+        tools: A sequence of tools that can be invoked by this node.
+
+            Supports:
+
             - **BaseTool instances**: Tools with schemas and metadata
             - **Plain functions**: Automatically converted to tools with inferred schemas
+
         name: The name identifier for this node in the graph. Used for debugging
-            and visualization. Defaults to "tools".
+            and visualization.
         tags: Optional metadata tags to associate with the node for filtering
-            and organization. Defaults to `None`.
+            and organization.
         handle_tool_errors: Configuration for error handling during tool execution.
             Supports multiple strategies:
 
-            - **True**: Catch all errors and return a ToolMessage with the default
+            - `True`: Catch all errors and return a `ToolMessage` with the default
                 error template containing the exception details.
-            - **str**: Catch all errors and return a ToolMessage with this custom
+            - `str`: Catch all errors and return a `ToolMessage` with this custom
                 error message string.
-            - **type[Exception]**: Only catch exceptions with the specified type and
+            - `type[Exception]`: Only catch exceptions with the specified type and
                 return the default error message for it.
-            - **tuple[type[Exception], ...]**: Only catch exceptions with the specified
+            - `tuple[type[Exception], ...]`: Only catch exceptions with the specified
                 types and return default error messages for them.
-            - **Callable[..., str]**: Catch exceptions matching the callable's signature
+            - `Callable[..., str]`: Catch exceptions matching the callable's signature
                 and return the string result of calling it with the exception.
-            - **False**: Disable error handling entirely, allowing exceptions to
+            - `False`: Disable error handling entirely, allowing exceptions to
                 propagate.
 
             Defaults to a callable that:
-                - catches tool invocation errors (due to invalid arguments provided by the model) and returns a descriptive error message
-                - ignores tool execution errors (they will be re-raised)
+
+            - Catches tool invocation errors (due to invalid arguments provided by the
+                model) and returns a descriptive error message
+            - Ignores tool execution errors (they will be re-raised)
 
         messages_key: The key in the state dictionary that contains the message list.
             This same key will be used for the output `ToolMessage` objects.
-            Defaults to "messages".
+
             Allows custom state schemas with different message field names.
 
     Examples:
@@ -665,9 +751,7 @@ class ToolNode(RunnableCallable):
         """
         super().__init__(self._func, self._afunc, name=name, tags=tags, trace=False)
         self._tools_by_name: dict[str, BaseTool] = {}
-        self._tool_to_state_args: dict[str, dict[str, str | None]] = {}
-        self._tool_to_store_arg: dict[str, str | None] = {}
-        self._tool_to_runtime_arg: dict[str, str | None] = {}
+        self._injected_args: dict[str, _InjectedArgs] = {}
         self._handle_tool_errors = handle_tool_errors
         self._messages_key = messages_key
         self._wrap_tool_call = wrap_tool_call
@@ -678,9 +762,8 @@ class ToolNode(RunnableCallable):
             else:
                 tool_ = tool
             self._tools_by_name[tool_.name] = tool_
-            self._tool_to_state_args[tool_.name] = _get_state_args(tool_)
-            self._tool_to_store_arg[tool_.name] = _get_store_arg(tool_)
-            self._tool_to_runtime_arg[tool_.name] = _get_runtime_arg(tool_)
+            # Build injected args mapping once during initialization in a single pass
+            self._injected_args[tool_.name] = _get_all_injected_args(tool_)
 
     @property
     def tools_by_name(self) -> dict[str, BaseTool]:
@@ -833,12 +916,8 @@ class ToolNode(RunnableCallable):
                 response = tool.invoke(call_args, config)
             except ValidationError as exc:
                 # Filter out errors for injected arguments
-                filtered_errors = _filter_validation_errors(
-                    exc,
-                    self._tool_to_state_args.get(call["name"], {}),
-                    self._tool_to_store_arg.get(call["name"]),
-                    self._tool_to_runtime_arg.get(call["name"]),
-                )
+                injected = self._injected_args.get(call["name"])
+                filtered_errors = _filter_validation_errors(exc, injected)
                 # Use original call["args"] without injected values for error reporting
                 raise ToolInvocationError(
                     call["name"], exc, call["args"], filtered_errors
@@ -990,12 +1069,8 @@ class ToolNode(RunnableCallable):
                 response = await tool.ainvoke(call_args, config)
             except ValidationError as exc:
                 # Filter out errors for injected arguments
-                filtered_errors = _filter_validation_errors(
-                    exc,
-                    self._tool_to_state_args.get(call["name"], {}),
-                    self._tool_to_store_arg.get(call["name"]),
-                    self._tool_to_runtime_arg.get(call["name"]),
-                )
+                injected = self._injected_args.get(call["name"])
+                filtered_errors = _filter_validation_errors(exc, injected)
                 # Use original call["args"] without injected values for error reporting
                 raise ToolInvocationError(
                     call["name"], exc, call["args"], filtered_errors
@@ -1188,86 +1263,6 @@ class ToolNode(RunnableCallable):
             return input["state"]
         return input
 
-    def _inject_state(
-        self,
-        tool_call: ToolCall,
-        state: list[AnyMessage] | dict[str, Any] | BaseModel,
-    ) -> ToolCall:
-        state_args = self._tool_to_state_args[tool_call["name"]]
-
-        if state_args and isinstance(state, list):
-            required_fields = list(state_args.values())
-            if (
-                len(required_fields) == 1 and required_fields[0] == self._messages_key
-            ) or required_fields[0] is None:
-                state = {self._messages_key: state}
-            else:
-                err_msg = (
-                    f"Invalid input to ToolNode. Tool {tool_call['name']} requires "
-                    f"graph state dict as input."
-                )
-                if any(state_field for state_field in state_args.values()):
-                    required_fields_str = ", ".join(f for f in required_fields if f)
-                    err_msg += f" State should contain fields {required_fields_str}."
-                raise ValueError(err_msg)
-
-        if isinstance(state, dict):
-            tool_state_args = {
-                tool_arg: state[state_field] if state_field else state
-                for tool_arg, state_field in state_args.items()
-            }
-        else:
-            tool_state_args = {
-                tool_arg: getattr(state, state_field) if state_field else state
-                for tool_arg, state_field in state_args.items()
-            }
-
-        tool_call["args"] = {
-            **tool_call["args"],
-            **tool_state_args,
-        }
-        return tool_call
-
-    def _inject_store(self, tool_call: ToolCall, store: BaseStore | None) -> ToolCall:
-        store_arg = self._tool_to_store_arg[tool_call["name"]]
-        if not store_arg:
-            return tool_call
-
-        if store is None:
-            msg = (
-                "Cannot inject store into tools with InjectedStore annotations - "
-                "please compile your graph with a store."
-            )
-            raise ValueError(msg)
-
-        tool_call["args"] = {
-            **tool_call["args"],
-            store_arg: store,
-        }
-        return tool_call
-
-    def _inject_runtime(
-        self, tool_call: ToolCall, tool_runtime: ToolRuntime
-    ) -> ToolCall:
-        """Inject ToolRuntime into tool call arguments.
-
-        Args:
-            tool_call: The tool call to inject runtime into.
-            tool_runtime: The ToolRuntime instance to inject.
-
-        Returns:
-            The tool call with runtime injected if needed.
-        """
-        runtime_arg = self._tool_to_runtime_arg.get(tool_call["name"])
-        if not runtime_arg:
-            return tool_call
-
-        tool_call["args"] = {
-            **tool_call["args"],
-            runtime_arg: tool_runtime,
-        }
-        return tool_call
-
     def _inject_tool_args(
         self,
         tool_call: ToolCall,
@@ -1306,12 +1301,64 @@ class ToolNode(RunnableCallable):
         if tool_call["name"] not in self.tools_by_name:
             return tool_call
 
+        injected = self._injected_args.get(tool_call["name"])
+        if not injected:
+            return tool_call
+
         tool_call_copy: ToolCall = copy(tool_call)
-        tool_call_with_state = self._inject_state(tool_call_copy, tool_runtime.state)
-        tool_call_with_store = self._inject_store(
-            tool_call_with_state, tool_runtime.store
-        )
-        return self._inject_runtime(tool_call_with_store, tool_runtime)
+        injected_args = {}
+
+        # Inject state
+        if injected.state:
+            state = tool_runtime.state
+            # Handle list state by converting to dict
+            if isinstance(state, list):
+                required_fields = list(injected.state.values())
+                if (
+                    len(required_fields) == 1
+                    and required_fields[0] == self._messages_key
+                ) or required_fields[0] is None:
+                    state = {self._messages_key: state}
+                else:
+                    err_msg = (
+                        f"Invalid input to ToolNode. Tool {tool_call['name']} requires "
+                        f"graph state dict as input."
+                    )
+                    if any(state_field for state_field in injected.state.values()):
+                        required_fields_str = ", ".join(f for f in required_fields if f)
+                        err_msg += (
+                            f" State should contain fields {required_fields_str}."
+                        )
+                    raise ValueError(err_msg)
+
+            # Extract state values
+            if isinstance(state, dict):
+                for tool_arg, state_field in injected.state.items():
+                    injected_args[tool_arg] = (
+                        state[state_field] if state_field else state
+                    )
+            else:
+                for tool_arg, state_field in injected.state.items():
+                    injected_args[tool_arg] = (
+                        getattr(state, state_field) if state_field else state
+                    )
+
+        # Inject store
+        if injected.store:
+            if tool_runtime.store is None:
+                msg = (
+                    "Cannot inject store into tools with InjectedStore annotations - "
+                    "please compile your graph with a store."
+                )
+                raise ValueError(msg)
+            injected_args[injected.store] = tool_runtime.store
+
+        # Inject runtime
+        if injected.runtime:
+            injected_args[injected.runtime] = tool_runtime
+
+        tool_call_copy["args"] = {**tool_call_copy["args"], **injected_args}
+        return tool_call_copy
 
     def _validate_tool_command(
         self,
@@ -1393,7 +1440,7 @@ def tools_condition(
     """Conditional routing function for tool-calling workflows.
 
     This utility function implements the standard conditional logic for ReAct-style
-    agents: if the last AI message contains tool calls, route to the tool execution
+    agents: if the last `AIMessage` contains tool calls, route to the tool execution
     node; otherwise, end the workflow. This pattern is fundamental to most tool-calling
     agent architectures.
 
@@ -1402,16 +1449,15 @@ def tools_condition(
 
     Args:
         state: The current graph state to examine for tool calls. Supported formats:
-            - Dictionary containing a messages key (for StateGraph)
-            - BaseModel instance with a messages attribute
+            - Dictionary containing a messages key (for `StateGraph`)
+            - `BaseModel` instance with a messages attribute
         messages_key: The key or attribute name containing the message list in the state.
             This allows customization for graphs using different state schemas.
-            Defaults to "messages".
 
     Returns:
-        Either "tools" if tool calls are present in the last AI message, or "__end__"
-        to terminate the workflow. These are the standard routing destinations for
-        tool-calling conditional edges.
+        Either `'tools'` if tool calls are present in the last `AIMessage`, or `'__end__'`
+            to terminate the workflow. These are the standard routing destinations for
+            tool-calling conditional edges.
 
     Raises:
         ValueError: If no messages can be found in the provided state format.
@@ -1608,7 +1654,7 @@ class InjectedStore(InjectedToolArg):
 
     This annotation enables tools to access LangGraph's persistent storage system
     without exposing storage details to the language model. Tools annotated with
-    InjectedStore receive the store instance automatically during execution while
+    `InjectedStore` receive the store instance automatically during execution while
     remaining invisible to the model's tool-calling interface.
 
     The store provides persistent, cross-session data storage that tools can use
@@ -1705,120 +1751,86 @@ def _is_injection(
     return False
 
 
-def _get_state_args(tool: BaseTool) -> dict[str, str | None]:
-    """Extract state injection mappings from tool annotations.
-
-    This function analyzes a tool's input schema to identify arguments that should
-    be injected with graph state. It processes InjectedState annotations to build
-    a mapping of tool argument names to state field names.
+def _get_injection_from_type(
+    type_: Any, injection_type: type[InjectedState | InjectedStore | ToolRuntime]
+) -> Any | None:
+    """Extract injection instance from a type annotation.
 
     Args:
-        tool: The tool to analyze for state injection requirements.
+        type_: The type annotation to check.
+        injection_type: The injection type to look for.
 
     Returns:
-        A dictionary mapping tool argument names to state field names. If a field
-        name is None, the entire state should be injected for that argument.
+        The injection instance if found, True if injection marker found without instance, None otherwise.
     """
-    full_schema = tool.get_input_schema()
-    tool_args_to_state_fields: dict = {}
+    type_args = get_args(type_)
+    matches = [arg for arg in type_args if _is_injection(arg, injection_type)]
 
-    for name, type_ in get_all_basemodel_annotations(full_schema).items():
-        injections = [
-            type_arg
-            for type_arg in get_args(type_)
-            if _is_injection(type_arg, InjectedState)
-        ]
-        if len(injections) > 1:
-            msg = (
-                "A tool argument should not be annotated with InjectedState more than "
-                f"once. Received arg {name} with annotations {injections}."
-            )
-            raise ValueError(msg)
-        if len(injections) == 1:
-            injection = injections[0]
-            if isinstance(injection, InjectedState) and injection.field:
-                tool_args_to_state_fields[name] = injection.field
-            else:
-                tool_args_to_state_fields[name] = None
-        else:
-            pass
-    return tool_args_to_state_fields
+    if len(matches) > 1:
+        msg = (
+            f"A tool argument should not be annotated with {injection_type.__name__} "
+            f"more than once. Found: {matches}"
+        )
+        raise ValueError(msg)
 
-
-def _get_store_arg(tool: BaseTool) -> str | None:
-    """Extract store injection argument from tool annotations.
-
-    This function analyzes a tool's input schema to identify the argument that
-    should be injected with the graph store. Only one store argument is supported
-    per tool.
-
-    Args:
-        tool: The tool to analyze for store injection requirements.
-
-    Returns:
-        The name of the argument that should receive the store injection, or None
-        if no store injection is required.
-
-    Raises:
-        ValueError: If a tool argument has multiple InjectedStore annotations.
-    """
-    full_schema = tool.get_input_schema()
-    for name, type_ in get_all_basemodel_annotations(full_schema).items():
-        injections = [
-            type_arg
-            for type_arg in get_args(type_)
-            if _is_injection(type_arg, InjectedStore)
-        ]
-        if len(injections) > 1:
-            msg = (
-                "A tool argument should not be annotated with InjectedStore more than "
-                f"once. Received arg {name} with annotations {injections}."
-            )
-            raise ValueError(msg)
-        if len(injections) == 1:
-            return name
+    if len(matches) == 1:
+        return matches[0]
+    elif _is_injection(type_, injection_type):
+        return True
 
     return None
 
 
-def _get_runtime_arg(tool: BaseTool) -> str | None:
-    """Extract runtime injection argument from tool annotations.
+def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
+    """Extract all injected arguments from tool in a single pass.
 
-    This function analyzes a tool's input schema to identify the argument that
-    should be injected with the ToolRuntime instance. Only one runtime argument
-    is supported per tool.
+    This function analyzes both the tool's input schema and function signature
+    to identify all arguments that should be injected (state, store, runtime).
 
     Args:
-        tool: The tool to analyze for runtime injection requirements.
+        tool: The tool to analyze for injection requirements.
 
     Returns:
-        The name of the argument that should receive the runtime injection, or None
-        if no runtime injection is required.
-
-    Raises:
-        ValueError: If a tool argument has multiple ToolRuntime annotations.
+        _InjectedArgs structure containing all detected injections.
     """
+    # Get annotations from both schema and function signature
     full_schema = tool.get_input_schema()
-    for name, type_ in get_all_basemodel_annotations(full_schema).items():
-        # Check if the parameter name is "runtime" (regardless of type)
+    schema_annotations = get_all_basemodel_annotations(full_schema)
+
+    func = getattr(tool, "func", None) or getattr(tool, "coroutine", None)
+    func_annotations = get_type_hints(func, include_extras=True) if func else {}
+
+    # Combine both annotation sources, preferring schema annotations
+    # In the future, we might want to add more restrictions here...
+    all_annotations = {**func_annotations, **schema_annotations}
+
+    # Track injected args
+    state_args: dict[str, str | None] = {}
+    store_arg: str | None = None
+    runtime_arg: str | None = None
+
+    for name, type_ in all_annotations.items():
+        # Check for runtime (special case: parameter named "runtime")
         if name == "runtime":
-            return name
-        # Check if the type itself is ToolRuntime (direct usage)
-        if _is_injection(type_, ToolRuntime):
-            return name
-        # Check if ToolRuntime is in Annotated args
-        injections = [
-            type_arg
-            for type_arg in get_args(type_)
-            if _is_injection(type_arg, ToolRuntime)
-        ]
-        if len(injections) > 1:
-            msg = (
-                "A tool argument should not be annotated with ToolRuntime more than "
-                f"once. Received arg {name} with annotations {injections}."
-            )
-            raise ValueError(msg)
-        if len(injections) == 1:
-            return name
+            runtime_arg = name
 
-    return None
+        # Check for InjectedState
+        if state_inj := _get_injection_from_type(type_, InjectedState):
+            if isinstance(state_inj, InjectedState) and state_inj.field:
+                state_args[name] = state_inj.field
+            else:
+                state_args[name] = None
+
+        # Check for InjectedStore
+        if _get_injection_from_type(type_, InjectedStore):
+            store_arg = name
+
+        # Check for ToolRuntime
+        if _get_injection_from_type(type_, ToolRuntime):
+            runtime_arg = name
+
+    return _InjectedArgs(
+        state=state_args,
+        store=store_arg,
+        runtime=runtime_arg,
+    )

libs/prebuilt/langgraph/prebuilt/tool_validator.py

@@ -45,9 +45,9 @@ def _default_format_error(
     category=LangGraphDeprecatedSinceV10,
 )
 class ValidationNode(RunnableCallable):
-    """A node that validates all tools requests from the last AIMessage.
+    """A node that validates all tools requests from the last `AIMessage`.
 
-    It can be used either in StateGraph with a "messages" key.
+    It can be used either in `StateGraph` with a `'messages'` key.
 
     !!! note
 
@@ -57,7 +57,8 @@ class ValidationNode(RunnableCallable):
         messages and tool IDs (for use in multi-turn conversations).
 
     Returns:
-        (Union[Dict[str, List[ToolMessage]], Sequence[ToolMessage]]): A list of ToolMessages with the validated content or error messages.
+        (Union[Dict[str, List[ToolMessage]], Sequence[ToolMessage]]): A list of
+            `ToolMessage` objects with the validated content or error messages.
 
     Example:
         ```python title="Example usage for re-prompting the model to generate a valid response:"

libs/prebuilt/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-prebuilt"
-version = "1.0.2"
+version = "1.0.5"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.10"
@@ -29,7 +29,10 @@ dependencies = [
 ]
 
 [project.urls]
-Repository = "https://www.github.com/langchain-ai/langgraph"
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/prebuilt"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [dependency-groups]
 test = [

libs/prebuilt/tests/test_on_tool_call.py

@@ -130,11 +130,17 @@ def test_modify_arguments() -> None:
         execute: Callable[[ToolCallRequest], ToolMessage | Command],
     ) -> ToolMessage | Command:
         """Handler that doubles the input arguments."""
-        # Modify the arguments
-        request.tool_call["args"]["a"] *= 2
-        request.tool_call["args"]["b"] *= 2
-
-        return execute(request)
+        # Modify the arguments using override method
+        modified_call = {
+            **request.tool_call,
+            "args": {
+                **request.tool_call["args"],
+                "a": request.tool_call["args"]["a"] * 2,
+                "b": request.tool_call["args"]["b"] * 2,
+            },
+        }
+        modified_request = request.override(tool_call=modified_call)
+        return execute(modified_request)
 
     tool_node = ToolNode([add], wrap_tool_call=modify_args_handler)
 
@@ -362,10 +368,17 @@ async def test_handler_with_async_execution() -> None:
         execute: Callable[[ToolCallRequest], ToolMessage | Command],
     ) -> ToolMessage | Command:
         """Handler that modifies arguments."""
-        # Add 10 to both arguments
-        request.tool_call["args"]["a"] += 10
-        request.tool_call["args"]["b"] += 10
-        return execute(request)
+        # Add 10 to both arguments using override method
+        modified_call = {
+            **request.tool_call,
+            "args": {
+                **request.tool_call["args"],
+                "a": request.tool_call["args"]["a"] + 10,
+                "b": request.tool_call["args"]["b"] + 10,
+            },
+        }
+        modified_request = request.override(tool_call=modified_call)
+        return execute(modified_request)
 
     tool_node = ToolNode([async_add], wrap_tool_call=modifying_handler)
 
@@ -1305,3 +1318,64 @@ async def test_state_extraction_with_tool_call_with_context_async() -> None:
     assert state_seen[0] == actual_state
     assert "__type" not in state_seen[0]
     assert "tool_call" not in state_seen[0]
+
+
+def test_tool_call_request_is_frozen() -> None:
+    """Test that ToolCallRequest raises deprecation warnings on direct attribute reassignment."""
+    tool_call: ToolCall = {"name": "add", "args": {"a": 1, "b": 2}, "id": "call_1"}
+    state: dict = {"messages": []}
+    runtime = None
+
+    request = ToolCallRequest(
+        tool_call=tool_call, tool=add, state=state, runtime=runtime
+    )  # type: ignore[arg-type]
+
+    # Test that direct attribute reassignment raises DeprecationWarning
+    with pytest.warns(
+        DeprecationWarning,
+        match="Setting attribute 'tool_call' on ToolCallRequest is deprecated",
+    ):
+        request.tool_call = {"name": "other", "args": {}, "id": "call_2"}  # type: ignore[misc]
+
+    with pytest.warns(
+        DeprecationWarning,
+        match="Setting attribute 'tool' on ToolCallRequest is deprecated",
+    ):
+        request.tool = None  # type: ignore[misc]
+
+    with pytest.warns(
+        DeprecationWarning,
+        match="Setting attribute 'state' on ToolCallRequest is deprecated",
+    ):
+        request.state = {}  # type: ignore[misc]
+
+    with pytest.warns(
+        DeprecationWarning,
+        match="Setting attribute 'runtime' on ToolCallRequest is deprecated",
+    ):
+        request.runtime = None  # type: ignore[misc]
+
+    # Test that override method works correctly
+    new_tool_call: ToolCall = {
+        "name": "multiply",
+        "args": {"x": 5, "y": 10},
+        "id": "call_3",
+    }
+
+    # Original request should be unchanged (note: it was modified by the warnings tests above)
+    # So we create a fresh request to test override properly
+    fresh_request = ToolCallRequest(
+        tool_call=tool_call, tool=add, state=state, runtime=runtime
+    )  # type: ignore[arg-type]
+    fresh_new_request = fresh_request.override(tool_call=new_tool_call)
+
+    # Original request should be unchanged
+    assert fresh_request.tool_call == tool_call
+    assert fresh_request.tool_call["name"] == "add"
+
+    # New request should have the updated tool_call
+    assert fresh_new_request.tool_call == new_tool_call
+    assert fresh_new_request.tool_call["name"] == "multiply"
+    assert fresh_new_request.tool == add  # Other fields should remain the same
+    assert fresh_new_request.state == state
+    assert fresh_new_request.runtime is None

libs/prebuilt/tests/test_react_agent.py

@@ -53,7 +53,6 @@ from langgraph.prebuilt.chat_agent_executor import (
 from langgraph.prebuilt.tool_node import (
     InjectedState,
     InjectedStore,
-    _get_state_args,
     _infer_handled_types,
 )
 from tests.any_str import AnyStr
@@ -639,8 +638,9 @@ def test_react_agent_parallel_tool_calls(
     for event in agent.stream(
         {"messages": [("user", query)]}, config, stream_mode="values"
     ):
-        if messages := event.get("messages"):
-            message_types.append([m.type for m in messages])
+        if "__interrupt__" not in event: 
+            if messages := event.get("messages"):
+                message_types.append([m.type for m in messages])
 
     if version == "v1":
         assert message_types == [
@@ -1084,21 +1084,6 @@ async def test_return_direct(version: str) -> None:
     ]
 
 
-def test__get_state_args() -> None:
-    class Schema1(BaseModel):
-        a: Annotated[str, InjectedState]
-
-    class Schema2(Schema1):
-        b: Annotated[int, InjectedState("bar")]
-
-    @dec_tool(args_schema=Schema2)
-    def foo(a: str, b: int) -> float:
-        """return"""
-        return 0.0
-
-    assert _get_state_args(foo) == {"a": None, "b": "bar"}
-
-
 def test_inspect_react() -> None:
     model = FakeToolCallingModel(tool_calls=[])
     agent = create_react_agent(model, [])

libs/prebuilt/tests/test_tool_node.py

@@ -42,6 +42,7 @@ from langgraph.prebuilt import (
 from langgraph.prebuilt.tool_node import (
     TOOL_CALL_ERROR_TEMPLATE,
     ToolInvocationError,
+    ToolRuntime,
     tools_condition,
 )
 
@@ -1610,3 +1611,252 @@ def test_tool_node_stream_writer() -> None:
             },
         ),
     ]
+
+
+def test_tool_call_request_setattr_deprecation_warning():
+    """Test that ToolCallRequest raises a deprecation warning on direct attribute modification."""
+    import warnings
+
+    from langgraph.prebuilt.tool_node import ToolCallRequest
+
+    # Create a mock ToolCall
+    tool_call = {"name": "test", "args": {"a": 1}, "id": "call_1", "type": "tool_call"}
+
+    # Create a ToolCallRequest
+    request = ToolCallRequest(
+        tool_call=tool_call,
+        tool=None,
+        state={"messages": []},
+        runtime=None,
+    )
+
+    # Test 1: Direct attribute assignment should raise deprecation warning but still work
+    with pytest.warns(DeprecationWarning, match="deprecated.*override"):
+        request.tool_call = {"name": "other", "args": {}, "id": "call_2"}
+
+    # Verify the attribute was actually modified
+    assert request.tool_call == {"name": "other", "args": {}, "id": "call_2"}
+
+    # Reset for further tests
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        request.tool_call = tool_call
+
+    # Test 2: override method should work without warnings
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        new_tool_call = {
+            "name": "new_tool",
+            "args": {"b": 2},
+            "id": "call_3",
+            "type": "tool_call",
+        }
+        new_request = request.override(tool_call=new_tool_call)
+
+        # Verify no warning was raised
+        assert len(w) == 0
+
+    # Verify original is unchanged
+    assert request.tool_call == tool_call
+
+    # Verify new request has updated values
+    assert new_request.tool_call == new_tool_call
+
+    # Test 3: Initialization should not trigger warning
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        ToolCallRequest(
+            tool_call=tool_call,
+            tool=None,
+            state={"messages": []},
+            runtime=None,
+        )
+        # Verify no warning was raised during initialization
+        assert len(w) == 0
+
+
+async def test_tool_node_inject_async_all_types_signature_only() -> None:
+    """Test all injection types without @tool decorator."""
+    store = InMemoryStore()
+    namespace = ("test",)
+    store.put(namespace, "test_key", {"store_data": "from_store"})
+
+    class TestState(TypedDict):
+        messages: list
+        foo: str
+        bar: int
+
+    async def comprehensive_async_tool(
+        x: int,
+        whole_state: Annotated[TestState, InjectedState],
+        foo_field: Annotated[str, InjectedState("foo")],
+        store: Annotated[BaseStore, InjectedStore()],
+        runtime: ToolRuntime,
+    ) -> str:
+        """Async tool that uses all injection types."""
+        bar_from_whole = whole_state["bar"]
+        foo_value = foo_field
+        store_val = store.get(namespace, "test_key").value["store_data"]
+        foo_from_runtime = runtime.state["foo"]
+        tool_call_id = runtime.tool_call_id
+
+        return (
+            f"x={x}, "
+            f"bar_from_whole={bar_from_whole}, "
+            f"foo_field={foo_value}, "
+            f"store={store_val}, "
+            f"foo_from_runtime={foo_from_runtime}, "
+            f"tool_call_id={tool_call_id}"
+        )
+
+    node = ToolNode([comprehensive_async_tool], handle_tool_errors=True)
+    tool_call = {
+        "name": "comprehensive_async_tool",
+        "args": {"x": 42},
+        "id": "test_call_123",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+
+    config = _create_config_with_runtime(store=store)
+    result = await node.ainvoke(
+        {"messages": [msg], "foo": "foo_value", "bar": 99}, config=config
+    )
+
+    tool_message = result["messages"][-1]
+    assert tool_message.content == (
+        "x=42, "
+        "bar_from_whole=99, "
+        "foo_field=foo_value, "
+        "store=from_store, "
+        "foo_from_runtime=foo_value, "
+        "tool_call_id=test_call_123"
+    )
+
+
+async def test_tool_node_inject_async_all_types_with_decorator() -> None:
+    """Test all injection types with @tool decorator."""
+    store = InMemoryStore()
+    namespace = ("test",)
+    store.put(namespace, "test_key", {"store_data": "from_store"})
+
+    class TestState(TypedDict):
+        messages: list
+        foo: str
+        bar: int
+
+    @dec_tool
+    async def comprehensive_async_tool(
+        x: int,
+        whole_state: Annotated[TestState, InjectedState],
+        foo_field: Annotated[str, InjectedState("foo")],
+        store: Annotated[BaseStore, InjectedStore()],
+        runtime: ToolRuntime,
+    ) -> str:
+        """Async tool that uses all injection types."""
+        bar_from_whole = whole_state["bar"]
+        foo_value = foo_field
+        store_val = store.get(namespace, "test_key").value["store_data"]
+        foo_from_runtime = runtime.state["foo"]
+        tool_call_id = runtime.tool_call_id
+
+        return (
+            f"x={x}, "
+            f"bar_from_whole={bar_from_whole}, "
+            f"foo_field={foo_value}, "
+            f"store={store_val}, "
+            f"foo_from_runtime={foo_from_runtime}, "
+            f"tool_call_id={tool_call_id}"
+        )
+
+    node = ToolNode([comprehensive_async_tool], handle_tool_errors=True)
+    tool_call = {
+        "name": "comprehensive_async_tool",
+        "args": {"x": 42},
+        "id": "test_call_456",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+
+    config = _create_config_with_runtime(store=store)
+    result = await node.ainvoke(
+        {"messages": [msg], "foo": "foo_value", "bar": 99}, config=config
+    )
+
+    tool_message = result["messages"][-1]
+    assert tool_message.content == (
+        "x=42, "
+        "bar_from_whole=99, "
+        "foo_field=foo_value, "
+        "store=from_store, "
+        "foo_from_runtime=foo_value, "
+        "tool_call_id=test_call_456"
+    )
+
+
+async def test_tool_node_inject_async_all_types_with_schema() -> None:
+    """Test all injection types with explicit schema."""
+    store = InMemoryStore()
+    namespace = ("test",)
+    store.put(namespace, "test_key", {"store_data": "from_store"})
+
+    class TestState(TypedDict):
+        messages: list
+        foo: str
+        bar: int
+
+    class ComprehensiveToolSchema(BaseModel):
+        model_config = {"arbitrary_types_allowed": True}
+        x: int
+        whole_state: Annotated[TestState, InjectedState]
+        foo_field: Annotated[str, InjectedState("foo")]
+        store: Annotated[BaseStore, InjectedStore()]
+        runtime: ToolRuntime
+
+    @dec_tool(args_schema=ComprehensiveToolSchema)
+    async def comprehensive_async_tool(
+        x: int,
+        whole_state: Annotated[TestState, InjectedState],
+        foo_field: Annotated[str, InjectedState("foo")],
+        store: Annotated[BaseStore, InjectedStore()],
+        runtime: ToolRuntime,
+    ) -> str:
+        """Async tool that uses all injection types."""
+        bar_from_whole = whole_state["bar"]
+        foo_value = foo_field
+        store_val = store.get(namespace, "test_key").value["store_data"]
+        foo_from_runtime = runtime.state["foo"]
+        tool_call_id = runtime.tool_call_id
+
+        return (
+            f"x={x}, "
+            f"bar_from_whole={bar_from_whole}, "
+            f"foo_field={foo_value}, "
+            f"store={store_val}, "
+            f"foo_from_runtime={foo_from_runtime}, "
+            f"tool_call_id={tool_call_id}"
+        )
+
+    node = ToolNode([comprehensive_async_tool], handle_tool_errors=True)
+    tool_call = {
+        "name": "comprehensive_async_tool",
+        "args": {"x": 42},
+        "id": "test_call_789",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+
+    config = _create_config_with_runtime(store=store)
+    result = await node.ainvoke(
+        {"messages": [msg], "foo": "foo_value", "bar": 99}, config=config
+    )
+
+    tool_message = result["messages"][-1]
+    assert tool_message.content == (
+        "x=42, "
+        "bar_from_whole=99, "
+        "foo_field=foo_value, "
+        "store=from_store, "
+        "foo_from_runtime=foo_value, "
+        "tool_call_id=test_call_789"
+    )

libs/sdk-py/Makefile

@@ -17,7 +17,7 @@ lint lint_diff:
 	uv run ruff check .
 	[ "$(PYTHON_FILES)" = "" ] || uv run ruff format $(PYTHON_FILES) --diff
 	[ "$(PYTHON_FILES)" = "" ] || uv run ruff check --select I $(PYTHON_FILES)
-	uvx ty check .
+	uv run ty check .
 
 format format_diff:
 	uv run ruff check --select I --fix $(PYTHON_FILES)

libs/sdk-py/langgraph_sdk/__init__.py

@@ -1,6 +1,8 @@
 from langgraph_sdk.auth import Auth
 from langgraph_sdk.client import get_client, get_sync_client
+from langgraph_sdk.encryption import Encryption
+from langgraph_sdk.encryption.types import EncryptionContext
 
-__version__ = "0.2.9"
+__version__ = "0.2.14"
 
-__all__ = ["Auth", "get_client", "get_sync_client"]
+__all__ = ["Auth", "Encryption", "EncryptionContext", "get_client", "get_sync_client"]

libs/sdk-py/langgraph_sdk/auth/__init__.py

@@ -90,11 +90,11 @@ class Auth:
     """
 
     __slots__ = (
-        "on",
-        "_handlers",
-        "_global_handlers",
         "_authenticate_handler",
+        "_global_handlers",
         "_handler_cache",
+        "_handlers",
+        "on",
     )
     types = types
     """Reference to auth type definitions.
@@ -259,7 +259,7 @@ class Auth:
         """
         if self._authenticate_handler is not None:
             raise ValueError(
-                "Authentication handler already set as {self._authenticate_handler}."
+                f"Authentication handler already set as {self._authenticate_handler}."
             )
         self._authenticate_handler = fn
         return fn
@@ -383,7 +383,7 @@ class _ResourceOn(typing.Generic[VCreate, VRead, VUpdate, VDelete, VSearch]):
         if fn is not None:
             _validate_handler(fn)
             return typing.cast(
-                _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch],
+                "_ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]",
                 _register_handler(self.auth, self.resource, "*", fn),
             )
 
@@ -392,7 +392,7 @@ class _ResourceOn(typing.Generic[VCreate, VRead, VUpdate, VDelete, VSearch]):
         ) -> _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]:
             _validate_handler(handler)
             return typing.cast(
-                _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch],
+                "_ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]",
                 _register_handler(self.auth, self.resource, "*", handler),
             )
 
@@ -612,10 +612,10 @@ class _On:
     __slots__ = (
         "_auth",
         "assistants",
-        "threads",
-        "runs",
         "crons",
+        "runs",
         "store",
+        "threads",
         "value",
     )
 
@@ -740,9 +740,8 @@ def is_studio_user(
 ) -> bool:
     return (
         isinstance(user, types.StudioUser)
-        or isinstance(user, dict)
-        and user.get("kind") == "StudioUser"  # ty: ignore[invalid-argument-type]
+        or (isinstance(user, dict) and user.get("kind") == "StudioUser")  # ty: ignore[invalid-argument-type]
     )
 
 
-__all__ = ["Auth", "types", "exceptions"]
+__all__ = ["Auth", "exceptions", "types"]

libs/sdk-py/langgraph_sdk/auth/types.py

@@ -243,7 +243,7 @@ class StudioUser:
         ```
     """
 
-    __slots__ = ("username", "_is_authenticated", "_permissions")
+    __slots__ = ("_is_authenticated", "_permissions", "username")
 
     def __init__(self, username: str, is_authenticated: bool = False) -> None:
         self.username = username
@@ -1093,22 +1093,22 @@ class on:
 
 
 __all__ = [
-    "on",
+    "AssistantsCreate",
+    "AssistantsDelete",
+    "AssistantsRead",
+    "AssistantsSearch",
+    "AssistantsUpdate",
     "MetadataInput",
     "RunsCreate",
-    "ThreadsCreate",
-    "ThreadsRead",
-    "ThreadsUpdate",
-    "ThreadsDelete",
-    "ThreadsSearch",
-    "AssistantsCreate",
-    "AssistantsRead",
-    "AssistantsUpdate",
-    "AssistantsDelete",
-    "AssistantsSearch",
+    "StoreDelete",
     "StoreGet",
-    "StoreSearch",
     "StoreListNamespaces",
     "StorePut",
-    "StoreDelete",
+    "StoreSearch",
+    "ThreadsCreate",
+    "ThreadsDelete",
+    "ThreadsRead",
+    "ThreadsSearch",
+    "ThreadsUpdate",
+    "on",
 ]