Update headers type

This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.

**Changes detected as of LangGraph API version 0.4.9**

This update was automatically generated by the sync workflow in the
langgraph-api repository.

Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>

.github/scripts/check_sdk_methods.py

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

.github/scripts/run_langgraph_cli_test.py

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

.github/workflows/_integration_test.yml

@@ -43,28 +43,43 @@ jobs:
       - name: Build and test service A
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples
+        env:
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
         run: |
           # The build-arg isn't used; just testing that we accept other args
-          langgraph build -t langgraph-test-a  --base-image "langchain/langgraph-trial"
-          cp .env.example .envg
+          langgraph build -t langgraph-test-a
+          cp .env.example .env
+          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
           timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -c langgraph.json -t langgraph-test-a
       - name: Build and test service B
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples/graphs
+        env:
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
         run: |
-          langgraph build -t langgraph-test-b  --base-image "langchain/langgraph-trial"
+          langgraph build -t langgraph-test-b
+          cp ../.env.example .env
+          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
           timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-b
       - name: Build and test service C
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples/graphs_reqs_a
+        env:
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
         run: |
-          langgraph build -t langgraph-test-c  --base-image "langchain/langgraph-trial"
+          langgraph build -t langgraph-test-c
+          cp ../.env.example .env
+          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
           timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-c
       - name: Build and test service D
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples/graphs_reqs_b
+        env:
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
         run: |
-          langgraph build -t langgraph-test-d  --base-image "langchain/langgraph-trial"
+          langgraph build -t langgraph-test-d
+          cp ../.env.example .env
+          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
           timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-d
 
       - name: Build JS service

.github/workflows/_test.yml

@@ -17,6 +17,7 @@ jobs:
     strategy:
       matrix:
         python-version:
+          - "3.9"
           - "3.10"
           - "3.11"
           - "3.12"

.github/workflows/_test_langgraph.yml

@@ -12,6 +12,7 @@ jobs:
     strategy:
       matrix:
         python-version:
+          - "3.9"
           - "3.10"
           - "3.11"
           - "3.12"

.github/workflows/ci.yml

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

.github/workflows/release.yml

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

.github/workflows/uv_lock_ugprade.yml

@@ -22,7 +22,7 @@ jobs:
         uses: astral-sh/setup-uv@v6
         with:
           # use minimum supported Python version
-          python-version: "3.10"
+          python-version: "3.9"
           enable-cache: true
           cache-suffix: "uv-lock-upgrade"
           

docs/_scripts/notebook_hooks.py

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

docs/docs/cloud/deployment/cloud.md

@@ -99,8 +99,8 @@ Starting from the `LangGraph Platform` view...
 1. In the top-right corner, select the gear icon (`Deployment Settings`).
 1. Update the `Git Branch` to the desired branch.
 1. Check/uncheck checkbox to `Automatically update deployment on push to branch`.
-    1. Branch creation/deletion and tag creation/deletion events will not trigger an update. Only pushes to an existing branch will trigger an update.
-    1. Pushes in quick succession to a branch will not trigger subsequent updates. In the future, this functionality may be changed/improved.
+1. Branch creation/deletion and tag creation/deletion events will not trigger an update. Only pushes to an existing branch will trigger an update.
+1. Pushes in quick succession to a branch will queue subsequent updates. Once a build completes, the most recent commit will begin building and the other queued builds will be skipped.
 
 ## Add or Remove GitHub Repositories
 

docs/docs/concepts/auth.md

@@ -112,7 +112,7 @@ auth = Auth()
 @auth.authenticate
 async def authenticate(headers: dict) -> Auth.types.MinimalUserDict:
     # Validate credentials (e.g., API key, JWT token)
-    api_key = headers.get("x-api-key")
+    api_key = headers.get(b"x-api-key")
     if not api_key or not is_valid_key(api_key):
         raise Auth.exceptions.HTTPException(
             status_code=401,

docs/docs/concepts/low_level.md

@@ -1040,7 +1040,7 @@ def node_a(state: State, runtime: Runtime[ContextSchema]):
     ...
 ```
 
-See [this guide](../how-tos/graph-api.ipynb#add-runtime-configuration) for a full breakdown on configuration.
+See [this guide](../how-tos/graph-api.md#add-runtime-configuration) for a full breakdown on configuration.
 :::
 
 :::js

docs/docs/concepts/persistence.md

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

docs/docs/concepts/plans.md

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

docs/docs/how-tos/auth/custom_auth.md

@@ -43,7 +43,7 @@ To leverage custom authentication and access user-level metadata in your deploym
 
     @auth.authenticate # (1)!
     async def authenticate(headers: dict) -> Auth.types.MinimalUserDict:
-        api_key = headers.get("x-api-key")
+        api_key = headers.get(b"x-api-key")
         if not api_key or not is_valid_key(api_key):
             raise Auth.exceptions.HTTPException(status_code=401, detail="Invalid API key")
 

docs/overrides/main.html

@@ -360,5 +360,5 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
 {% endblock %}
 
 {% block announce %}
-<strong>LangGraph Platform docs have moved!</strong> Find the LangGraph Platform docs at the new <a href="https://docs.langchain.com/langgraph-platform" target="_blank">LangChain Docs</a> site.
+Our new LangChain Academy Course Deep Research with LangGraph is now live! <a href="https://academy.langchain.com/courses/deep-research-with-langgraph/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2025_deep-research-course_co" target="_blank">Enroll for free</a>.
 {% endblock %}

libs/checkpoint/tests/test_redis_cache.py

@@ -161,9 +161,7 @@ class TestRedisCache:
     async def test_async_operations(self):
         """Test async set and get operations with sync Redis client."""
         # Create sync Redis client and cache (like main integration tests)
-        client = redis.Redis(
-            host="localhost", port=6379, db=1, decode_responses=False
-        )
+        client = redis.Redis(host="localhost", port=6379, db=1, decode_responses=False)
         try:
             client.ping()
         except Exception:
@@ -189,9 +187,7 @@ class TestRedisCache:
     async def test_async_clear(self):
         """Test async clear operations with sync Redis client."""
         # Create sync Redis client and cache (like main integration tests)
-        client = redis.Redis(
-            host="localhost", port=6379, db=1, decode_responses=False
-        )
+        client = redis.Redis(host="localhost", port=6379, db=1, decode_responses=False)
         try:
             client.ping()
         except Exception:

libs/cli/Makefile

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

libs/cli/examples/.env.example

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

libs/cli/generate_schema.py

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

libs/cli/langgraph_cli/__init__.py

@@ -0,0 +1 @@
+__version__ = "0.4.0"

libs/cli/langgraph_cli/config.py

@@ -17,6 +17,9 @@ DEFAULT_PYTHON_VERSION = "3.11"
 DEFAULT_IMAGE_DISTRO = "debian"
 
 
+Distros = Literal["debian", "wolfi", "bullseye", "bookworm"]
+
+
 class TTLConfig(TypedDict, total=False):
     """Configuration for TTL (time-to-live) behavior in the store."""
 
@@ -354,6 +357,8 @@ class HttpConfig(TypedDict, total=False):
 
     You can include or exclude headers as configurable values to condition your
     agent's behavior or permissions on a request's headers."""
+    logging_headers: Optional[ConfigurableHeaderConfig]
+    """Optional. Defines which headers are excluded from logging."""
 
 
 class Config(TypedDict, total=False):
@@ -369,6 +374,13 @@ class Config(TypedDict, total=False):
     Must be >= 20 if provided.
     """
 
+    api_version: Optional[str]
+    """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: Optional[str]
     """Optional. Internal use only.
     """
@@ -378,10 +390,11 @@ class Config(TypedDict, total=False):
     
     Defaults to langchain/langgraph-api or langchain/langgraphjs-api."""
 
-    image_distro: Optional[str]
+    image_distro: Optional[Distros]
     """Optional. Linux distribution for the base image.
     
-    Must be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.
+    Must be one of 'wolfi', 'debian', 'bullseye', or 'bookworm'.
+    If omitted, defaults to 'debian' ('latest').
     """
 
     pip_config_file: Optional[str]
@@ -587,13 +600,28 @@ def validate_config(config: Config) -> Config:
     )
 
     image_distro = config.get("image_distro", DEFAULT_IMAGE_DISTRO)
+    internal_docker_tag = config.get("_INTERNAL_docker_tag")
+    api_version = config.get("api_version")
+    if internal_docker_tag:
+        if api_version:
+            raise click.UsageError(
+                "Cannot specify both _INTERNAL_docker_tag and api_version."
+            )
+    if api_version:
+        try:
+            parts = tuple(map(int, api_version.split("-")[0].split(".")))
+            if len(parts) > 3:
+                raise ValueError(
+                    "Version must be major or major.minor or major.minor.patch."
+                )
+        except TypeError:
+            raise click.UsageError(f"Invalid version format: {api_version}") from None
 
     config = {
         "node_version": node_version,
         "python_version": python_version,
         "pip_config_file": config.get("pip_config_file"),
         "pip_installer": config.get("pip_installer", "auto"),
-        "_INTERNAL_docker_tag": config.get("_INTERNAL_docker_tag"),
         "base_image": config.get("base_image"),
         "image_distro": image_distro,
         "dependencies": config.get("dependencies", []),
@@ -608,6 +636,10 @@ def validate_config(config: Config) -> Config:
         "ui_config": config.get("ui_config"),
         "keep_pkg_tools": config.get("keep_pkg_tools"),
     }
+    if internal_docker_tag:
+        config["_INTERNAL_docker_tag"] = internal_docker_tag
+    if api_version:
+        config["api_version"] = api_version
 
     if config.get("node_version"):
         node_version = config["node_version"]
@@ -644,17 +676,17 @@ def validate_config(config: Config) -> Config:
                 "Add at least one dependency to 'dependencies' list."
             )
 
-    if not config["graphs"]:
+    if not config.get("graphs"):
         raise click.UsageError(
             "No graphs found in config. Add at least one graph to 'graphs' dictionary."
         )
 
     # Validate image_distro config
     if image_distro := config.get("image_distro"):
-        if image_distro not in ["debian", "wolfi"]:
+        if image_distro not in Distros.__args__:
             raise click.UsageError(
                 f"Invalid image_distro: '{image_distro}'. "
-                "Must be either 'debian' or 'wolfi'."
+                "Must be one of 'debian', 'bullseye', or 'bookworm'."
             )
 
     if pip_installer := config.get("pip_installer"):
@@ -1465,6 +1497,7 @@ def docker_tag(
     base_image: Optional[str] = None,
     api_version: Optional[str] = None,
 ) -> str:
+    api_version = api_version or config.get("api_version")
     base_image = base_image or default_base_image(config)
 
     image_distro = config.get("image_distro")
@@ -1473,9 +1506,6 @@ def docker_tag(
     if config.get("_INTERNAL_docker_tag"):
         return f"{base_image}:{config['_INTERNAL_docker_tag']}"
 
-    if "/langgraph-server" in base_image:
-        return f"{base_image}-py{config['python_version']}"
-
     # Build the standard tag format
     language, version = None, None
     if config.get("node_version") and not config.get("python_version"):
@@ -1488,6 +1518,8 @@ def docker_tag(
     # Prepend API version if provided
     if api_version:
         full_tag = f"{api_version}-{language}{version_distro_tag}"
+    elif "/langgraph-server" in base_image and version_distro_tag not in base_image:
+        return f"{base_image}-{language}{version_distro_tag}"
     else:
         full_tag = version_distro_tag
 

libs/cli/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-cli"
-version = "0.3.6"
+dynamic = ["version"]
 description = "CLI for interacting with LangGraph API"
 authors = []
 requires-python = ">=3.9"
@@ -15,11 +15,12 @@ dependencies = [
     "click>=8.1.7",
     "langgraph-sdk>=0.1.0 ; python_version >= '3.11'",
 ]
-
+[tool.hatch.version]
+path = "langgraph_cli/__init__.py"
 [project.optional-dependencies]
 inmem = [
-    "langgraph-api>=0.2.67,<0.3.0 ; python_version >= '3.11'",
-    "langgraph-runtime-inmem>=0.6.0 ; python_version >= '3.11'",
+    "langgraph-api>=0.3,<0.5.0 ; python_version >= '3.11'",
+    "langgraph-runtime-inmem>=0.7 ; python_version >= '3.11'",
     "python-dotenv>=0.8.0",
 ]
 

libs/cli/schemas/schema.json

@@ -15,7 +15,8 @@
               "description": "Optional. Python version in 'major.minor' format (e.g. '3.11').\nMust be at least 3.11 or greater for this deployment to function properly.\n",
               "enum": [
                 "3.11",
-                "3.12"
+                "3.12",
+                "3.13"
               ]
             },
             "pip_config_file": {
@@ -40,6 +41,17 @@
               ],
               "description": "Optional. Internal use only.\n"
             },
+            "api_version": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Which semantic version of the LangGraph API server to use.\n\nDefaults to latest. Check the\nfor more information.\n"
+            },
             "auth": {
               "anyOf": [
                 {
@@ -122,8 +134,9 @@
             "image_distro": {
               "anyOf": [
                 {
-                  "type": "string",
                   "enum": [
+                    "bookworm",
+                    "bullseye",
                     "debian",
                     "wolfi"
                   ]
@@ -132,7 +145,7 @@
                   "type": "null"
                 }
               ],
-              "description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n"
+              "description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', 'bullseye', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
             },
             "keep_pkg_tools": {
               "anyOf": [
@@ -221,6 +234,17 @@
               ],
               "description": "Optional. Internal use only.\n"
             },
+            "api_version": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Which semantic version of the LangGraph API server to use.\n\nDefaults to latest. Check the\nfor more information.\n"
+            },
             "auth": {
               "anyOf": [
                 {
@@ -313,7 +337,7 @@
                   "type": "null"
                 }
               ],
-              "description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n"
+              "description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', 'bullseye', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
             },
             "keep_pkg_tools": {
               "anyOf": [
@@ -552,6 +576,17 @@
         "disable_threads": {
           "type": "boolean",
           "description": "Optional. If True, /threads routes are removed.\n\nDefault is False.\n"
+        },
+        "logging_headers": {
+          "anyOf": [
+            {
+              "$ref": "#/$defs/ConfigurableHeaderConfig"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Optional. Defines which headers are excluded from logging."
         }
       },
       "required": []

libs/cli/schemas/schema.v0.json

@@ -15,7 +15,8 @@
               "description": "Optional. Python version in 'major.minor' format (e.g. '3.11').\nMust be at least 3.11 or greater for this deployment to function properly.\n",
               "enum": [
                 "3.11",
-                "3.12"
+                "3.12",
+                "3.13"
               ]
             },
             "pip_config_file": {
@@ -40,6 +41,17 @@
               ],
               "description": "Optional. Internal use only.\n"
             },
+            "api_version": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Which semantic version of the LangGraph API server to use.\n\nDefaults to latest. Check the\nfor more information.\n"
+            },
             "auth": {
               "anyOf": [
                 {
@@ -122,8 +134,9 @@
             "image_distro": {
               "anyOf": [
                 {
-                  "type": "string",
                   "enum": [
+                    "bookworm",
+                    "bullseye",
                     "debian",
                     "wolfi"
                   ]
@@ -132,7 +145,7 @@
                   "type": "null"
                 }
               ],
-              "description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n"
+              "description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', 'bullseye', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
             },
             "keep_pkg_tools": {
               "anyOf": [
@@ -221,6 +234,17 @@
               ],
               "description": "Optional. Internal use only.\n"
             },
+            "api_version": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Which semantic version of the LangGraph API server to use.\n\nDefaults to latest. Check the\nfor more information.\n"
+            },
             "auth": {
               "anyOf": [
                 {
@@ -313,7 +337,7 @@
                   "type": "null"
                 }
               ],
-              "description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n"
+              "description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', 'bullseye', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
             },
             "keep_pkg_tools": {
               "anyOf": [
@@ -552,6 +576,17 @@
         "disable_threads": {
           "type": "boolean",
           "description": "Optional. If True, /threads routes are removed.\n\nDefault is False.\n"
+        },
+        "logging_headers": {
+          "anyOf": [
+            {
+              "$ref": "#/$defs/ConfigurableHeaderConfig"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Optional. Defines which headers are excluded from logging."
         }
       },
       "required": []

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

@@ -381,7 +381,9 @@ def test_dockerfile_command_with_base_image() -> None:
         assert save_path.exists()
         with open(save_path) as f:
             dockerfile = f.read()
-            assert re.match("FROM langchain/langgraph-server:0.2-py3.*", dockerfile)
+            assert re.match("FROM langchain/langgraph-server:0.2-py3.*", dockerfile), (
+                "\n".join(dockerfile.splitlines()[:3])
+            )
 
 
 def test_dockerfile_command_with_docker_compose() -> None:

libs/cli/tests/unit_tests/test_config.py

@@ -38,7 +38,6 @@ def test_validate_config():
     }
     actual_config = validate_config(expected_config)
     expected_config = {
-        "_INTERNAL_docker_tag": None,
         "base_image": None,
         "python_version": "3.11",
         "node_version": None,
@@ -61,7 +60,6 @@ def test_validate_config():
     # full config
     env = ".env"
     expected_config = {
-        "_INTERNAL_docker_tag": None,
         "base_image": None,
         "python_version": "3.12",
         "node_version": None,
@@ -190,7 +188,6 @@ def test_validate_config_image_distro():
             }
         )
     assert "Invalid image_distro: 'ubuntu'" in str(exc_info.value)
-    assert "Must be either 'debian' or 'wolfi'" in str(exc_info.value)
 
     with pytest.raises(click.UsageError) as exc_info:
         validate_config(
@@ -1339,19 +1336,22 @@ def test_docker_tag_different_node_versions_with_distro():
         assert tag == expected_tag, f"Failed for Node.js {node_version}"
 
 
-def test_docker_tag_with_api_version():
+@pytest.mark.parametrize("in_config", [False, True])
+def test_docker_tag_with_api_version(in_config: bool):
     """Test docker_tag function with api_version parameter."""
 
     # Test 1: Python config with api_version and default distro
+    version = "0.2.74"
     config = validate_config(
         {
             "python_version": "3.11",
             "dependencies": ["."],
             "graphs": {"agent": "./agent.py:graph"},
+            "api_version": version if in_config else None,
         }
     )
-    tag = docker_tag(config, api_version="0.2.74")
-    assert tag == "langchain/langgraph-api:0.2.74-py3.11"
+    tag = docker_tag(config, api_version=version if not in_config else None)
+    assert tag == f"langchain/langgraph-api:{version}-py3.11"
 
     # Test 2: Python config with api_version and wolfi distro
     config = validate_config(
@@ -1360,20 +1360,22 @@ def test_docker_tag_with_api_version():
             "dependencies": ["."],
             "graphs": {"agent": "./agent.py:graph"},
             "image_distro": "wolfi",
+            "api_version": version if in_config else None,
         }
     )
-    tag = docker_tag(config, api_version="0.2.74")
-    assert tag == "langchain/langgraph-api:0.2.74-py3.12-wolfi"
+    tag = docker_tag(config, api_version=version if not in_config else None)
+    assert tag == f"langchain/langgraph-api:{version}-py3.12-wolfi"
 
     # Test 3: Node.js config with api_version and default distro
     config = validate_config(
         {
             "node_version": "20",
             "graphs": {"agent": "./agent.js:graph"},
+            "api_version": version if in_config else None,
         }
     )
-    tag = docker_tag(config, api_version="0.2.74")
-    assert tag == "langchain/langgraphjs-api:0.2.74-node20"
+    tag = docker_tag(config, api_version=version if not in_config else None)
+    assert tag == f"langchain/langgraphjs-api:{version}-node20"
 
     # Test 4: Node.js config with api_version and wolfi distro
     config = validate_config(
@@ -1381,10 +1383,11 @@ def test_docker_tag_with_api_version():
             "node_version": "20",
             "graphs": {"agent": "./agent.js:graph"},
             "image_distro": "wolfi",
+            "api_version": version if in_config else None,
         }
     )
-    tag = docker_tag(config, api_version="0.2.74")
-    assert tag == "langchain/langgraphjs-api:0.2.74-node20-wolfi"
+    tag = docker_tag(config, api_version=version if not in_config else None)
+    assert tag == f"langchain/langgraphjs-api:{version}-node20-wolfi"
 
     # Test 5: Custom base image with api_version
     config = validate_config(
@@ -1393,10 +1396,15 @@ def test_docker_tag_with_api_version():
             "dependencies": ["."],
             "graphs": {"agent": "./agent.py:graph"},
             "base_image": "my-registry/custom-image",
+            "api_version": version if in_config else None,
         }
     )
-    tag = docker_tag(config, base_image="my-registry/custom-image", api_version="1.0.0")
-    assert tag == "my-registry/custom-image:1.0.0-py3.11"
+    tag = docker_tag(
+        config,
+        base_image="my-registry/custom-image",
+        api_version=version if not in_config else None,
+    )
+    assert tag == f"my-registry/custom-image:{version}-py3.11"
 
     # Test 6: api_version with different Python versions
     for python_version in ["3.11", "3.12", "3.13"]:
@@ -1405,10 +1413,11 @@ def test_docker_tag_with_api_version():
                 "python_version": python_version,
                 "dependencies": ["."],
                 "graphs": {"agent": "./agent.py:graph"},
+                "api_version": version if in_config else None,
             }
         )
-        tag = docker_tag(config, api_version="0.2.74")
-        assert tag == f"langchain/langgraph-api:0.2.74-py{python_version}"
+        tag = docker_tag(config, api_version=version if not in_config else None)
+        assert tag == f"langchain/langgraph-api:{version}-py{python_version}"
 
     # Test 7: Without api_version should work as before
     config = validate_config(
@@ -1428,10 +1437,11 @@ def test_docker_tag_with_api_version():
             "node_version": "20",
             "dependencies": ["."],
             "graphs": {"python": "./agent.py:graph", "js": "./agent.js:graph"},
+            "api_version": version if in_config else None,
         }
     )
-    tag = docker_tag(config, api_version="0.2.74")
-    assert tag == "langchain/langgraph-api:0.2.74-py3.11"
+    tag = docker_tag(config, api_version=version if not in_config else None)
+    assert tag == f"langchain/langgraph-api:{version}-py3.11"
 
     # Test 9: api_version with _INTERNAL_docker_tag should ignore api_version
     config = validate_config(
@@ -1451,12 +1461,15 @@ def test_docker_tag_with_api_version():
             "python_version": "3.11",
             "dependencies": ["."],
             "graphs": {"agent": "./agent.py:graph"},
+            "api_version": version if in_config else None,
         }
     )
     tag = docker_tag(
-        config, base_image="langchain/langgraph-server:0.2", api_version="0.2.74"
+        config,
+        base_image="langchain/langgraph-server",
+        api_version=version if not in_config else None,
     )
-    assert tag == "langchain/langgraph-server:0.2-py3.11"
+    assert tag == f"langchain/langgraph-server:{version}-py3.11"
 
 
 def test_config_to_docker_with_api_version():

libs/cli/uv.lock

@@ -18,16 +18,16 @@ wheels = [
 
 [[package]]
 name = "anyio"
-version = "4.9.0"
+version = "4.10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna", marker = "python_full_version >= '3.11'" },
     { name = "sniffio", marker = "python_full_version >= '3.11'" },
     { name = "typing-extensions", marker = "python_full_version >= '3.11' and python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949, upload-time = "2025-03-17T00:02:54.77Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252, upload-time = "2025-08-04T08:54:26.451Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916, upload-time = "2025-03-17T00:02:52.713Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/12/e5e0282d673bb9746bacfb6e2dba8719989d3660cdb2ea79aee9a9651afb/anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1", size = 107213, upload-time = "2025-08-04T08:54:24.882Z" },
 ]
 
 [[package]]
@@ -53,11 +53,11 @@ wheels = [
 
 [[package]]
 name = "certifi"
-version = "2025.7.14"
+version = "2025.8.3"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b3/76/52c535bcebe74590f296d6c77c86dabf761c41980e1347a2422e4aa2ae41/certifi-2025.7.14.tar.gz", hash = "sha256:8ea99dbdfaaf2ba2f9bac77b9249ef62ec5218e7c2b2e903378ed5fccf765995", size = 163981, upload-time = "2025-07-14T03:29:28.449Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/dc/67/960ebe6bf230a96cda2e0abcf73af550ec4f090005363542f0765df162e0/certifi-2025.8.3.tar.gz", hash = "sha256:e564105f78ded564e3ae7c923924435e1daa7463faeab5bb932bc53ffae63407", size = 162386, upload-time = "2025-08-03T03:07:47.08Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/4f/52/34c6cf5bb9285074dc3531c437b3919e825d976fde097a7a73f79e726d03/certifi-2025.7.14-py3-none-any.whl", hash = "sha256:6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2", size = 162722, upload-time = "2025-07-14T03:29:26.863Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/48/1549795ba7742c948d2ad169c1c8cdbae65bc450d6cd753d124b17c8cd32/certifi-2025.8.3-py3-none-any.whl", hash = "sha256:f6c12493cfb1b06ba2ff328595af9350c65d6644968e5d3a2ffd78699af217a5", size = 161216, upload-time = "2025-08-03T03:07:45.777Z" },
 ]
 
 [[package]]
@@ -131,76 +131,77 @@ wheels = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.2"
+version = "3.4.3"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e4/33/89c2ced2b67d1c2a61c19c6751aa8902d46ce3dacb23600a283619f5a12d/charset_normalizer-3.4.2.tar.gz", hash = "sha256:5baececa9ecba31eff645232d59845c07aa030f0c81ee70184a90d35099a0e63", size = 126367, upload-time = "2025-05-02T08:34:42.01Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/83/2d/5fd176ceb9b2fc619e63405525573493ca23441330fcdaee6bef9460e924/charset_normalizer-3.4.3.tar.gz", hash = "sha256:6fce4b8500244f6fcb71465d4a4930d132ba9ab8e71a7859e6a5d59851068d14", size = 122371, upload-time = "2025-08-09T07:57:28.46Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/95/28/9901804da60055b406e1a1c5ba7aac1276fb77f1dde635aabfc7fd84b8ab/charset_normalizer-3.4.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7c48ed483eb946e6c04ccbe02c6b4d1d48e51944b6db70f697e089c193404941", size = 201818, upload-time = "2025-05-02T08:31:46.725Z" },
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libs/langgraph/bench/react_agent.py

@@ -10,7 +10,7 @@ from langchain_core.outputs import ChatGeneration, ChatResult
 from langchain_core.tools import StructuredTool
 
 from langgraph.checkpoint.base import BaseCheckpointSaver
-from langgraph.prebuilt.chat_agent_executor import create_agent
+from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.pregel import Pregel
 
 
@@ -60,7 +60,7 @@ def react_agent(n_tools: int, checkpointer: Optional[BaseCheckpointSaver]) -> Pr
         ]
     )
 
-    return create_agent(model, [tool], checkpointer=checkpointer)
+    return create_react_agent(model, [tool], checkpointer=checkpointer)
 
 
 if __name__ == "__main__":

libs/langgraph/langgraph/pregel/remote.py

@@ -25,9 +25,17 @@ from langgraph_sdk.client import (
     get_client,
     get_sync_client,
 )
-from langgraph_sdk.schema import Checkpoint, ThreadState
-from langgraph_sdk.schema import Command as CommandSDK
-from langgraph_sdk.schema import StreamMode as StreamModeSDK
+from langgraph_sdk.schema import (
+    Checkpoint,
+    QueryParamTypes,
+    ThreadState,
+)
+from langgraph_sdk.schema import (
+    Command as CommandSDK,
+)
+from langgraph_sdk.schema import (
+    StreamMode as StreamModeSDK,
+)
 from typing_extensions import Self
 
 from langgraph._internal._config import merge_configs
@@ -208,6 +216,8 @@ class RemoteGraph(PregelProtocol):
         config: RunnableConfig | None = None,
         *,
         xray: int | bool = False,
+        headers: dict[str, str] | None = None,
+        params: QueryParamTypes | None = None,
     ) -> DrawableGraph:
         """Get graph by graph name.
 
@@ -226,6 +236,8 @@ class RemoteGraph(PregelProtocol):
         graph = sync_client.assistants.get_graph(
             assistant_id=self.assistant_id,
             xray=xray,
+            headers=headers,
+            params=params,
         )
         return DrawableGraph(
             nodes=self._get_drawable_nodes(graph),
@@ -237,6 +249,8 @@ class RemoteGraph(PregelProtocol):
         config: RunnableConfig | None = None,
         *,
         xray: int | bool = False,
+        headers: dict[str, str] | None = None,
+        params: QueryParamTypes | None = None,
     ) -> DrawableGraph:
         """Get graph by graph name.
 
@@ -255,6 +269,8 @@ class RemoteGraph(PregelProtocol):
         graph = await client.assistants.get_graph(
             assistant_id=self.assistant_id,
             xray=xray,
+            headers=headers,
+            params=params,
         )
         return DrawableGraph(
             nodes=self._get_drawable_nodes(graph),
@@ -376,7 +392,12 @@ class RemoteGraph(PregelProtocol):
         return sanitized
 
     def get_state(
-        self, config: RunnableConfig, *, subgraphs: bool = False
+        self,
+        config: RunnableConfig,
+        *,
+        subgraphs: bool = False,
+        headers: dict[str, str] | None = None,
+        params: QueryParamTypes | None = None,
     ) -> StateSnapshot:
         """Get the state of a thread.
 
@@ -388,6 +409,8 @@ class RemoteGraph(PregelProtocol):
             config: A `RunnableConfig` that includes `thread_id` in the
                 `configurable` field.
             subgraphs: Include subgraphs in the state.
+            headers: Optional custom headers to include with the request.
+            params: Optional query parameters to include with the request.
 
         Returns:
             The latest state of the thread.
@@ -399,11 +422,18 @@ class RemoteGraph(PregelProtocol):
             thread_id=merged_config["configurable"]["thread_id"],
             checkpoint=self._get_checkpoint(merged_config),
             subgraphs=subgraphs,
+            headers=headers,
+            params=params,
         )
         return self._create_state_snapshot(state)
 
     async def aget_state(
-        self, config: RunnableConfig, *, subgraphs: bool = False
+        self,
+        config: RunnableConfig,
+        *,
+        subgraphs: bool = False,
+        headers: dict[str, str] | None = None,
+        params: QueryParamTypes | None = None,
     ) -> StateSnapshot:
         """Get the state of a thread.
 
@@ -415,6 +445,8 @@ class RemoteGraph(PregelProtocol):
             config: A `RunnableConfig` that includes `thread_id` in the
                 `configurable` field.
             subgraphs: Include subgraphs in the state.
+            headers: Optional custom headers to include with the request.
+            params: Optional query parameters to include with the request.
 
         Returns:
             The latest state of the thread.
@@ -426,6 +458,8 @@ class RemoteGraph(PregelProtocol):
             thread_id=merged_config["configurable"]["thread_id"],
             checkpoint=self._get_checkpoint(merged_config),
             subgraphs=subgraphs,
+            headers=headers,
+            params=params,
         )
         return self._create_state_snapshot(state)
 
@@ -436,6 +470,8 @@ class RemoteGraph(PregelProtocol):
         filter: dict[str, Any] | None = None,
         before: RunnableConfig | None = None,
         limit: int | None = None,
+        headers: dict[str, str] | None = None,
+        params: QueryParamTypes | None = None,
     ) -> Iterator[StateSnapshot]:
         """Get the state history of a thread.
 
@@ -460,6 +496,8 @@ class RemoteGraph(PregelProtocol):
             before=self._get_checkpoint(before),
             metadata=filter,
             checkpoint=self._get_checkpoint(merged_config),
+            headers=headers,
+            params=params,
         )
         for state in states:
             yield self._create_state_snapshot(state)
@@ -471,6 +509,8 @@ class RemoteGraph(PregelProtocol):
         filter: dict[str, Any] | None = None,
         before: RunnableConfig | None = None,
         limit: int | None = None,
+        headers: dict[str, str] | None = None,
+        params: QueryParamTypes | None = None,
     ) -> AsyncIterator[StateSnapshot]:
         """Get the state history of a thread.
 
@@ -482,6 +522,8 @@ class RemoteGraph(PregelProtocol):
             filter: Metadata to filter on.
             before: A `RunnableConfig` that includes checkpoint metadata.
             limit: Max number of states to return.
+            headers: Optional custom headers to include with the request.
+            params: Optional query parameters to include with the request.
 
         Returns:
             States of the thread.
@@ -495,6 +537,8 @@ class RemoteGraph(PregelProtocol):
             before=self._get_checkpoint(before),
             metadata=filter,
             checkpoint=self._get_checkpoint(merged_config),
+            headers=headers,
+            params=params,
         )
         for state in states:
             yield self._create_state_snapshot(state)
@@ -518,6 +562,9 @@ class RemoteGraph(PregelProtocol):
         config: RunnableConfig,
         values: dict[str, Any] | Any | None,
         as_node: str | None = None,
+        *,
+        headers: dict[str, str] | None = None,
+        params: QueryParamTypes | None = None,
     ) -> RunnableConfig:
         """Update the state of a thread.
 
@@ -540,6 +587,8 @@ class RemoteGraph(PregelProtocol):
             values=values,
             as_node=as_node,
             checkpoint=self._get_checkpoint(merged_config),
+            headers=headers,
+            params=params,
         )
         return self._get_config(response["checkpoint"])
 
@@ -548,6 +597,9 @@ class RemoteGraph(PregelProtocol):
         config: RunnableConfig,
         values: dict[str, Any] | Any | None,
         as_node: str | None = None,
+        *,
+        headers: dict[str, str] | None = None,
+        params: QueryParamTypes | None = None,
     ) -> RunnableConfig:
         """Update the state of a thread.
 
@@ -570,6 +622,8 @@ class RemoteGraph(PregelProtocol):
             values=values,
             as_node=as_node,
             checkpoint=self._get_checkpoint(merged_config),
+            headers=headers,
+            params=params,
         )
         return self._get_config(response["checkpoint"])
 
@@ -634,6 +688,7 @@ class RemoteGraph(PregelProtocol):
         interrupt_after: All | Sequence[str] | None = None,
         subgraphs: bool = False,
         headers: dict[str, str] | None = None,
+        params: QueryParamTypes | None = None,
         **kwargs: Any,
     ) -> Iterator[dict[str, Any] | Any]:
         """Create a run and stream the results.
@@ -678,9 +733,10 @@ class RemoteGraph(PregelProtocol):
             interrupt_after=interrupt_after,
             stream_subgraphs=subgraphs or stream is not None,
             if_not_exists="create",
-            headers=_merge_tracing_headers(headers)
-            if self.distributed_tracing
-            else headers,
+            headers=(
+                _merge_tracing_headers(headers) if self.distributed_tracing else headers
+            ),
+            params=params,
             **kwargs,
         ):
             # split mode and ns
@@ -741,6 +797,7 @@ class RemoteGraph(PregelProtocol):
         interrupt_after: All | Sequence[str] | None = None,
         subgraphs: bool = False,
         headers: dict[str, str] | None = None,
+        params: QueryParamTypes | None = None,
         **kwargs: Any,
     ) -> AsyncIterator[dict[str, Any] | Any]:
         """Create a run and stream the results.
@@ -785,9 +842,10 @@ class RemoteGraph(PregelProtocol):
             interrupt_after=interrupt_after,
             stream_subgraphs=subgraphs or stream is not None,
             if_not_exists="create",
-            headers=_merge_tracing_headers(headers)
-            if self.distributed_tracing
-            else headers,
+            headers=(
+                _merge_tracing_headers(headers) if self.distributed_tracing else headers
+            ),
+            params=params,
             **kwargs,
         ):
             # split mode and ns
@@ -862,6 +920,7 @@ class RemoteGraph(PregelProtocol):
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         headers: dict[str, str] | None = None,
+        params: QueryParamTypes | None = None,
         **kwargs: Any,
     ) -> dict[str, Any] | Any:
         """Create a run, wait until it finishes and return the final state.
@@ -884,6 +943,7 @@ class RemoteGraph(PregelProtocol):
             interrupt_after=interrupt_after,
             headers=headers,
             stream_mode="values",
+            params=params,
             **kwargs,
         ):
             pass
@@ -900,6 +960,7 @@ class RemoteGraph(PregelProtocol):
         interrupt_before: All | Sequence[str] | None = None,
         interrupt_after: All | Sequence[str] | None = None,
         headers: dict[str, str] | None = None,
+        params: QueryParamTypes | None = None,
         **kwargs: Any,
     ) -> dict[str, Any] | Any:
         """Create a run, wait until it finishes and return the final state.
@@ -922,6 +983,7 @@ class RemoteGraph(PregelProtocol):
             interrupt_after=interrupt_after,
             headers=headers,
             stream_mode="values",
+            params=params,
             **kwargs,
         ):
             pass
@@ -934,11 +996,11 @@ class RemoteGraph(PregelProtocol):
 def _merge_tracing_headers(headers: dict[str, str] | None) -> dict[str, str] | None:
     if rt := ls.get_current_run_tree():
         tracing_headers = rt.to_headers()
-        baggage = tracing_headers.pop("baggage")
         if headers:
             if "baggage" in headers:
-                baggage = headers["baggage"] + "," + baggage
-            tracing_headers["baggage"] = baggage
+                tracing_headers["baggage"] = (
+                    f"{headers['baggage']},{tracing_headers['baggage']}"
+                )
             headers.update(tracing_headers)
         else:
             headers = tracing_headers

libs/langgraph/langgraph/types.py

@@ -507,6 +507,7 @@ def interrupt(value: Any) -> Any:
     # find previous resume values
     if scratchpad.resume:
         if idx < len(scratchpad.resume):
+            conf[CONFIG_KEY_SEND]([(RESUME, scratchpad.resume)])
             return scratchpad.resume[idx]
     # find current resume value
     v = scratchpad.get_null_resume(True)

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "0.6.4"
+version = "0.6.6"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.9"
@@ -14,7 +14,7 @@ license-files = ['LICENSE']
 dependencies = [
     "langchain-core>=0.1",
     "langgraph-checkpoint>=2.1.0,<3.0.0",
-    "langgraph-sdk>=0.2.0,<0.3.0",
+    "langgraph-sdk>=0.2.2,<0.3.0",
     "langgraph-prebuilt>=0.6.0,<0.7.0",
     "xxhash>=3.5.0",
     "pydantic>=2.7.4",

libs/langgraph/tests/__snapshots__/test_large_cases.ambr

@@ -175,10 +175,10 @@
   '''
 # ---
 # name: test_prebuilt_tool_chat
-  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}, "structured_response": {"title": "Structured Response", "type": "null"}}, "required": ["messages"], "title": "AgentState", "type": "object"}'
+  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}}, "required": ["messages"], "title": "AgentState", "type": "object"}'
 # ---
 # name: test_prebuilt_tool_chat.1
-  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}, "structured_response": {"title": "Structured Response", "type": "null"}}, "required": ["messages"], "title": "AgentState", "type": "object"}'
+  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}}, "required": ["messages"], "title": "AgentState", "type": "object"}'
 # ---
 # name: test_prebuilt_tool_chat.2
   '''
@@ -198,7 +198,7 @@
         }
       },
       {
-        "id": "model",
+        "id": "agent",
         "type": "runnable",
         "data": {
           "id": [
@@ -207,7 +207,7 @@
             "_runnable",
             "RunnableCallable"
           ],
-          "name": "model"
+          "name": "agent"
         }
       },
       {
@@ -230,21 +230,21 @@
     "edges": [
       {
         "source": "__start__",
-        "target": "model"
+        "target": "agent"
       },
       {
-        "source": "model",
+        "source": "agent",
         "target": "__end__",
         "conditional": true
       },
       {
-        "source": "model",
+        "source": "agent",
         "target": "tools",
         "conditional": true
       },
       {
         "source": "tools",
-        "target": "model"
+        "target": "agent"
       }
     ]
   }
@@ -253,10 +253,10 @@
 # name: test_prebuilt_tool_chat.3
   '''
   graph TD;
-  	__start__ --> model;
-  	model -.-> __end__;
-  	model -.-> tools;
-  	tools --> model;
+  	__start__ --> agent;
+  	agent -.-> __end__;
+  	agent -.-> tools;
+  	tools --> agent;
   
   '''
 # ---

libs/langgraph/tests/test_large_cases.py

@@ -21,7 +21,7 @@ from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.constants import END, START
 from langgraph.graph import StateGraph
 from langgraph.graph.message import MessagesState, add_messages
-from langgraph.prebuilt.chat_agent_executor import create_agent
+from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.pregel import NodeBuilder, Pregel
 from langgraph.types import (
@@ -1301,7 +1301,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
         ]
     )
 
-    app = create_agent(model, tools)
+    app = create_react_agent(model, tools)
 
     assert json.dumps(app.get_input_jsonschema()) == snapshot
     assert json.dumps(app.get_output_jsonschema()) == snapshot
@@ -1390,11 +1390,11 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
             ),
             {
                 "langgraph_step": 1,
-                "langgraph_node": "model",
-                "langgraph_triggers": ("branch:to:model",),
-                "langgraph_path": (PULL, "model"),
-                "langgraph_checkpoint_ns": AnyStr("model:"),
-                "checkpoint_ns": AnyStr("model:"),
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": (PULL, "agent"),
+                "langgraph_checkpoint_ns": AnyStr("agent:"),
+                "checkpoint_ns": AnyStr("agent:"),
                 "ls_provider": "fakechatmodel",
                 "ls_model_type": "chat",
             },
@@ -1449,11 +1449,11 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
             ),
             {
                 "langgraph_step": 3,
-                "langgraph_node": "model",
-                "langgraph_triggers": ("branch:to:model",),
-                "langgraph_path": (PULL, "model"),
-                "langgraph_checkpoint_ns": AnyStr("model:"),
-                "checkpoint_ns": AnyStr("model:"),
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": (PULL, "agent"),
+                "langgraph_checkpoint_ns": AnyStr("agent:"),
+                "checkpoint_ns": AnyStr("agent:"),
                 "ls_provider": "fakechatmodel",
                 "ls_model_type": "chat",
             },
@@ -1497,11 +1497,11 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
             ),
             {
                 "langgraph_step": 5,
-                "langgraph_node": "model",
-                "langgraph_triggers": ("branch:to:model",),
-                "langgraph_path": (PULL, "model"),
-                "langgraph_checkpoint_ns": AnyStr("model:"),
-                "checkpoint_ns": AnyStr("model:"),
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": (PULL, "agent"),
+                "langgraph_checkpoint_ns": AnyStr("agent:"),
+                "checkpoint_ns": AnyStr("agent:"),
                 "ls_provider": "fakechatmodel",
                 "ls_model_type": "chat",
             },
@@ -1533,7 +1533,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
     for output in (invoke_updates_events, stream_updates_events):
         assert output[:3] == [
             {
-                "model": {
+                "agent": {
                     "messages": [
                         _AnyIdAIMessage(
                             content="",
@@ -1560,7 +1560,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
                 }
             },
             {
-                "model": {
+                "agent": {
                     "messages": [
                         _AnyIdAIMessage(
                             content="",
@@ -1606,7 +1606,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
             },
         )
         assert output[5:] == [
-            {"model": {"messages": [_AnyIdAIMessage(content="answer")]}}
+            {"agent": {"messages": [_AnyIdAIMessage(content="answer")]}}
         ]
 
 

libs/langgraph/tests/test_large_cases_async.py

@@ -23,7 +23,7 @@ from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.constants import END, START
 from langgraph.graph.message import add_messages
 from langgraph.graph.state import StateGraph
-from langgraph.prebuilt.chat_agent_executor import create_agent
+from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.pregel import NodeBuilder, Pregel
 from langgraph.types import PregelTask, Send, StateSnapshot, StreamWriter
@@ -1059,7 +1059,7 @@ async def test_prebuilt_tool_chat() -> None:
 
     tools = [search_api]
 
-    app = create_agent(model, tools)
+    app = create_react_agent(model, tools)
 
     assert await app.ainvoke(
         {"messages": [HumanMessage(content="what is weather in sf")]}
@@ -1143,11 +1143,11 @@ async def test_prebuilt_tool_chat() -> None:
             ),
             {
                 "langgraph_step": 1,
-                "langgraph_node": "model",
-                "langgraph_triggers": ("branch:to:model",),
-                "langgraph_path": (PULL, "model"),
-                "langgraph_checkpoint_ns": AnyStr("model:"),
-                "checkpoint_ns": AnyStr("model:"),
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": (PULL, "agent"),
+                "langgraph_checkpoint_ns": AnyStr("agent:"),
+                "checkpoint_ns": AnyStr("agent:"),
                 "ls_provider": "fakechatmodel",
                 "ls_model_type": "chat",
             },
@@ -1202,11 +1202,11 @@ async def test_prebuilt_tool_chat() -> None:
             ),
             {
                 "langgraph_step": 3,
-                "langgraph_node": "model",
-                "langgraph_triggers": ("branch:to:model",),
-                "langgraph_path": (PULL, "model"),
-                "langgraph_checkpoint_ns": AnyStr("model:"),
-                "checkpoint_ns": AnyStr("model:"),
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": (PULL, "agent"),
+                "langgraph_checkpoint_ns": AnyStr("agent:"),
+                "checkpoint_ns": AnyStr("agent:"),
                 "ls_provider": "fakechatmodel",
                 "ls_model_type": "chat",
             },
@@ -1250,11 +1250,11 @@ async def test_prebuilt_tool_chat() -> None:
             ),
             {
                 "langgraph_step": 5,
-                "langgraph_node": "model",
-                "langgraph_triggers": ("branch:to:model",),
-                "langgraph_path": (PULL, "model"),
-                "langgraph_checkpoint_ns": AnyStr("model:"),
-                "checkpoint_ns": AnyStr("model:"),
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": (PULL, "agent"),
+                "langgraph_checkpoint_ns": AnyStr("agent:"),
+                "checkpoint_ns": AnyStr("agent:"),
                 "ls_provider": "fakechatmodel",
                 "ls_model_type": "chat",
             },
@@ -1269,7 +1269,7 @@ async def test_prebuilt_tool_chat() -> None:
     ]
     assert stream_updates_events[:3] == [
         {
-            "model": {
+            "agent": {
                 "messages": [
                     _AnyIdAIMessage(
                         content="",
@@ -1296,7 +1296,7 @@ async def test_prebuilt_tool_chat() -> None:
             }
         },
         {
-            "model": {
+            "agent": {
                 "messages": [
                     _AnyIdAIMessage(
                         content="",
@@ -1342,7 +1342,7 @@ async def test_prebuilt_tool_chat() -> None:
         },
     )
     assert stream_updates_events[5:] == [
-        {"model": {"messages": [_AnyIdAIMessage(content="answer")]}}
+        {"agent": {"messages": [_AnyIdAIMessage(content="answer")]}}
     ]
 
 

libs/langgraph/tests/test_pregel.py

@@ -4805,7 +4805,10 @@ def test_interrupt_subgraph(sync_checkpointer: BaseCheckpointSaver):
     assert graph.invoke(Command(resume="bar"), thread1)
 
 
-def test_interrupt_multiple(sync_checkpointer: BaseCheckpointSaver):
+@pytest.mark.parametrize("resume_style", ["null", "map"])
+def test_interrupt_multiple(
+    sync_checkpointer: BaseCheckpointSaver, resume_style: Literal["null", "map"]
+):
     class State(TypedDict):
         my_key: Annotated[str, operator.add]
 
@@ -4821,7 +4824,8 @@ def test_interrupt_multiple(sync_checkpointer: BaseCheckpointSaver):
     graph = builder.compile(checkpointer=sync_checkpointer)
     thread1 = {"configurable": {"thread_id": "1"}}
 
-    assert [e for e in graph.stream({"my_key": "DE", "market": "DE"}, thread1)] == [
+    result = [e for e in graph.stream({"my_key": "DE", "market": "DE"}, thread1)]
+    assert result == [
         {
             "__interrupt__": (
                 Interrupt(
@@ -4832,12 +4836,19 @@ def test_interrupt_multiple(sync_checkpointer: BaseCheckpointSaver):
         }
     ]
 
-    assert [
+    result = [
         event
         for event in graph.stream(
-            Command(resume="answer 1", update={"my_key": " foofoo "}), thread1
+            Command(
+                resume="answer 1"
+                if resume_style == "null"
+                else {result[0]["__interrupt__"][0].id: "answer 1"},
+                update={"my_key": " foofoo "},
+            ),
+            thread1,
         )
-    ] == [
+    ]
+    assert result == [
         {
             "__interrupt__": (
                 Interrupt(
@@ -4851,7 +4862,13 @@ def test_interrupt_multiple(sync_checkpointer: BaseCheckpointSaver):
     assert [
         event
         for event in graph.stream(
-            Command(resume="answer 2"), thread1, stream_mode="values"
+            Command(
+                resume="answer 2"
+                if resume_style == "null"
+                else {result[0]["__interrupt__"][0].id: "answer 2"}
+            ),
+            thread1,
+            stream_mode="values",
         )
     ] == [
         {"my_key": "DE foofoo "},

libs/langgraph/tests/test_remote_graph.py

@@ -1,6 +1,6 @@
 import re
 import sys
-from typing import Annotated, Union
+from typing import Annotated, Optional, Union
 from unittest.mock import AsyncMock, MagicMock
 
 import langsmith as ls
@@ -1183,7 +1183,10 @@ async def test_remote_graph_stream_messages_tuple(
 @pytest.mark.anyio
 @pytest.mark.parametrize("distributed_tracing", [False, True])
 @pytest.mark.parametrize("stream", [False, True])
-async def test_include_headers(distributed_tracing: bool, stream: bool):
+@pytest.mark.parametrize("headers", [None, {"foo": "bar"}])
+async def test_include_headers(
+    distributed_tracing: bool, stream: bool, headers: Optional[dict[str, str]]
+):
     mock_async_client = MagicMock()
     async_iter = MagicMock()
     return_value = [
@@ -1213,7 +1216,7 @@ async def test_include_headers(distributed_tracing: bool, stream: bool):
                 async for _ in remote_pregel.astream(
                     {"input": {"messages": [{"type": "human", "content": "hello"}]}},
                     config,
-                    headers={"foo": "bar"},
+                    headers=headers,
                 ):
                     pass
 
@@ -1221,12 +1224,14 @@ async def test_include_headers(distributed_tracing: bool, stream: bool):
                 await remote_pregel.ainvoke(
                     {"input": {"messages": [{"type": "human", "content": "hello"}]}},
                     config,
-                    headers={"foo": "bar"},
+                    headers=headers,
                 )
-    expected = {"foo": "bar"}
+    expected = headers.copy() if headers else None
     if distributed_tracing:
+        if expected is None:
+            expected = {}
         expected["langsmith-trace"] = AnyStr()
-        expected["baggage"] = AnyStr()
+        expected["baggage"] = AnyStr("langsmith-metadata=")
 
     assert astream_mock.call_args.kwargs["headers"] == expected
     stream_mock.assert_not_called()
@@ -1237,7 +1242,7 @@ async def test_include_headers(distributed_tracing: bool, stream: bool):
                 for _ in remote_pregel.stream(
                     {"input": {"messages": [{"type": "human", "content": "hello"}]}},
                     config,
-                    headers={"foo": "bar"},
+                    headers=headers,
                 ):
                     pass
 
@@ -1245,6 +1250,6 @@ async def test_include_headers(distributed_tracing: bool, stream: bool):
                 remote_pregel.invoke(
                     {"input": {"messages": [{"type": "human", "content": "hello"}]}},
                     config,
-                    headers={"foo": "bar"},
+                    headers=headers,
                 )
     assert stream_mock.call_args.kwargs["headers"] == expected

libs/prebuilt/langgraph/prebuilt/__init__.py

@@ -1,6 +1,6 @@
 """langgraph.prebuilt exposes a higher-level API for creating and executing agents and tools."""
 
-from langgraph.prebuilt.chat_agent_executor import create_agent
+from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_node import (
     InjectedState,
     InjectedStore,
@@ -10,7 +10,7 @@ from langgraph.prebuilt.tool_node import (
 from langgraph.prebuilt.tool_validator import ValidationNode
 
 __all__ = [
-    "create_agent",
+    "create_react_agent",
     "ToolNode",
     "tools_condition",
     "ValidationNode",

libs/prebuilt/langgraph/prebuilt/_internal/_typing.py

@@ -1,11 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Awaitable, Callable
-from typing import TypeVar
-
-from typing_extensions import ParamSpec
-
-P = ParamSpec("P")
-R = TypeVar("R")
-
-SyncOrAsync = Callable[P, R | Awaitable[R]]

libs/prebuilt/langgraph/prebuilt/interrupt.py

@@ -1,4 +1,4 @@
-from typing import Literal
+from typing import Literal, Optional, Union
 
 from typing_extensions import TypedDict
 
@@ -68,7 +68,7 @@ class HumanInterrupt(TypedDict):
 
     action_request: ActionRequest
     config: HumanInterruptConfig
-    description: str | None
+    description: Optional[str]
 
 
 class HumanResponse(TypedDict):
@@ -87,4 +87,4 @@ class HumanResponse(TypedDict):
     """
 
     type: Literal["accept", "ignore", "response", "edit"]
-    args: None | str | ActionRequest
+    args: Union[None, str, ActionRequest]

libs/prebuilt/langgraph/prebuilt/responses.py

@@ -1,313 +0,0 @@
-"""Types for setting agent response formats."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, is_dataclass
-from types import UnionType
-from typing import Any, Generic, Literal, TypeVar, Union, get_args, get_origin
-
-from langchain_core.messages import AIMessage
-from langchain_core.tools import BaseTool, StructuredTool
-from pydantic import BaseModel, TypeAdapter
-from typing_extensions import Self, is_typeddict
-
-# Supported schema types: Pydantic models, dataclasses, TypedDict, JSON schema dicts
-SchemaT = TypeVar("SchemaT")
-
-SchemaKind = Literal["pydantic", "dataclass", "typeddict", "json_schema"]
-
-
-def _parse_with_schema(
-    schema: type[SchemaT] | dict, schema_kind: SchemaKind, data: dict[str, Any]
-) -> Any:
-    """Parse data using for any supported schema type.
-
-    Args:
-        schema: The schema type (Pydantic model, dataclass, or TypedDict)
-        data: The data to parse
-
-    Returns:
-        The parsed instance according to the schema type
-
-    Raises:
-        ValueError: If parsing fails
-    """
-    if schema_kind == "json_schema":
-        return data
-    else:
-        try:
-            adapter: TypeAdapter[SchemaT] = TypeAdapter(schema)
-            return adapter.validate_python(data)
-        except Exception as e:
-            schema_name = getattr(schema, "__name__", str(schema))
-            raise ValueError(f"Failed to parse data to {schema_name}: {e}") from e
-
-
-@dataclass(init=False)
-class _SchemaSpec(Generic[SchemaT]):
-    """Describes a structured output schema."""
-
-    schema: type[SchemaT] | dict[str, Any]
-    """The schema for the response, can be a Pydantic model, dataclass, TypedDict, or JSON schema dict."""
-
-    name: str
-    """Name of the schema, used for tool calling.
-    
-    If not provided, the name will be the model name or "structured_output" if it's a JSON schema.
-    """
-
-    description: str
-    """Custom description of the schema. 
-    
-    If not provided, provided will use the model's docstring.
-    """
-
-    schema_kind: SchemaKind
-    """The kind of schema."""
-
-    json_schema: dict[str, Any]
-    """JSON schema associated with the schema."""
-
-    strict: bool = False
-    """Whether to enforce strict validation of the schema."""
-
-    def __init__(
-        self,
-        schema: type[SchemaT] | dict[str, Any],
-        *,
-        name: str | None = None,
-        description: str | None = None,
-        strict: bool = False,
-    ) -> None:
-        """Initialize SchemaSpec with schema and optional parameters."""
-        self.schema = schema
-
-        self.name = name or (
-            schema.get("title", "structured_output")
-            if isinstance(schema, dict)
-            else getattr(schema, "__name__", "structured_output")
-        )
-
-        self.description = description or (
-            schema.get("description", "")
-            if isinstance(schema, dict)
-            else getattr(schema, "__doc__", None) or ""
-        )
-
-        self.strict = strict
-
-        if isinstance(schema, dict):
-            self.schema_kind = "json_schema"
-            self.json_schema = schema
-        elif isinstance(schema, type) and issubclass(schema, BaseModel):
-            self.schema_kind = "pydantic"
-            self.json_schema = schema.model_json_schema()
-        elif is_dataclass(schema):
-            self.schema_kind = "dataclass"
-            self.json_schema = TypeAdapter(schema).json_schema()
-        elif is_typeddict(schema):
-            self.schema_kind = "typeddict"
-            self.json_schema = TypeAdapter(schema).json_schema()
-        else:
-            raise ValueError(
-                f"Unsupported schema type: {type(schema)}. "
-                f"Supported types: Pydantic models, dataclasses, TypedDicts, and JSON schema dicts."
-            )
-
-
-@dataclass(init=False)
-class ToolOutput(Generic[SchemaT]):
-    """Use a tool calling strategy for model responses."""
-
-    schema: type[SchemaT] | dict[str, Any]
-    """Schema for the tool calls."""
-
-    schema_specs: list[_SchemaSpec[SchemaT]]
-    """Schema specs for the tool calls."""
-
-    tool_message_content: str | None
-    """The content of the tool message to be returned when the model calls an artificial structured output tool."""
-
-    def __init__(
-        self,
-        schema: type[SchemaT] | dict[str, Any],
-        tool_message_content: str | None = None,
-    ) -> None:
-        """Initialize ToolOutput with schemas and tool message content."""
-        self.schema = schema
-        self.tool_message_content = tool_message_content
-
-        if get_origin(schema) in (UnionType, Union):
-            self.schema_specs = [_SchemaSpec(s) for s in get_args(schema)]
-        else:
-            self.schema_specs = [_SchemaSpec(schema)]
-
-
-@dataclass(init=False)
-class NativeOutput(Generic[SchemaT]):
-    """Use the model provider's native structured output method."""
-
-    schema: type[SchemaT] | dict[str, Any]
-    """Schema for native mode."""
-
-    schema_spec: _SchemaSpec[SchemaT]
-    """Schema spec for native mode."""
-
-    def __init__(
-        self,
-        schema: type[SchemaT] | dict[str, Any],
-    ) -> None:
-        self.schema = schema
-        self.schema_spec = _SchemaSpec(schema)
-
-    def to_model_kwargs(self) -> dict[str, Any]:
-        # OpenAI:
-        # - see https://platform.openai.com/docs/guides/structured-outputs
-        response_format = {
-            "type": "json_schema",
-            "json_schema": {
-                "name": self.schema_spec.name,
-                "schema": self.schema_spec.json_schema,
-            },
-        }
-        return {"response_format": response_format}
-
-
-@dataclass
-class OutputToolBinding(Generic[SchemaT]):
-    """Information for tracking structured output tool metadata.
-
-    This contains all necessary information to handle structured responses
-    generated via tool calls, including the original schema, its type classification,
-    and the corresponding tool implementation used by the tools strategy.
-    """
-
-    schema: type[SchemaT] | dict[str, Any]
-    """The original schema provided for structured output (Pydantic model, dataclass, TypedDict, or JSON schema dict)."""
-
-    schema_kind: SchemaKind
-    """Classification of the schema type for proper response construction."""
-
-    tool: BaseTool
-    """LangChain tool instance created from the schema for model binding."""
-
-    @classmethod
-    def from_schema_spec(cls, schema_spec: _SchemaSpec[SchemaT]) -> Self:
-        """Create an OutputToolBinding instance from a SchemaSpec.
-
-        Args:
-            schema_spec: The SchemaSpec to convert
-
-        Returns:
-            An OutputToolBinding instance with the appropriate tool created
-        """
-        return cls(
-            schema=schema_spec.schema,
-            schema_kind=schema_spec.schema_kind,
-            tool=StructuredTool(
-                args_schema=schema_spec.json_schema,
-                name=schema_spec.name,
-                description=schema_spec.description,
-            ),
-        )
-
-    def parse(self, tool_args: dict[str, Any]) -> SchemaT:
-        """Parse tool arguments according to the schema.
-
-        Args:
-            tool_args: The arguments from the tool call
-
-        Returns:
-            The parsed response according to the schema type
-
-        Raises:
-            ValueError: If parsing fails
-        """
-        return _parse_with_schema(self.schema, self.schema_kind, tool_args)
-
-
-@dataclass
-class NativeOutputBinding(Generic[SchemaT]):
-    """Information for tracking native structured output metadata.
-
-    This contains all necessary information to handle structured responses
-    generated via native provider output, including the original schema,
-    its type classification, and parsing logic for provider-enforced JSON.
-    """
-
-    schema: type[SchemaT] | dict[str, Any]
-    """The original schema provided for structured output (Pydantic model, dataclass, TypedDict, or JSON schema dict)."""
-
-    schema_kind: SchemaKind
-    """Classification of the schema type for proper response construction."""
-
-    @classmethod
-    def from_schema_spec(cls, schema_spec: _SchemaSpec[SchemaT]) -> Self:
-        """Create a NativeOutputBinding instance from a SchemaSpec.
-
-        Args:
-            schema_spec: The SchemaSpec to convert
-
-        Returns:
-            A NativeOutputBinding instance for parsing native structured output
-        """
-        return cls(
-            schema=schema_spec.schema,
-            schema_kind=schema_spec.schema_kind,
-        )
-
-    def parse(self, response: AIMessage) -> SchemaT:
-        """Parse AIMessage content according to the schema.
-
-        Args:
-            response: The AI message containing the structured output
-
-        Returns:
-            The parsed response according to the schema
-
-        Raises:
-            ValueError: If text extraction, JSON parsing or schema validation fails
-        """
-        # Extract text content from AIMessage and parse as JSON
-        raw_text = self._extract_text_content_from_message(response)
-
-        import json
-
-        try:
-            data = json.loads(raw_text)
-        except Exception as e:
-            schema_name = getattr(self.schema, "__name__", "structured_output")
-            raise ValueError(
-                f"Native structured output expected valid JSON for {schema_name}, but parsing failed: {e}."
-            ) from e
-
-        # Parse according to schema
-        return _parse_with_schema(self.schema, self.schema_kind, data)
-
-    def _extract_text_content_from_message(self, message: AIMessage) -> str:
-        """Extract text content from an AIMessage.
-
-        Args:
-            message: The AI message to extract text from
-
-        Returns:
-            The extracted text content
-        """
-        content = message.content
-        if isinstance(content, str):
-            return content
-        if isinstance(content, list):
-            parts: list[str] = []
-            for c in content:
-                if isinstance(c, dict):
-                    if c.get("type") == "text" and "text" in c:
-                        parts.append(str(c["text"]))
-                    elif "content" in c and isinstance(c["content"], str):
-                        parts.append(c["content"])
-                else:
-                    parts.append(str(c))
-            return "".join(parts)
-        return str(content)
-
-
-ResponseFormat = ToolOutput[SchemaT] | NativeOutput[SchemaT]

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -31,22 +31,21 @@ Typical Usage:
     ```
 """
 
-from __future__ import annotations
-
 import asyncio
 import inspect
 import json
-from collections.abc import Callable, Sequence
 from copy import copy, deepcopy
 from dataclasses import replace
 from typing import (
-    Annotated,
     Any,
+    Callable,
     Literal,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
     Union,
     cast,
-    get_args,
-    get_origin,
     get_type_hints,
 )
 
@@ -70,6 +69,7 @@ from langchain_core.tools.base import (
     get_all_basemodel_annotations,
 )
 from pydantic import BaseModel
+from typing_extensions import Annotated, get_args, get_origin
 
 from langgraph._internal._runnable import RunnableCallable
 from langgraph.errors import GraphBubbleUp
@@ -83,7 +83,7 @@ INVALID_TOOL_NAME_ERROR_TEMPLATE = (
 TOOL_CALL_ERROR_TEMPLATE = "Error: {error}\n Please fix your mistakes."
 
 
-def msg_content_output(output: Any) -> str | list[dict]:
+def msg_content_output(output: Any) -> Union[str, list[dict]]:
     """Convert tool output to valid message content format.
 
     LangChain ToolMessages accept either string content or a list of content blocks.
@@ -125,7 +125,12 @@ def msg_content_output(output: Any) -> str | list[dict]:
 def _handle_tool_error(
     e: Exception,
     *,
-    flag: bool | str | Callable[..., str] | tuple[type[Exception], ...],
+    flag: Union[
+        bool,
+        str,
+        Callable[..., str],
+        tuple[type[Exception], ...],
+    ],
 ) -> str:
     """Generate error message content based on exception handling configuration.
 
@@ -151,7 +156,7 @@ def _handle_tool_error(
         The tuple case is handled by the caller through exception type checking,
         not by this function directly.
     """
-    if isinstance(flag, bool | tuple):
+    if isinstance(flag, (bool, tuple)):
         content = TOOL_CALL_ERROR_TEMPLATE.format(error=repr(e))
     elif isinstance(flag, str):
         content = flag
@@ -232,39 +237,17 @@ def _infer_handled_types(handler: Callable[..., str]) -> tuple[type[Exception],
 
 
 class ToolNode(RunnableCallable):
-    """A node for executing tools in LangGraph workflows.
+    """A node that runs the tools called in the last AIMessage.
 
-    Handles tool execution patterns including function calls, state injection,
-    persistent storage, and control flow. Manages parallel execution,
-    error handling.
+    It can be used either in StateGraph with a "messages" state key (or a custom key passed via ToolNode's 'messages_key').
+    If multiple tool calls are requested, they will be run in parallel. The output will be
+    a list of ToolMessages, one for each tool call.
 
-    Input Formats:
-        1. Graph state with `messages` key that has a list of messages:
-           - Common representation for agentic workflows
-           - Supports custom messages key via ``messages_key`` parameter
-
-        2. **Message List**: ``[AIMessage(..., tool_calls=[...])]``
-           - List of messages with tool calls in the last AIMessage
-
-        3. **Direct Tool Calls**: ``[{"name": "tool", "args": {...}, "id": "1", "type": "tool_call"}]``
-           - Bypasses message parsing for direct tool execution
-           - For programmatic tool invocation and testing
-
-    Output Formats:
-        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
+    Tool calls can also be passed directly as a list of `ToolCall` dicts.
 
     Args:
-        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
+        tools: A sequence of tools that can be invoked by this node. Tools can be
+            BaseTool instances or plain functions that will be converted to tools.
         name: The name identifier for this node in the graph. Used for debugging
             and visualization. Defaults to "tools".
         tags: Optional metadata tags to associate with the node for filtering
@@ -272,24 +255,21 @@ class ToolNode(RunnableCallable):
         handle_tool_errors: Configuration for error handling during tool execution.
             Defaults to True. 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.
-            - **tuple[type[Exception], ...]**: Only catch exceptions with the specified
+            - tuple[type[Exception], ...]: Only catch exceptions of 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
-              propagate.
+            - False: Disable error handling entirely, allowing exceptions to propagate.
 
         messages_key: The key in the state dictionary that contains the message list.
-            This same key will be used for the output ToolMessages.
-            Defaults to "messages".
-            Allows custom state schemas with different message field names.
+            This same key will be used for the output ToolMessages. Defaults to "messages".
 
-    Examples:
-        Basic usage:
+    Example:
+        Basic usage with simple tools:
 
         ```python
         from langgraph.prebuilt import ToolNode
@@ -303,42 +283,48 @@ class ToolNode(RunnableCallable):
         tool_node = ToolNode([calculator])
         ```
 
-        State injection:
+        Custom error handling:
 
         ```python
-        from typing_extensions import Annotated
-        from langgraph.prebuilt import InjectedState
+        def handle_math_errors(e: ZeroDivisionError) -> str:
+            return "Cannot divide by zero!"
 
-        @tool
-        def context_tool(query: str, state: Annotated[dict, InjectedState]) -> str:
-            \"\"\"Some tool that uses state.\"\"\"
-            return f"Query: {query}, Messages: {len(state['messages'])}"
-
-        tool_node = ToolNode([context_tool])
+        tool_node = ToolNode([calculator], handle_tool_errors=handle_math_errors)
         ```
 
-        Error handling:
+        Direct tool call execution:
 
         ```python
-        def handle_errors(e: ValueError) -> str:
-            return "Invalid input provided"
-
-        tool_node = ToolNode([my_tool], handle_tool_errors=handle_errors)
+        tool_calls = [{"name": "calculator", "args": {"a": 5, "b": 3}, "id": "1", "type": "tool_call"}]
+        result = tool_node.invoke(tool_calls)
         ```
+
+    Note:
+        The ToolNode expects input in one of three formats:
+        1. A dictionary with a messages key containing a list of messages
+        2. A list of messages directly
+        3. A list of tool call dictionaries
+
+        When using message formats, the last message must be an AIMessage with
+        tool_calls populated. The node automatically extracts and processes these
+        tool calls concurrently.
+
+        For advanced use cases involving state injection or store access, tools
+        can be annotated with InjectedState or InjectedStore to receive graph
+        context automatically.
     """
 
-    name: str = "tools"
+    name: str = "ToolNode"
 
     def __init__(
         self,
-        tools: Sequence[BaseTool | Callable],
+        tools: Sequence[Union[BaseTool, Callable]],
         *,
         name: str = "tools",
-        tags: list[str] | None = None,
-        handle_tool_errors: bool
-        | str
-        | Callable[..., str]
-        | tuple[type[Exception], ...] = True,
+        tags: Optional[list[str]] = None,
+        handle_tool_errors: Union[
+            bool, str, Callable[..., str], tuple[type[Exception], ...]
+        ] = True,
         messages_key: str = "messages",
     ) -> None:
         """Initialize the ToolNode with the provided tools and configuration.
@@ -351,31 +337,28 @@ class ToolNode(RunnableCallable):
             messages_key: State key containing messages.
         """
         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._handle_tool_errors = handle_tool_errors
-        self._messages_key = messages_key
-        for tool in tools:
-            if not isinstance(tool, BaseTool):
-                tool_ = create_tool(cast(type[BaseTool], tool))
-            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_)
-
-    @property
-    def tools_by_name(self) -> dict[str, BaseTool]:
-        """Mapping from tool name to BaseTool instance."""
-        return self._tools_by_name
+        self.tools_by_name: dict[str, BaseTool] = {}
+        self.tool_to_state_args: dict[str, dict[str, Optional[str]]] = {}
+        self.tool_to_store_arg: dict[str, Optional[str]] = {}
+        self.handle_tool_errors = handle_tool_errors
+        self.messages_key = messages_key
+        for tool_ in tools:
+            if not isinstance(tool_, BaseTool):
+                tool_ = create_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_)
 
     def _func(
         self,
-        input: list[AnyMessage] | dict[str, Any] | BaseModel,
+        input: Union[
+            list[AnyMessage],
+            dict[str, Any],
+            BaseModel,
+        ],
         config: RunnableConfig,
         *,
-        store: BaseStore | None,
+        store: Optional[BaseStore],
     ) -> Any:
         tool_calls, input_type = self._parse_input(input, store)
         config_list = get_config_list(config, len(tool_calls))
@@ -389,10 +372,14 @@ class ToolNode(RunnableCallable):
 
     async def _afunc(
         self,
-        input: list[AnyMessage] | dict[str, Any] | BaseModel,
+        input: Union[
+            list[AnyMessage],
+            dict[str, Any],
+            BaseModel,
+        ],
         config: RunnableConfig,
         *,
-        store: BaseStore | None,
+        store: Optional[BaseStore],
     ) -> Any:
         tool_calls, input_type = self._parse_input(input, store)
         outputs = await asyncio.gather(
@@ -403,14 +390,14 @@ class ToolNode(RunnableCallable):
 
     def _combine_tool_outputs(
         self,
-        outputs: list[ToolMessage | Command],
+        outputs: list[ToolMessage],
         input_type: Literal["list", "dict", "tool_calls"],
-    ) -> list[Command | list[ToolMessage] | dict[str, list[ToolMessage]]]:
+    ) -> list[Union[Command, list[ToolMessage], dict[str, list[ToolMessage]]]]:
         # preserve existing behavior for non-command tool outputs for backwards
         # compatibility
         if not any(isinstance(output, Command) for output in outputs):
             # TypedDict, pydantic, dataclass, etc. should all be able to load from dict
-            return outputs if input_type == "list" else {self._messages_key: outputs}
+            return outputs if input_type == "list" else {self.messages_key: outputs}
 
         # LangGraph will automatically handle list of Command and non-command node
         # updates
@@ -419,7 +406,7 @@ class ToolNode(RunnableCallable):
         ] = []
 
         # combine all parent commands with goto into a single parent command
-        parent_command: Command | None = None
+        parent_command: Optional[Command] = None
         for output in outputs:
             if isinstance(output, Command):
                 if (
@@ -438,7 +425,7 @@ class ToolNode(RunnableCallable):
                     combined_outputs.append(output)
             else:
                 combined_outputs.append(
-                    [output] if input_type == "list" else {self._messages_key: [output]}
+                    [output] if input_type == "list" else {self.messages_key: [output]}
                 )
 
         if parent_command:
@@ -450,15 +437,13 @@ class ToolNode(RunnableCallable):
         call: ToolCall,
         input_type: Literal["list", "dict", "tool_calls"],
         config: RunnableConfig,
-    ) -> ToolMessage | Command:
+    ) -> ToolMessage:
         """Run a single tool call synchronously."""
         if invalid_tool_message := self._validate_tool_call(call):
             return invalid_tool_message
-
         try:
             call_args = {**call, **{"type": "tool_call"}}
-            tool = self.tools_by_name[call["name"]]
-            response = tool.invoke(call_args, config)
+            response = self.tools_by_name[call["name"]].invoke(call_args, config)
 
         # GraphInterrupt is a special exception that will always be raised.
         # It can be triggered in the following scenarios,
@@ -470,20 +455,20 @@ class ToolNode(RunnableCallable):
         except GraphBubbleUp as e:
             raise e
         except Exception as e:
-            if isinstance(self._handle_tool_errors, tuple):
-                handled_types: tuple = self._handle_tool_errors
-            elif callable(self._handle_tool_errors):
-                handled_types = _infer_handled_types(self._handle_tool_errors)
+            if isinstance(self.handle_tool_errors, tuple):
+                handled_types: tuple = self.handle_tool_errors
+            elif callable(self.handle_tool_errors):
+                handled_types = _infer_handled_types(self.handle_tool_errors)
             else:
                 # default behavior is catching all exceptions
                 handled_types = (Exception,)
 
             # Unhandled
-            if not self._handle_tool_errors or not isinstance(e, handled_types):
+            if not self.handle_tool_errors or not isinstance(e, handled_types):
                 raise e
             # Handled
             else:
-                content = _handle_tool_error(e, flag=self._handle_tool_errors)
+                content = _handle_tool_error(e, flag=self.handle_tool_errors)
             return ToolMessage(
                 content=content,
                 name=call["name"],
@@ -494,7 +479,9 @@ class ToolNode(RunnableCallable):
         if isinstance(response, Command):
             return self._validate_tool_command(response, call, input_type)
         elif isinstance(response, ToolMessage):
-            response.content = cast(str | list, msg_content_output(response.content))
+            response.content = cast(
+                Union[str, list], msg_content_output(response.content)
+            )
             return response
         else:
             raise TypeError(
@@ -506,15 +493,15 @@ class ToolNode(RunnableCallable):
         call: ToolCall,
         input_type: Literal["list", "dict", "tool_calls"],
         config: RunnableConfig,
-    ) -> ToolMessage | Command:
+    ) -> ToolMessage:
         """Run a single tool call asynchronously."""
         if invalid_tool_message := self._validate_tool_call(call):
             return invalid_tool_message
 
         try:
             call_args = {**call, **{"type": "tool_call"}}
-            tool = self.tools_by_name[call["name"]]
-            response = await tool.ainvoke(call_args, config)
+            response = await self.tools_by_name[call["name"]].ainvoke(call_args, config)
+
         # GraphInterrupt is a special exception that will always be raised.
         # It can be triggered in the following scenarios,
         # Where GraphInterrupt(GraphBubbleUp) is raised from an `interrupt` invocation most commonly:
@@ -525,20 +512,20 @@ class ToolNode(RunnableCallable):
         except GraphBubbleUp as e:
             raise e
         except Exception as e:
-            if isinstance(self._handle_tool_errors, tuple):
-                handled_types: tuple = self._handle_tool_errors
-            elif callable(self._handle_tool_errors):
-                handled_types = _infer_handled_types(self._handle_tool_errors)
+            if isinstance(self.handle_tool_errors, tuple):
+                handled_types: tuple = self.handle_tool_errors
+            elif callable(self.handle_tool_errors):
+                handled_types = _infer_handled_types(self.handle_tool_errors)
             else:
                 # default behavior is catching all exceptions
                 handled_types = (Exception,)
 
             # Unhandled
-            if not self._handle_tool_errors or not isinstance(e, handled_types):
+            if not self.handle_tool_errors or not isinstance(e, handled_types):
                 raise e
             # Handled
             else:
-                content = _handle_tool_error(e, flag=self._handle_tool_errors)
+                content = _handle_tool_error(e, flag=self.handle_tool_errors)
 
             return ToolMessage(
                 content=content,
@@ -550,7 +537,9 @@ class ToolNode(RunnableCallable):
         if isinstance(response, Command):
             return self._validate_tool_command(response, call, input_type)
         elif isinstance(response, ToolMessage):
-            response.content = cast(str | list, msg_content_output(response.content))
+            response.content = cast(
+                Union[str, list], msg_content_output(response.content)
+            )
             return response
         else:
             raise TypeError(
@@ -559,9 +548,13 @@ class ToolNode(RunnableCallable):
 
     def _parse_input(
         self,
-        input: list[AnyMessage] | dict[str, Any] | BaseModel,
-        store: BaseStore | None,
-    ) -> tuple[list[ToolCall], Literal["list", "dict", "tool_calls"]]:
+        input: Union[
+            list[AnyMessage],
+            dict[str, Any],
+            BaseModel,
+        ],
+        store: Optional[BaseStore],
+    ) -> Tuple[list[ToolCall], Literal["list", "dict", "tool_calls"]]:
         input_type: Literal["list", "dict", "tool_calls"]
         if isinstance(input, list):
             if isinstance(input[-1], dict) and input[-1].get("type") == "tool_call":
@@ -571,11 +564,9 @@ class ToolNode(RunnableCallable):
             else:
                 input_type = "list"
                 messages = input
-        elif isinstance(input, dict) and (
-            messages := input.get(self._messages_key, [])
-        ):
+        elif isinstance(input, dict) and (messages := input.get(self.messages_key, [])):
             input_type = "dict"
-        elif messages := getattr(input, self._messages_key, []):
+        elif messages := getattr(input, self.messages_key, []):
             # Assume dataclass-like state that can coerce from dict
             input_type = "dict"
         else:
@@ -594,13 +585,11 @@ class ToolNode(RunnableCallable):
         ]
         return tool_calls, input_type
 
-    def _validate_tool_call(self, call: ToolCall) -> ToolMessage | None:
-        requested_tool = call["name"]
-        if requested_tool not in self.tools_by_name:
-            all_tool_names = list(self.tools_by_name.keys())
+    def _validate_tool_call(self, call: ToolCall) -> Optional[ToolMessage]:
+        if (requested_tool := call["name"]) not in self.tools_by_name:
             content = INVALID_TOOL_NAME_ERROR_TEMPLATE.format(
                 requested_tool=requested_tool,
-                available_tools=", ".join(all_tool_names),
+                available_tools=", ".join(self.tools_by_name.keys()),
             )
             return ToolMessage(
                 content, name=requested_tool, tool_call_id=call["id"], status="error"
@@ -611,17 +600,21 @@ class ToolNode(RunnableCallable):
     def _inject_state(
         self,
         tool_call: ToolCall,
-        input: list[AnyMessage] | dict[str, Any] | BaseModel,
+        input: Union[
+            list[AnyMessage],
+            dict[str, Any],
+            BaseModel,
+        ],
     ) -> ToolCall:
-        state_args = self._tool_to_state_args[tool_call["name"]]
+        state_args = self.tool_to_state_args[tool_call["name"]]
         if state_args and isinstance(input, list):
             required_fields = list(state_args.values())
             if (
                 len(required_fields) == 1
-                and required_fields[0] == self._messages_key
+                and required_fields[0] == self.messages_key
                 or required_fields[0] is None
             ):
-                input = {self._messages_key: input}
+                input = {self.messages_key: input}
             else:
                 err_msg = (
                     f"Invalid input to ToolNode. Tool {tool_call['name']} requires "
@@ -649,8 +642,10 @@ class ToolNode(RunnableCallable):
         }
         return tool_call
 
-    def _inject_store(self, tool_call: ToolCall, store: BaseStore | None) -> ToolCall:
-        store_arg = self._tool_to_store_arg[tool_call["name"]]
+    def _inject_store(
+        self, tool_call: ToolCall, store: Optional[BaseStore]
+    ) -> ToolCall:
+        store_arg = self.tool_to_store_arg[tool_call["name"]]
         if not store_arg:
             return tool_call
 
@@ -669,8 +664,12 @@ class ToolNode(RunnableCallable):
     def inject_tool_args(
         self,
         tool_call: ToolCall,
-        input: list[AnyMessage] | dict[str, Any] | BaseModel,
-        store: BaseStore | None,
+        input: Union[
+            list[AnyMessage],
+            dict[str, Any],
+            BaseModel,
+        ],
+        store: Optional[BaseStore],
     ) -> ToolCall:
         """Inject graph state and store into tool call arguments.
 
@@ -723,15 +722,15 @@ class ToolNode(RunnableCallable):
             # input type is dict when ToolNode is invoked with a dict input (e.g. {"messages": [AIMessage(..., tool_calls=[...])]})
             if input_type not in ("dict", "tool_calls"):
                 raise ValueError(
-                    f"Tools can provide a dict in Command.update only when using dict with '{self._messages_key}' key as ToolNode input, "
+                    f"Tools can provide a dict in Command.update only when using dict with '{self.messages_key}' key as ToolNode input, "
                     f"got: {command.update} for tool '{call['name']}'"
                 )
 
             updated_command = deepcopy(command)
             state_update = cast(dict[str, Any], updated_command.update) or {}
-            messages_update = state_update.get(self._messages_key, [])
+            messages_update = state_update.get(self.messages_key, [])
         elif isinstance(command.update, list):
-            # Input type is list when ToolNode is invoked with a list input (e.g. [AIMessage(..., tool_calls=[...])])
+            # input type is list when ToolNode is invoked with a list input (e.g. [AIMessage(..., tool_calls=[...])])
             if input_type != "list":
                 raise ValueError(
                     f"Tools can provide a list of messages in Command.update only when using list of messages as ToolNode input, "
@@ -776,7 +775,7 @@ class ToolNode(RunnableCallable):
 
 
 def tools_condition(
-    state: list[AnyMessage] | dict[str, Any] | BaseModel,
+    state: Union[list[AnyMessage], dict[str, Any], BaseModel],
     messages_key: str = "messages",
 ) -> Literal["tools", "__end__"]:
     """Conditional routing function for tool-calling workflows.
@@ -922,7 +921,7 @@ class InjectedState(InjectedToolArg):
           tool execution
     """  # noqa: E501
 
-    def __init__(self, field: str | None = None) -> None:
+    def __init__(self, field: Optional[str] = None) -> None:
         self.field = field
 
 
@@ -1003,7 +1002,7 @@ class InjectedStore(InjectedToolArg):
 
 
 def _is_injection(
-    type_arg: Any, injection_type: type[InjectedState] | type[InjectedStore]
+    type_arg: Any, injection_type: Union[Type[InjectedState], Type[InjectedStore]]
 ) -> bool:
     """Check if a type argument represents an injection annotation.
 
@@ -1028,7 +1027,7 @@ def _is_injection(
     return False
 
 
-def _get_state_args(tool: BaseTool) -> dict[str, str | None]:
+def _get_state_args(tool: BaseTool) -> dict[str, Optional[str]]:
     """Extract state injection mappings from tool annotations.
 
     This function analyzes a tool's input schema to identify arguments that should
@@ -1067,7 +1066,7 @@ def _get_state_args(tool: BaseTool) -> dict[str, str | None]:
     return tool_args_to_state_fields
 
 
-def _get_store_arg(tool: BaseTool) -> str | None:
+def _get_store_arg(tool: BaseTool) -> Optional[str]:
     """Extract store injection argument from tool annotations.
 
     This function analyzes a tool's input schema to identify the argument that

libs/prebuilt/langgraph/prebuilt/tool_validator.py

@@ -5,9 +5,15 @@ returns a ToolMessage with the error message. The ValidationNode can be used in
 StateGraph with a "messages" key. If multiple tool calls are requested, they will be run in parallel.
 """
 
-from collections.abc import Callable, Sequence
 from typing import (
     Any,
+    Callable,
+    Dict,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
     cast,
 )
 
@@ -33,7 +39,7 @@ from langgraph._internal._runnable import RunnableCallable
 def _default_format_error(
     error: BaseException,
     call: ToolCall,
-    schema: type[BaseModel] | type[BaseModelV1],
+    schema: Union[Type[BaseModel], Type[BaseModelV1]],
 ) -> str:
     """Default error formatting function."""
     return f"{repr(error)}\n\nRespond after fixing all validation errors."
@@ -121,16 +127,17 @@ class ValidationNode(RunnableCallable):
 
     def __init__(
         self,
-        schemas: Sequence[BaseTool | type[BaseModel] | Callable],
+        schemas: Sequence[Union[BaseTool, Type[BaseModel], Callable]],
         *,
-        format_error: Callable[[BaseException, ToolCall, type[BaseModel]], str]
-        | None = None,
+        format_error: Optional[
+            Callable[[BaseException, ToolCall, Type[BaseModel]], str]
+        ] = None,
         name: str = "validation",
-        tags: list[str] | None = None,
+        tags: Optional[list[str]] = None,
     ) -> None:
         super().__init__(self._func, None, name=name, tags=tags, trace=False)
         self._format_error = format_error or _default_format_error
-        self.schemas_by_name: dict[str, type[BaseModel]] = {}
+        self.schemas_by_name: Dict[str, Type[BaseModel]] = {}
         for schema in schemas:
             if isinstance(schema, BaseTool):
                 if schema.args_schema is None:
@@ -146,9 +153,9 @@ class ValidationNode(RunnableCallable):
                     )
                 self.schemas_by_name[schema.name] = schema.args_schema
             elif isinstance(schema, type) and issubclass(
-                schema, BaseModel | BaseModelV1
+                schema, (BaseModel, BaseModelV1)
             ):
-                self.schemas_by_name[schema.__name__] = cast(type[BaseModel], schema)
+                self.schemas_by_name[schema.__name__] = cast(Type[BaseModel], schema)
             elif callable(schema):
                 base_model = create_schema_from_function("Validation", schema)
                 self.schemas_by_name[schema.__name__] = base_model
@@ -158,8 +165,8 @@ class ValidationNode(RunnableCallable):
                 )
 
     def _get_message(
-        self, input: list[AnyMessage] | dict[str, Any]
-    ) -> tuple[str, AIMessage]:
+        self, input: Union[list[AnyMessage], dict[str, Any]]
+    ) -> Tuple[str, AIMessage]:
         """Extract the last AIMessage from the input."""
         if isinstance(input, list):
             output_type = "list"
@@ -174,7 +181,7 @@ class ValidationNode(RunnableCallable):
         return output_type, message
 
     def _func(
-        self, input: list[AnyMessage] | dict[str, Any], config: RunnableConfig
+        self, input: Union[list[AnyMessage], dict[str, Any]], config: RunnableConfig
     ) -> Any:
         """Validate and run tool calls synchronously."""
         output_type, message = self._get_message(input)

libs/prebuilt/pyproject.toml

@@ -7,7 +7,7 @@ name = "langgraph-prebuilt"
 version = "0.6.4"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
-requires-python = ">=3.10"
+requires-python = ">=3.9"
 readme = "README.md"
 license = "MIT"
 license-files = ['LICENSE']
@@ -52,9 +52,8 @@ addopts = "--strict-markers --strict-config --durations=5 -vv"
 asyncio_mode = "auto"
 
 [tool.ruff]
-lint.select = [ "E", "F", "I", "TID251", "UP" ]
+lint.select = [ "E", "F", "I", "TID251" ]
 lint.ignore = [ "E501" ]
-target-version = "py310"
 
 [tool.pytest-watcher]
 now = true

libs/prebuilt/tests/__snapshots__/test_react_agent_graph.ambr

@@ -1,83 +1,173 @@
 # serializer version: 1
-# name: test_react_agent_graph_structure[None-None-tools0]
+# name: test_react_agent_graph_structure[None-None-None-tools0]
   '''
   graph TD;
-  	__start__ --> model;
-  	model --> __end__;
+  	__start__ --> agent;
+  	agent --> __end__;
   
   '''
 # ---
-# name: test_react_agent_graph_structure[None-None-tools1]
+# name: test_react_agent_graph_structure[None-None-None-tools1]
   '''
   graph TD;
-  	__start__ --> model;
-  	model -.-> __end__;
-  	model -.-> tools;
-  	tools --> model;
+  	__start__ --> agent;
+  	agent -.-> __end__;
+  	agent -.-> tools;
+  	tools --> agent;
   
   '''
 # ---
-# name: test_react_agent_graph_structure[None-pre_model_hook-tools0]
+# name: test_react_agent_graph_structure[None-None-pre_model_hook-tools0]
   '''
   graph TD;
   	__start__ --> pre_model_hook;
-  	pre_model_hook --> model;
-  	model --> __end__;
+  	pre_model_hook --> agent;
+  	agent --> __end__;
   
   '''
 # ---
-# name: test_react_agent_graph_structure[None-pre_model_hook-tools1]
+# name: test_react_agent_graph_structure[None-None-pre_model_hook-tools1]
   '''
   graph TD;
   	__start__ --> pre_model_hook;
-  	model -.-> __end__;
-  	model -.-> tools;
-  	pre_model_hook --> model;
+  	agent -.-> __end__;
+  	agent -.-> tools;
+  	pre_model_hook --> agent;
   	tools --> pre_model_hook;
   
   '''
 # ---
-# name: test_react_agent_graph_structure[post_model_hook-None-tools0]
+# name: test_react_agent_graph_structure[None-post_model_hook-None-tools0]
   '''
   graph TD;
-  	__start__ --> model;
-  	model --> post_model_hook;
+  	__start__ --> agent;
+  	agent --> post_model_hook;
   	post_model_hook --> __end__;
   
   '''
 # ---
-# name: test_react_agent_graph_structure[post_model_hook-None-tools1]
+# name: test_react_agent_graph_structure[None-post_model_hook-None-tools1]
   '''
   graph TD;
-  	__start__ --> model;
-  	model --> post_model_hook;
+  	__start__ --> agent;
+  	agent --> post_model_hook;
   	post_model_hook -.-> __end__;
-  	post_model_hook -.-> model;
+  	post_model_hook -.-> agent;
   	post_model_hook -.-> tools;
-  	tools --> model;
+  	tools --> agent;
   
   '''
 # ---
-# name: test_react_agent_graph_structure[post_model_hook-pre_model_hook-tools0]
+# name: test_react_agent_graph_structure[None-post_model_hook-pre_model_hook-tools0]
   '''
   graph TD;
   	__start__ --> pre_model_hook;
-  	model --> post_model_hook;
-  	pre_model_hook --> model;
+  	agent --> post_model_hook;
+  	pre_model_hook --> agent;
   	post_model_hook --> __end__;
   
   '''
 # ---
-# name: test_react_agent_graph_structure[post_model_hook-pre_model_hook-tools1]
+# name: test_react_agent_graph_structure[None-post_model_hook-pre_model_hook-tools1]
   '''
   graph TD;
   	__start__ --> pre_model_hook;
-  	model --> post_model_hook;
+  	agent --> post_model_hook;
   	post_model_hook -.-> __end__;
   	post_model_hook -.-> pre_model_hook;
   	post_model_hook -.-> tools;
-  	pre_model_hook --> model;
+  	pre_model_hook --> agent;
   	tools --> pre_model_hook;
   
   '''
 # ---
+# name: test_react_agent_graph_structure[ResponseFormat-None-None-tools0]
+  '''
+  graph TD;
+  	__start__ --> agent;
+  	agent --> generate_structured_response;
+  	generate_structured_response --> __end__;
+  
+  '''
+# ---
+# name: test_react_agent_graph_structure[ResponseFormat-None-None-tools1]
+  '''
+  graph TD;
+  	__start__ --> agent;
+  	agent -.-> generate_structured_response;
+  	agent -.-> tools;
+  	tools --> agent;
+  	generate_structured_response --> __end__;
+  
+  '''
+# ---
+# name: test_react_agent_graph_structure[ResponseFormat-None-pre_model_hook-tools0]
+  '''
+  graph TD;
+  	__start__ --> pre_model_hook;
+  	agent --> generate_structured_response;
+  	pre_model_hook --> agent;
+  	generate_structured_response --> __end__;
+  
+  '''
+# ---
+# name: test_react_agent_graph_structure[ResponseFormat-None-pre_model_hook-tools1]
+  '''
+  graph TD;
+  	__start__ --> pre_model_hook;
+  	agent -.-> generate_structured_response;
+  	agent -.-> tools;
+  	pre_model_hook --> agent;
+  	tools --> pre_model_hook;
+  	generate_structured_response --> __end__;
+  
+  '''
+# ---
+# name: test_react_agent_graph_structure[ResponseFormat-post_model_hook-None-tools0]
+  '''
+  graph TD;
+  	__start__ --> agent;
+  	agent --> post_model_hook;
+  	post_model_hook --> generate_structured_response;
+  	generate_structured_response --> __end__;
+  
+  '''
+# ---
+# name: test_react_agent_graph_structure[ResponseFormat-post_model_hook-None-tools1]
+  '''
+  graph TD;
+  	__start__ --> agent;
+  	agent --> post_model_hook;
+  	post_model_hook -.-> agent;
+  	post_model_hook -.-> generate_structured_response;
+  	post_model_hook -.-> tools;
+  	tools --> agent;
+  	generate_structured_response --> __end__;
+  
+  '''
+# ---
+# name: test_react_agent_graph_structure[ResponseFormat-post_model_hook-pre_model_hook-tools0]
+  '''
+  graph TD;
+  	__start__ --> pre_model_hook;
+  	agent --> post_model_hook;
+  	post_model_hook --> generate_structured_response;
+  	pre_model_hook --> agent;
+  	generate_structured_response --> __end__;
+  
+  '''
+# ---
+# name: test_react_agent_graph_structure[ResponseFormat-post_model_hook-pre_model_hook-tools1]
+  '''
+  graph TD;
+  	__start__ --> pre_model_hook;
+  	agent --> post_model_hook;
+  	post_model_hook -.-> generate_structured_response;
+  	post_model_hook -.-> pre_model_hook;
+  	post_model_hook -.-> tools;
+  	pre_model_hook --> agent;
+  	tools --> pre_model_hook;
+  	generate_structured_response --> __end__;
+  
+  '''
+# ---

libs/prebuilt/tests/any_str.py

@@ -1,8 +1,9 @@
 import re
+from typing import Union
 
 
 class AnyStr(str):
-    def __init__(self, prefix: str | re.Pattern = "") -> None:
+    def __init__(self, prefix: Union[str, re.Pattern] = "") -> None:
         super().__init__()
         self.prefix = prefix
 

libs/prebuilt/tests/conftest_checkpointer.py

@@ -1,6 +1,8 @@
+import sys
 from contextlib import asynccontextmanager, contextmanager
 from uuid import uuid4
 
+import pytest
 from psycopg import AsyncConnection, Connection
 from psycopg_pool import AsyncConnectionPool, ConnectionPool
 
@@ -93,6 +95,8 @@ async def _checkpointer_sqlite_aio():
 
 @asynccontextmanager
 async def _checkpointer_postgres_aio():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
     database = f"test_{uuid4().hex[:16]}"
     # create unique db
     async with await AsyncConnection.connect(
@@ -116,6 +120,8 @@ async def _checkpointer_postgres_aio():
 
 @asynccontextmanager
 async def _checkpointer_postgres_aio_pipe():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
     database = f"test_{uuid4().hex[:16]}"
     # create unique db
     async with await AsyncConnection.connect(
@@ -142,6 +148,8 @@ async def _checkpointer_postgres_aio_pipe():
 
 @asynccontextmanager
 async def _checkpointer_postgres_aio_pool():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
     database = f"test_{uuid4().hex[:16]}"
     # create unique db
     async with await AsyncConnection.connect(

libs/prebuilt/tests/conftest_store.py

@@ -1,6 +1,8 @@
+import sys
 from contextlib import asynccontextmanager, contextmanager
 from uuid import uuid4
 
+import pytest
 from psycopg import AsyncConnection, Connection
 
 from langgraph.store.memory import InMemoryStore
@@ -73,6 +75,8 @@ def _store_postgres_pool():
 
 @asynccontextmanager
 async def _store_postgres_aio():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
     database = f"test_{uuid4().hex[:16]}"
     async with await AsyncConnection.connect(
         DEFAULT_POSTGRES_URI, autocommit=True
@@ -93,6 +97,8 @@ async def _store_postgres_aio():
 
 @asynccontextmanager
 async def _store_postgres_aio_pipe():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
     database = f"test_{uuid4().hex[:16]}"
     async with await AsyncConnection.connect(
         DEFAULT_POSTGRES_URI, autocommit=True
@@ -116,6 +122,8 @@ async def _store_postgres_aio_pipe():
 
 @asynccontextmanager
 async def _store_postgres_aio_pool():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
     database = f"test_{uuid4().hex[:16]}"
     async with await AsyncConnection.connect(
         DEFAULT_POSTGRES_URI, autocommit=True

libs/prebuilt/tests/memory_assert.py

@@ -2,6 +2,7 @@ import os
 import tempfile
 from collections import defaultdict
 from functools import partial
+from typing import Optional
 
 from langgraph.checkpoint.base import (
     ChannelVersions,
@@ -19,8 +20,8 @@ class MemorySaverAssertImmutable(InMemorySaver):
     def __init__(
         self,
         *,
-        serde: SerializerProtocol | None = None,
-        put_sleep: float | None = None,
+        serde: Optional[SerializerProtocol] = None,
+        put_sleep: Optional[float] = None,
     ) -> None:
         _, filename = tempfile.mkstemp()
         super().__init__(

libs/prebuilt/tests/model.py

@@ -1,10 +1,13 @@
-import json
-from collections.abc import Callable, Sequence
-from dataclasses import asdict, is_dataclass
 from typing import (
     Any,
-    Generic,
+    Callable,
+    Dict,
+    List,
     Literal,
+    Optional,
+    Sequence,
+    Type,
+    Union,
 )
 
 from langchain_core.callbacks import CallbackManagerForLLMRun
@@ -15,59 +18,36 @@ from langchain_core.messages import (
     ToolCall,
 )
 from langchain_core.outputs import ChatGeneration, ChatResult
-from langchain_core.runnables import Runnable
+from langchain_core.runnables import Runnable, RunnableLambda
 from langchain_core.tools import BaseTool
 from pydantic import BaseModel
 
-from langgraph.prebuilt.chat_agent_executor import StructuredResponseT
+from langgraph.prebuilt.chat_agent_executor import StructuredResponse
 
 
-class FakeToolCallingModel(BaseChatModel, Generic[StructuredResponseT]):
-    tool_calls: list[list[ToolCall]] | list[list[dict]] | None = None
-    structured_response: StructuredResponseT | None = None
+class FakeToolCallingModel(BaseChatModel):
+    tool_calls: Optional[list[list[ToolCall]]] = None
+    structured_response: Optional[StructuredResponse] = None
     index: int = 0
     tool_style: Literal["openai", "anthropic"] = "openai"
 
     def _generate(
         self,
-        messages: list[BaseMessage],
-        stop: list[str] | None = None,
-        run_manager: CallbackManagerForLLMRun | None = None,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> ChatResult:
         """Top Level call"""
-        rf = kwargs.get("response_format")
-        is_native = isinstance(rf, dict) and rf.get("type") == "json_schema"
-        if is_native:
-            print("NATIVE. tool_calls: ", self.tool_calls)
-
-        if self.tool_calls:
-            if is_native:
-                tool_calls = (
-                    self.tool_calls[self.index]
-                    if self.index < len(self.tool_calls)
-                    else []
-                )
-            else:
-                tool_calls = self.tool_calls[self.index % len(self.tool_calls)]
-        else:
-            tool_calls = []
-
-        if is_native and not tool_calls:
-            if isinstance(self.structured_response, BaseModel):
-                content_obj = self.structured_response.model_dump()
-            elif is_dataclass(self.structured_response):
-                content_obj = asdict(self.structured_response)
-            elif isinstance(self.structured_response, dict):
-                content_obj = self.structured_response
-            message = AIMessage(content=json.dumps(content_obj), id=str(self.index))
-        else:
-            messages_string = "-".join([m.content for m in messages])
-            message = AIMessage(
-                content=messages_string,
-                id=str(self.index),
-                tool_calls=tool_calls.copy(),
-            )
+        messages_string = "-".join([m.content for m in messages])
+        tool_calls = (
+            self.tool_calls[self.index % len(self.tool_calls)]
+            if self.tool_calls
+            else []
+        )
+        message = AIMessage(
+            content=messages_string, id=str(self.index), tool_calls=tool_calls.copy()
+        )
         self.index += 1
         return ChatResult(generations=[ChatGeneration(message=message)])
 
@@ -75,9 +55,17 @@ class FakeToolCallingModel(BaseChatModel, Generic[StructuredResponseT]):
     def _llm_type(self) -> str:
         return "fake-tool-call-model"
 
+    def with_structured_output(
+        self, schema: Type[BaseModel]
+    ) -> Runnable[LanguageModelInput, StructuredResponse]:
+        if self.structured_response is None:
+            raise ValueError("Structured response is not set")
+
+        return RunnableLambda(lambda x: self.structured_response)
+
     def bind_tools(
         self,
-        tools: Sequence[dict[str, Any] | type[BaseModel] | Callable | BaseTool],
+        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
         **kwargs: Any,
     ) -> Runnable[LanguageModelInput, BaseMessage]:
         if len(tools) == 0:

libs/prebuilt/tests/specifications/responses.json

@@ -1,46 +0,0 @@
-[
-    {
-      "name": "updated structured response",
-      "responseFormat": [
-        {
-          "type": "object",
-          "properties": {
-            "name": { "type": "string" },
-            "role": { "type": "string" }
-          },
-          "required": ["name", "role"]
-        },
-        {
-          "type": "object",
-          "properties": {
-            "name": { "type": "string" },
-            "department": { "type": "string" }
-          },
-          "required": ["name", "department"]
-        }
-      ],
-      "assertionsByInvocation": [
-        {
-          "prompt": "What is the role of Sabine?",
-          "toolsWithExpectedCalls": {
-            "getEmployeeRole": 1,
-            "getEmployeeDepartment": 0
-          },
-          "expectedLastMessage": "Returning structured response: {'name': 'Sabine', 'role': 'Developer'}",
-          "expectedStructuredResponse": { "name": "Sabine", "role": "Developer" },
-          "llmRequestCount": 2
-        },
-        {
-          "prompt": "In which department does Henrik work?",
-          "toolsWithExpectedCalls": {
-            "getEmployeeRole": 1,
-            "getEmployeeDepartment": 1
-          },
-          "expectedLastMessage": "Returning structured response: {'name': 'Henrik', 'department': 'IT'}",
-          "expectedStructuredResponse": { "name": "Henrik", "department": "IT" },
-          "llmRequestCount": 4
-        }
-      ]
-    }
-    
-  ]

libs/prebuilt/tests/test_deprecation.py

@@ -0,0 +1,41 @@
+import pytest
+from typing_extensions import TypedDict
+
+from langgraph.prebuilt import create_react_agent
+from langgraph.warnings import LangGraphDeprecatedSinceV10
+from tests.model import FakeToolCallingModel
+
+
+class Config(TypedDict):
+    model: str
+
+
+@pytest.mark.filterwarnings("ignore:`config_schema` is deprecated")
+@pytest.mark.filterwarnings("ignore:`get_config_jsonschema` is deprecated")
+def test_config_schema_deprecation() -> None:
+    with pytest.warns(
+        LangGraphDeprecatedSinceV10,
+        match="`config_schema` is deprecated and will be removed. Please use `context_schema` instead.",
+    ):
+        agent = create_react_agent(FakeToolCallingModel(), [], config_schema=Config)
+        assert agent.context_schema == Config
+
+    with pytest.warns(
+        LangGraphDeprecatedSinceV10,
+        match="`config_schema` is deprecated. Use `get_context_jsonschema` for the relevant schema instead.",
+    ):
+        assert agent.config_schema() is not None
+
+    with pytest.warns(
+        LangGraphDeprecatedSinceV10,
+        match="`get_config_jsonschema` is deprecated. Use `get_context_jsonschema` instead.",
+    ):
+        assert agent.get_config_jsonschema() is not None
+
+
+def test_extra_kwargs_deprecation() -> None:
+    with pytest.raises(
+        TypeError,
+        match="create_react_agent\(\) got unexpected keyword arguments: \{'extra': 'extra'\}",
+    ):
+        create_react_agent(FakeToolCallingModel(), [], extra="extra")

libs/prebuilt/tests/test_react_agent_graph.py

@@ -1,10 +1,10 @@
-from collections.abc import Callable
+from typing import Callable, Union
 
 import pytest
 from pydantic import BaseModel
 from syrupy import SnapshotAssertion
 
-from langgraph.prebuilt import create_agent
+from langgraph.prebuilt import create_react_agent
 from tests.model import FakeToolCallingModel
 
 model = FakeToolCallingModel()
@@ -34,25 +34,19 @@ class ResponseFormat(BaseModel):
 @pytest.mark.parametrize("tools", [[], [tool]])
 @pytest.mark.parametrize("pre_model_hook", [None, pre_model_hook])
 @pytest.mark.parametrize("post_model_hook", [None, post_model_hook])
+@pytest.mark.parametrize("response_format", [None, ResponseFormat])
 def test_react_agent_graph_structure(
     snapshot: SnapshotAssertion,
     tools: list[Callable],
-    pre_model_hook: Callable | None,
-    post_model_hook: Callable | None,
+    pre_model_hook: Union[Callable, None],
+    post_model_hook: Union[Callable, None],
+    response_format: Union[type[BaseModel], None],
 ) -> None:
-    agent = create_agent(
+    agent = create_react_agent(
         model,
         tools=tools,
         pre_model_hook=pre_model_hook,
         post_model_hook=post_model_hook,
+        response_format=response_format,
     )
-    try:
-        assert agent.get_graph().draw_mermaid(with_styles=False) == snapshot
-    except Exception as e:
-        raise ValueError(
-            "The graph structure has changed. Please update the snapshot."
-            "Configuration used:\n"
-            f"tools: {tools}, "
-            f"pre_model_hook: {pre_model_hook}, "
-            f"post_model_hook: {post_model_hook}, "
-        ) from e
+    assert agent.get_graph().draw_mermaid(with_styles=False) == snapshot

libs/prebuilt/tests/test_response_format.py

@@ -1,504 +0,0 @@
-"""Test suite for create_react_agent with structured output response_format permutations."""
-
-from dataclasses import dataclass
-
-import pytest
-from langchain_core.messages import HumanMessage
-from pydantic import BaseModel, Field
-from typing_extensions import TypedDict
-
-from langgraph.prebuilt import create_agent
-from langgraph.prebuilt.responses import NativeOutput, ToolOutput
-from tests.model import FakeToolCallingModel
-
-try:
-    from langchain_openai import ChatOpenAI
-except ImportError:
-    skip_openai_integration_tests = True
-else:
-    skip_openai_integration_tests = False
-
-
-# Test data models
-class WeatherBaseModel(BaseModel):
-    """Weather response."""
-
-    temperature: float = Field(description="The temperature in fahrenheit")
-    condition: str = Field(description="Weather condition")
-
-
-@dataclass
-class WeatherDataclass:
-    """Weather response."""
-
-    temperature: float
-    condition: str
-
-
-class WeatherTypedDict(TypedDict):
-    """Weather response."""
-
-    temperature: float
-    condition: str
-
-
-weather_json_schema = {
-    "type": "object",
-    "properties": {
-        "temperature": {"type": "number", "description": "Temperature in fahrenheit"},
-        "condition": {"type": "string", "description": "Weather condition"},
-    },
-    "title": "weather_schema",
-    "required": ["temperature", "condition"],
-}
-
-
-class LocationResponse(BaseModel):
-    city: str = Field(description="The city name")
-    country: str = Field(description="The country name")
-
-
-def get_weather() -> str:
-    """Get the weather."""
-
-    return "The weather is sunny and 75°F."
-
-
-def get_location() -> str:
-    """Get the current location."""
-
-    return "You are in New York, USA."
-
-
-# Standardized test data
-WEATHER_DATA = {"temperature": 75.0, "condition": "sunny"}
-LOCATION_DATA = {"city": "New York", "country": "USA"}
-
-# Standardized expected responses
-EXPECTED_WEATHER_PYDANTIC = WeatherBaseModel(**WEATHER_DATA)
-EXPECTED_WEATHER_DATACLASS = WeatherDataclass(**WEATHER_DATA)
-EXPECTED_WEATHER_DICT: WeatherTypedDict = {"temperature": 75.0, "condition": "sunny"}
-EXPECTED_LOCATION = LocationResponse(**LOCATION_DATA)
-
-
-class TestResponseFormatAsModel:
-    def test_pydantic_model(self) -> None:
-        """Test response_format as Pydantic model."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-            [
-                {
-                    "name": "WeatherBaseModel",
-                    "id": "2",
-                    "args": WEATHER_DATA,
-                }
-            ],
-        ]
-
-        model = FakeToolCallingModel(tool_calls=tool_calls)
-
-        agent = create_agent(model, [get_weather], response_format=WeatherBaseModel)
-        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-        assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
-        assert len(response["messages"]) == 5
-
-    def test_dataclass(self) -> None:
-        """Test response_format as dataclass."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-            [
-                {
-                    "name": "WeatherDataclass",
-                    "id": "2",
-                    "args": WEATHER_DATA,
-                }
-            ],
-        ]
-
-        model = FakeToolCallingModel(tool_calls=tool_calls)
-
-        agent = create_agent(model, [get_weather], response_format=WeatherDataclass)
-        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-        assert response["structured_response"] == EXPECTED_WEATHER_DATACLASS
-        assert len(response["messages"]) == 5
-
-    def test_typed_dict(self) -> None:
-        """Test response_format as TypedDict."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-            [
-                {
-                    "name": "WeatherTypedDict",
-                    "id": "2",
-                    "args": WEATHER_DATA,
-                }
-            ],
-        ]
-
-        model = FakeToolCallingModel(tool_calls=tool_calls)
-
-        agent = create_agent(model, [get_weather], response_format=WeatherTypedDict)
-        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-        assert response["structured_response"] == EXPECTED_WEATHER_DICT
-        assert len(response["messages"]) == 5
-
-    def test_json_schema(self) -> None:
-        """Test response_format as JSON schema."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-            [
-                {
-                    "name": "weather_schema",
-                    "id": "2",
-                    "args": WEATHER_DATA,
-                }
-            ],
-        ]
-
-        model = FakeToolCallingModel(tool_calls=tool_calls)
-
-        agent = create_agent(model, [get_weather], response_format=weather_json_schema)
-        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-        assert response["structured_response"] == EXPECTED_WEATHER_DICT
-        assert len(response["messages"]) == 5
-
-
-class TestResponseFormatAsToolOutput:
-    def test_pydantic_model(self) -> None:
-        """Test response_format as ToolOutput with Pydantic model."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-            [
-                {
-                    "name": "WeatherBaseModel",
-                    "id": "2",
-                    "args": WEATHER_DATA,
-                }
-            ],
-        ]
-
-        model = FakeToolCallingModel(tool_calls=tool_calls)
-
-        agent = create_agent(
-            model, [get_weather], response_format=ToolOutput(WeatherBaseModel)
-        )
-        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-        assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
-        assert len(response["messages"]) == 5
-
-    def test_dataclass(self) -> None:
-        """Test response_format as ToolOutput with dataclass."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-            [
-                {
-                    "name": "WeatherDataclass",
-                    "id": "2",
-                    "args": WEATHER_DATA,
-                }
-            ],
-        ]
-
-        model = FakeToolCallingModel(tool_calls=tool_calls)
-
-        agent = create_agent(
-            model, [get_weather], response_format=ToolOutput(WeatherDataclass)
-        )
-        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-        assert response["structured_response"] == EXPECTED_WEATHER_DATACLASS
-        assert len(response["messages"]) == 5
-
-    def test_typed_dict(self) -> None:
-        """Test response_format as ToolOutput with TypedDict."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-            [
-                {
-                    "name": "WeatherTypedDict",
-                    "id": "2",
-                    "args": WEATHER_DATA,
-                }
-            ],
-        ]
-
-        model = FakeToolCallingModel(tool_calls=tool_calls)
-
-        agent = create_agent(
-            model, [get_weather], response_format=ToolOutput(WeatherTypedDict)
-        )
-        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-        assert response["structured_response"] == EXPECTED_WEATHER_DICT
-        assert len(response["messages"]) == 5
-
-    def test_json_schema(self) -> None:
-        """Test response_format as ToolOutput with JSON schema."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-            [
-                {
-                    "name": "weather_schema",
-                    "id": "2",
-                    "args": WEATHER_DATA,
-                }
-            ],
-        ]
-
-        model = FakeToolCallingModel(tool_calls=tool_calls)
-
-        agent = create_agent(
-            model, [get_weather], response_format=ToolOutput(weather_json_schema)
-        )
-        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-        assert response["structured_response"] == EXPECTED_WEATHER_DICT
-        assert len(response["messages"]) == 5
-
-    def test_union_of_types(self) -> None:
-        """Test response_format as ToolOutput with Union of various types."""
-        # Test with WeatherBaseModel
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-            [
-                {
-                    "name": "WeatherBaseModel",
-                    "id": "2",
-                    "args": WEATHER_DATA,
-                }
-            ],
-        ]
-
-        model = FakeToolCallingModel(tool_calls=tool_calls)
-
-        agent = create_agent(
-            model,
-            [get_weather, get_location],
-            response_format=ToolOutput(WeatherBaseModel | LocationResponse),
-        )
-        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-        assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
-        assert len(response["messages"]) == 5
-
-        # Test with LocationResponse
-        tool_calls_location = [
-            [{"args": {}, "id": "1", "name": "get_location"}],
-            [
-                {
-                    "name": "LocationResponse",
-                    "id": "2",
-                    "args": LOCATION_DATA,
-                }
-            ],
-        ]
-
-        model_location = FakeToolCallingModel(tool_calls=tool_calls_location)
-
-        agent_location = create_agent(
-            model_location,
-            [get_weather, get_location],
-            response_format=ToolOutput(WeatherBaseModel | LocationResponse),
-        )
-        response_location = agent_location.invoke(
-            {"messages": [HumanMessage("Where am I?")]}
-        )
-
-        assert response_location["structured_response"] == EXPECTED_LOCATION
-        assert len(response_location["messages"]) == 5
-
-    def test_multiple_tool_messages(self) -> None:
-        """Test response_format as ToolOutput with Pydantic model."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-            [
-                {
-                    "name": "WeatherBaseModel",
-                    "id": "2",
-                    "args": WEATHER_DATA,
-                },
-                {
-                    "name": "WeatherDataclass",
-                    "id": "3",
-                    "args": WEATHER_DATA,
-                },
-            ],
-        ]
-
-        model = FakeToolCallingModel(tool_calls=tool_calls)
-
-        agent = create_agent(
-            model,
-            [get_weather],
-            response_format=ToolOutput(WeatherBaseModel | WeatherDataclass),
-        )
-
-        with pytest.raises(
-            AssertionError,
-            match="Model incorrectly returned multiple structured responses.",
-        ):
-            agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-
-class TestResponseFormatAsNativeOutput:
-    def test_pydantic_model(self) -> None:
-        """Test response_format as NativeOutput with Pydantic model."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-        ]
-
-        model = FakeToolCallingModel[WeatherBaseModel](
-            tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_PYDANTIC
-        )
-
-        agent = create_agent(
-            model, [get_weather], response_format=NativeOutput(WeatherBaseModel)
-        )
-        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-        assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
-        assert len(response["messages"]) == 4
-
-    def test_dataclass(self) -> None:
-        """Test response_format as NativeOutput with dataclass."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-        ]
-
-        model = FakeToolCallingModel[WeatherDataclass](
-            tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_DATACLASS
-        )
-
-        agent = create_agent(
-            model, [get_weather], response_format=NativeOutput(WeatherDataclass)
-        )
-        response = agent.invoke(
-            {"messages": [HumanMessage("What's the weather?")]},
-        )
-
-        assert response["structured_response"] == EXPECTED_WEATHER_DATACLASS
-        assert len(response["messages"]) == 4
-
-    def test_typed_dict(self) -> None:
-        """Test response_format as NativeOutput with TypedDict."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-        ]
-
-        model = FakeToolCallingModel[WeatherTypedDict](
-            tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_DICT
-        )
-
-        agent = create_agent(
-            model, [get_weather], response_format=NativeOutput(WeatherTypedDict)
-        )
-        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-        assert response["structured_response"] == EXPECTED_WEATHER_DICT
-        assert len(response["messages"]) == 4
-
-    def test_json_schema(self) -> None:
-        """Test response_format as NativeOutput with JSON schema."""
-        tool_calls = [
-            [{"args": {}, "id": "1", "name": "get_weather"}],
-        ]
-
-        model = FakeToolCallingModel[dict](
-            tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_DICT
-        )
-
-        agent = create_agent(
-            model, [get_weather], response_format=NativeOutput(weather_json_schema)
-        )
-        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-        assert response["structured_response"] == EXPECTED_WEATHER_DICT
-        assert len(response["messages"]) == 4
-
-
-def test_union_of_types() -> None:
-    """Test response_format as NativeOutput with Union (if supported)."""
-    tool_calls = [
-        [{"args": {}, "id": "1", "name": "get_weather"}],
-        [
-            {
-                "name": "WeatherBaseModel",
-                "id": "2",
-                "args": WEATHER_DATA,
-            }
-        ],
-    ]
-
-    model = FakeToolCallingModel[WeatherBaseModel | LocationResponse](
-        tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_PYDANTIC
-    )
-
-    agent = create_agent(
-        model,
-        [get_weather, get_location],
-        response_format=ToolOutput(WeatherBaseModel | LocationResponse),
-    )
-    response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-    assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
-    assert len(response["messages"]) == 5
-
-
-@pytest.mark.skipif(
-    skip_openai_integration_tests, reason="OpenAI integration tests are disabled."
-)
-def test_inference_to_native_output() -> None:
-    """Test that native output is inferred when a model supports it."""
-    model = ChatOpenAI(model="gpt-5")
-    agent = create_agent(
-        model,
-        prompt="You are a helpful weather assistant. Please call the get_weather tool, then use the WeatherReport tool to generate the final response.",
-        tools=[get_weather],
-        response_format=WeatherBaseModel,
-    )
-    response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-    assert isinstance(response["structured_response"], WeatherBaseModel)
-    assert response["structured_response"].temperature == 75.0
-    assert response["structured_response"].condition.lower() == "sunny"
-    assert len(response["messages"]) == 4
-
-    assert [m.type for m in response["messages"]] == [
-        "human",  # "What's the weather?"
-        "ai",  # "What's the weather?"
-        "tool",  # "The weather is sunny and 75°F."
-        "ai",  # structured response
-    ]
-
-
-@pytest.mark.skipif(
-    skip_openai_integration_tests, reason="OpenAI integration tests are disabled."
-)
-def test_inference_to_tool_output() -> None:
-    """Test that tool output is inferred when a model supports it."""
-    model = ChatOpenAI(model="gpt-4")
-    agent = create_agent(
-        model,
-        prompt="You are a helpful weather assistant. Please call the get_weather tool, then use the WeatherReport tool to generate the final response.",
-        tools=[get_weather],
-        response_format=ToolOutput(WeatherBaseModel),
-    )
-    response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
-
-    assert isinstance(response["structured_response"], WeatherBaseModel)
-    assert response["structured_response"].temperature == 75.0
-    assert response["structured_response"].condition.lower() == "sunny"
-    assert len(response["messages"]) == 5
-
-    assert [m.type for m in response["messages"]] == [
-        "human",  # "What's the weather?"
-        "ai",  # "What's the weather?"
-        "tool",  # "The weather is sunny and 75°F."
-        "ai",  # structured response
-        "tool",  # artificial tool message
-    ]

libs/prebuilt/tests/test_responses.py

@@ -1,152 +0,0 @@
-"""Unit tests for langgraph.prebuilt.responses module."""
-
-import pytest
-from pydantic import BaseModel
-
-from langgraph.prebuilt.responses import (
-    OutputToolBinding,
-    ToolOutput,
-    _SchemaSpec,
-)
-
-
-class _TestModel(BaseModel):
-    """A test model for structured output."""
-
-    name: str
-    age: int
-    email: str = "default@example.com"
-
-
-class CustomModel(BaseModel):
-    """Custom model with a custom docstring."""
-
-    value: float
-    description: str
-
-
-class EmptyDocModel(BaseModel):
-    # No custom docstring, should have no description in tool
-    data: str
-
-
-class TestUsingToolStrategy:
-    """Test UsingToolStrategy dataclass."""
-
-    def test_basic_creation(self):
-        """Test basic UsingToolStrategy creation."""
-        strategy = ToolOutput(schema=_TestModel)
-        assert strategy.schema == _TestModel
-        assert strategy.tool_message_content is None
-        assert len(strategy.schema_specs) == 1
-
-    def test_multiple_schemas(self):
-        """Test UsingToolStrategy with multiple schemas."""
-        strategy = ToolOutput(schema=_TestModel | CustomModel)
-        assert len(strategy.schema_specs) == 2
-        assert strategy.schema_specs[0].schema == _TestModel
-        assert strategy.schema_specs[1].schema == CustomModel
-
-    def test_schema_with_tool_message_content(self):
-        """Test UsingToolStrategy with tool message content."""
-        strategy = ToolOutput(schema=_TestModel, tool_message_content="custom message")
-        assert strategy.schema == _TestModel
-        assert strategy.tool_message_content == "custom message"
-        assert len(strategy.schema_specs) == 1
-
-
-class TestOutputToolBinding:
-    """Test OutputToolBinding dataclass and its methods."""
-
-    def test_from_schema_spec_basic(self):
-        """Test basic OutputToolBinding creation from SchemaSpec."""
-        schema_spec = _SchemaSpec(schema=_TestModel)
-        tool_binding = OutputToolBinding.from_schema_spec(schema_spec)
-
-        assert tool_binding.schema == _TestModel
-        assert tool_binding.schema_kind == "pydantic"
-        assert tool_binding.tool is not None
-        assert tool_binding.tool.name == "_TestModel"
-
-    def test_from_schema_spec_with_custom_name(self):
-        """Test OutputToolBinding creation with custom name."""
-        schema_spec = _SchemaSpec(schema=_TestModel, name="custom_tool_name")
-        tool_binding = OutputToolBinding.from_schema_spec(schema_spec)
-        assert tool_binding.tool.name == "custom_tool_name"
-
-    def test_from_schema_spec_with_custom_description(self):
-        """Test OutputToolBinding creation with custom description."""
-        schema_spec = _SchemaSpec(
-            schema=_TestModel, description="Custom tool description"
-        )
-        tool_binding = OutputToolBinding.from_schema_spec(schema_spec)
-
-        assert tool_binding.tool.description == "Custom tool description"
-
-    def test_from_schema_spec_with_model_docstring(self):
-        """Test OutputToolBinding creation using model docstring as description."""
-        schema_spec = _SchemaSpec(schema=CustomModel)
-        tool_binding = OutputToolBinding.from_schema_spec(schema_spec)
-
-        assert tool_binding.tool.description == "Custom model with a custom docstring."
-
-    @pytest.mark.skip(
-        reason="Need to fix bug in langchain-core for inheritance of doc-strings."
-    )
-    def test_from_schema_spec_empty_docstring(self):
-        """Test OutputToolBinding creation with model that has default docstring."""
-
-        # Create a model with the same docstring as BaseModel
-        class DefaultDocModel(BaseModel):
-            # This should have the same docstring as BaseModel
-            pass
-
-        schema_spec = _SchemaSpec(schema=DefaultDocModel)
-        tool_binding = OutputToolBinding.from_schema_spec(schema_spec)
-
-        # Should use empty description when model has default BaseModel docstring
-        assert tool_binding.tool.description == ""
-
-    def test_parse_payload_pydantic_success(self):
-        """Test successful parsing for Pydantic model."""
-        schema_spec = _SchemaSpec(schema=_TestModel)
-        tool_binding = OutputToolBinding.from_schema_spec(schema_spec)
-
-        tool_args = {"name": "John", "age": 30}
-        result = tool_binding.parse(tool_args)
-
-        assert isinstance(result, _TestModel)
-        assert result.name == "John"
-        assert result.age == 30
-        assert result.email == "default@example.com"  # default value
-
-    def test_parse_payload_pydantic_validation_error(self):
-        """Test parsing failure for invalid Pydantic data."""
-        schema_spec = _SchemaSpec(schema=_TestModel)
-        tool_binding = OutputToolBinding.from_schema_spec(schema_spec)
-
-        # Missing required field 'name'
-        tool_args = {"age": 30}
-
-        with pytest.raises(ValueError, match="Failed to parse data to _TestModel"):
-            tool_binding.parse(tool_args)
-
-
-class TestEdgeCases:
-    """Test edge cases and error conditions."""
-
-    def test_empty_schemas_list(self) -> None:
-        """Test UsingToolStrategy with empty schemas list."""
-        strategy = ToolOutput(EmptyDocModel)
-        assert len(strategy.schema_specs) == 1
-
-    @pytest.mark.skip(
-        reason="Need to fix bug in langchain-core for inheritance of doc-strings."
-    )
-    def test_base_model_doc_constant(self) -> None:
-        """Test that BASE_MODEL_DOC constant is set correctly."""
-        binding = OutputToolBinding.from_schema_spec(_SchemaSpec(EmptyDocModel))
-        assert binding.tool.name == "EmptyDocModel"
-        assert (
-            binding.tool.description[:5] == ""
-        )  # Should be empty for default docstring

libs/prebuilt/tests/test_responses_int.py

@@ -1,153 +0,0 @@
-from __future__ import annotations
-
-import json
-from collections.abc import Sequence
-from pathlib import Path
-from typing import Any, Optional, Union
-from unittest.mock import MagicMock
-
-import pytest
-from langchain_core.messages import HumanMessage
-from langchain_core.tools import tool
-from pydantic import BaseModel, create_model
-
-from langgraph.prebuilt import create_agent
-from langgraph.prebuilt.responses import ToolOutput
-
-try:
-    from langchain_openai import ChatOpenAI
-except ImportError:
-    skip_openai_integration_tests = True
-else:
-    skip_openai_integration_tests = False
-
-
-def _load_spec() -> list[dict[str, Any]]:
-    with (Path(__file__).parent / "specifications" / "responses.json").open(
-        "r", encoding="utf-8"
-    ) as f:
-        return json.load(f)
-
-
-TEST_CASES = _load_spec()
-
-AGENT_PROMPT = "You are an HR assistant."
-
-EMPLOYEES = [
-    {"name": "Sabine", "role": "Developer", "department": "IT"},
-    {"name": "Henrik", "role": "Product Manager", "department": "IT"},
-    {"name": "Jessica", "role": "HR", "department": "People"},
-]
-
-
-def _make_tool(fn, *, name: str, description: str):
-    mock = MagicMock(side_effect=lambda *, name: fn(name=name))
-    InputModel = create_model(f"{name}_input", name=(str, ...))
-
-    @tool(name, description=description, args_schema=InputModel)
-    def _wrapped(name: str):
-        return mock(name=name)
-
-    return {"tool": _wrapped, "mock": mock}
-
-
-def _build_tool_output_response_format(
-    response_format_spec: Sequence[dict[str, Any]],
-) -> ToolOutput:
-    models: list[type[BaseModel]] = []
-    keyset_to_tool_name: dict[frozenset[str], str] = {}
-    type_map = {
-        "string": str,
-        "number": float,
-        "integer": int,
-        "boolean": bool,
-        "object": dict,
-        "array": list,
-    }
-
-    for idx, schema in enumerate(response_format_spec):
-        properties = schema["properties"]
-        required = set(schema["required"])
-        type_name = schema.get("title") or f"structured_output_format_{idx + 1}"
-        fields = {}
-        for k, prop in properties.items():
-            py_type = type_map.get(prop.get("type"), Any)
-            fields[k] = (py_type, ...) if k in required else (Optional[py_type], None)  # noqa: UP045
-        model = create_model(type_name, **fields)
-        models.append(model)
-        keyset_to_tool_name[frozenset(required)] = type_name
-
-    union_type = Union[tuple(models)]  # noqa: UP045, UP007
-    return ToolOutput(union_type)
-
-
-@pytest.mark.skipif(
-    skip_openai_integration_tests, reason="OpenAI integration tests are disabled."
-)
-@pytest.mark.xfail(
-    reason="currently failing due to undefined behavior for multiple structured responses."
-)
-@pytest.mark.parametrize("case", TEST_CASES, ids=[c["name"] for c in TEST_CASES])
-def test_responses_integration_matrix(case: dict[str, Any]) -> None:
-    def get_employee_role(*, name: str) -> str | None:
-        for e in EMPLOYEES:
-            if e["name"] == name:
-                return e["role"]
-        return None
-
-    def get_employee_department(*, name: str) -> str | None:
-        for e in EMPLOYEES:
-            if e["name"] == name:
-                return e["department"]
-        return None
-
-    role_tool = _make_tool(
-        get_employee_role,
-        name="getEmployeeRole",
-        description="Get the employee role by name",
-    )
-    dept_tool = _make_tool(
-        get_employee_department,
-        name="getEmployeeDepartment",
-        description="Get the employee department by name",
-    )
-
-    response_spec = case["responseFormat"]
-    if isinstance(response_spec, dict):
-        response_spec = [response_spec]
-    tool_output = _build_tool_output_response_format(response_spec)
-
-    for assertion in case["assertionsByInvocation"]:
-        prompt: str = assertion["prompt"]
-        expected_calls: dict[str, int] = assertion["toolsWithExpectedCalls"]
-        expected_structured = assertion.get("expectedStructuredResponse")
-        expected_last_message = assertion.get("expectedLastMessage")
-
-        model = ChatOpenAI(
-            model="gpt-4o-mini",
-            temperature=0,
-        )
-
-        agent = create_agent(
-            model,
-            tools=[role_tool["tool"], dept_tool["tool"]],
-            prompt=AGENT_PROMPT,
-            response_format=tool_output,
-        )
-        result = agent.invoke({"messages": [HumanMessage(prompt)]})
-
-        # TODO: Count LLM calls. JS handles with mock fetch. Could pass in mock http_client?
-
-        # Count tool calls
-        assert role_tool["mock"].call_count == expected_calls["getEmployeeRole"]
-        assert dept_tool["mock"].call_count == expected_calls["getEmployeeDepartment"]
-
-        # Check last message content
-        last_message = result["messages"][-1]
-        assert last_message.content == expected_last_message
-
-        # Check structured response
-        structured_response_json = result["structured_response"].model_dump()
-        assert structured_response_json == expected_structured
-
-        print("Passed test for: ", case["name"])

libs/prebuilt/tests/test_tool_node.py

@@ -1,46 +1,25 @@
-import dataclasses
-import json
-from functools import partial
 from typing import (
     Annotated,
     Any,
-    TypeVar,
+    Union,
 )
 
 import pytest
 from langchain_core.messages import (
     AIMessage,
-    AnyMessage,
-    HumanMessage,
     RemoveMessage,
-    ToolCall,
     ToolMessage,
 )
 from langchain_core.tools import BaseTool, ToolException
 from langchain_core.tools import tool as dec_tool
 from pydantic import BaseModel, ValidationError
-from pydantic.v1 import BaseModel as BaseModelV1
 from pydantic.v1 import ValidationError as ValidationErrorV1
-from typing_extensions import TypedDict
 
-from langgraph.config import get_stream_writer
 from langgraph.errors import GraphBubbleUp, GraphInterrupt
-from langgraph.graph import START, MessagesState, StateGraph
-from langgraph.graph.message import REMOVE_ALL_MESSAGES, add_messages
-from langgraph.prebuilt import (
-    ToolNode,
-)
-from langgraph.prebuilt.tool_node import (
-    TOOL_CALL_ERROR_TEMPLATE,
-    InjectedState,
-    InjectedStore,
-    tools_condition,
-)
-from langgraph.store.base import BaseStore
-from langgraph.store.memory import InMemoryStore
+from langgraph.graph.message import REMOVE_ALL_MESSAGES
+from langgraph.prebuilt import ToolNode
+from langgraph.prebuilt.tool_node import TOOL_CALL_ERROR_TEMPLATE
 from langgraph.types import Command, Send
-from tests.messages import _AnyIdHumanMessage, _AnyIdToolMessage
-from tests.model import FakeToolCallingModel
 
 pytestmark = pytest.mark.anyio
 
@@ -83,8 +62,7 @@ def tool5(some_val: int):
 tool5.handle_tool_error = "foo"
 
 
-async def test_tool_node() -> None:
-    """Test tool node."""
+async def test_tool_node():
     result = ToolNode([tool1]).invoke(
         {
             "messages": [
@@ -176,7 +154,7 @@ async def test_tool_node() -> None:
     assert tool_message.tool_call_id == "some 3"
 
 
-async def test_tool_node_tool_call_input() -> None:
+async def test_tool_node_tool_call_input():
     # Single tool call
     tool_call_1 = {
         "name": "tool1",
@@ -217,8 +195,8 @@ async def test_tool_node_tool_call_input() -> None:
     ]
 
 
-async def test_tool_node_error_handling() -> None:
-    def handle_all(e: ValueError | ToolException | ValidationError):
+async def test_tool_node_error_handling():
+    def handle_all(e: Union[ValueError, ToolException, ValidationError]):
         return TOOL_CALL_ERROR_TEMPLATE.format(error=repr(e))
 
     # test catching all exceptions, via:
@@ -279,7 +257,7 @@ async def test_tool_node_error_handling() -> None:
         assert result_error["messages"][2].tool_call_id == "another id"
 
 
-async def test_tool_node_error_handling_callable() -> None:
+async def test_tool_node_error_handling_callable():
     def handle_value_error(e: ValueError):
         return "Value error"
 
@@ -1178,304 +1156,3 @@ async def test_tool_node_command_remove_all_messages():
     command = result[0]
     assert isinstance(command, Command)
     assert command.update == {"messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES)]}
-
-
-class _InjectStateSchema(TypedDict):
-    messages: list
-    foo: str
-
-
-class _InjectedStatePydanticSchema(BaseModelV1):
-    messages: list
-    foo: str
-
-
-class _InjectedStatePydanticV2Schema(BaseModel):
-    messages: list
-    foo: str
-
-
-@dataclasses.dataclass
-class _InjectedStateDataclassSchema:
-    messages: list
-    foo: str
-
-
-T = TypeVar("T")
-
-
-@pytest.mark.parametrize(
-    "schema_",
-    [
-        _InjectStateSchema,
-        _InjectedStatePydanticSchema,
-        _InjectedStatePydanticV2Schema,
-        _InjectedStateDataclassSchema,
-    ],
-)
-def test_tool_node_inject_state(schema_: type[T]) -> None:
-    def tool1(some_val: int, state: Annotated[T, InjectedState]) -> str:
-        """Tool 1 docstring."""
-        if isinstance(state, dict):
-            return state["foo"]
-        else:
-            return getattr(state, "foo")
-
-    def tool2(some_val: int, state: Annotated[T, InjectedState()]) -> str:
-        """Tool 2 docstring."""
-        if isinstance(state, dict):
-            return state["foo"]
-        else:
-            return getattr(state, "foo")
-
-    def tool3(
-        some_val: int,
-        foo: Annotated[str, InjectedState("foo")],
-        msgs: Annotated[list[AnyMessage], InjectedState("messages")],
-    ) -> str:
-        """Tool 1 docstring."""
-        return foo
-
-    def tool4(
-        some_val: int, msgs: Annotated[list[AnyMessage], InjectedState("messages")]
-    ) -> str:
-        """Tool 1 docstring."""
-        return msgs[0].content
-
-    node = ToolNode([tool1, tool2, tool3, tool4])
-    for tool_name in ("tool1", "tool2", "tool3"):
-        tool_call = {
-            "name": tool_name,
-            "args": {"some_val": 1},
-            "id": "some 0",
-            "type": "tool_call",
-        }
-        msg = AIMessage("hi?", tool_calls=[tool_call])
-        result = node.invoke(schema_(**{"messages": [msg], "foo": "bar"}))
-        tool_message = result["messages"][-1]
-        assert tool_message.content == "bar", f"Failed for tool={tool_name}"
-
-        if tool_name == "tool3":
-            failure_input = None
-            try:
-                failure_input = schema_(**{"messages": [msg], "notfoo": "bar"})
-            except Exception:
-                pass
-            if failure_input is not None:
-                with pytest.raises(KeyError):
-                    node.invoke(failure_input)
-
-                with pytest.raises(ValueError):
-                    node.invoke([msg])
-        else:
-            failure_input = None
-            try:
-                failure_input = schema_(**{"messages": [msg], "notfoo": "bar"})
-            except Exception:
-                # We'd get a validation error from pydantic state and wouldn't make it to the node
-                # anyway
-                pass
-            if failure_input is not None:
-                messages_ = node.invoke(failure_input)
-                tool_message = messages_["messages"][-1]
-                assert "KeyError" in tool_message.content
-                tool_message = node.invoke([msg])[-1]
-                assert "KeyError" in tool_message.content
-
-    tool_call = {
-        "name": "tool4",
-        "args": {"some_val": 1},
-        "id": "some 0",
-        "type": "tool_call",
-    }
-    msg = AIMessage("hi?", tool_calls=[tool_call])
-    result = node.invoke(schema_(**{"messages": [msg], "foo": ""}))
-    tool_message = result["messages"][-1]
-    assert tool_message.content == "hi?"
-
-    result = node.invoke([msg])
-    tool_message = result[-1]
-    assert tool_message.content == "hi?"
-
-
-def test_tool_node_inject_store() -> None:
-    store = InMemoryStore()
-    namespace = ("test",)
-
-    def tool1(some_val: int, store: Annotated[BaseStore, InjectedStore()]) -> str:
-        """Tool 1 docstring."""
-        store_val = store.get(namespace, "test_key").value["foo"]
-        return f"Some val: {some_val}, store val: {store_val}"
-
-    def tool2(some_val: int, store: Annotated[BaseStore, InjectedStore()]) -> str:
-        """Tool 2 docstring."""
-        store_val = store.get(namespace, "test_key").value["foo"]
-        return f"Some val: {some_val}, store val: {store_val}"
-
-    def tool3(
-        some_val: int,
-        bar: Annotated[str, InjectedState("bar")],
-        store: Annotated[BaseStore, InjectedStore()],
-    ) -> str:
-        """Tool 3 docstring."""
-        store_val = store.get(namespace, "test_key").value["foo"]
-        return f"Some val: {some_val}, store val: {store_val}, state val: {bar}"
-
-    node = ToolNode([tool1, tool2, tool3], handle_tool_errors=True)
-    store.put(namespace, "test_key", {"foo": "bar"})
-
-    class State(MessagesState):
-        bar: str
-
-    builder = StateGraph(State)
-    builder.add_node("tools", node)
-    builder.add_edge(START, "tools")
-    graph = builder.compile(store=store)
-
-    for tool_name in ("tool1", "tool2"):
-        tool_call = {
-            "name": tool_name,
-            "args": {"some_val": 1},
-            "id": "some 0",
-            "type": "tool_call",
-        }
-        msg = AIMessage("hi?", tool_calls=[tool_call])
-        node_result = node.invoke({"messages": [msg]}, store=store)
-        graph_result = graph.invoke({"messages": [msg]})
-        for result in (node_result, graph_result):
-            result["messages"][-1]
-            tool_message = result["messages"][-1]
-            assert tool_message.content == "Some val: 1, store val: bar", (
-                f"Failed for tool={tool_name}"
-            )
-
-    tool_call = {
-        "name": "tool3",
-        "args": {"some_val": 1},
-        "id": "some 0",
-        "type": "tool_call",
-    }
-    msg = AIMessage("hi?", tool_calls=[tool_call])
-    node_result = node.invoke({"messages": [msg], "bar": "baz"}, store=store)
-    graph_result = graph.invoke({"messages": [msg], "bar": "baz"})
-    for result in (node_result, graph_result):
-        result["messages"][-1]
-        tool_message = result["messages"][-1]
-        assert tool_message.content == "Some val: 1, store val: bar, state val: baz", (
-            f"Failed for tool={tool_name}"
-        )
-
-    # test injected store without passing store to compiled graph
-    failing_graph = builder.compile()
-    with pytest.raises(ValueError):
-        failing_graph.invoke({"messages": [msg], "bar": "baz"})
-
-
-def test_tool_node_ensure_utf8() -> None:
-    @dec_tool
-    def get_day_list(days: list[str]) -> list[str]:
-        """choose days"""
-        return days
-
-    data = ["星期一", "水曜日", "목요일", "Friday"]
-    tools = [get_day_list]
-    tool_calls = [ToolCall(name=get_day_list.name, args={"days": data}, id="test_id")]
-    outputs: list[ToolMessage] = ToolNode(tools).invoke(
-        [AIMessage(content="", tool_calls=tool_calls)]
-    )
-    assert outputs[0].content == json.dumps(data, ensure_ascii=False)
-
-
-def test_tool_node_messages_key() -> None:
-    @dec_tool
-    def add(a: int, b: int):
-        """Adds a and b."""
-        return a + b
-
-    model = FakeToolCallingModel(
-        tool_calls=[[ToolCall(name=add.name, args={"a": 1, "b": 2}, id="test_id")]]
-    )
-
-    class State(TypedDict):
-        subgraph_messages: Annotated[list[AnyMessage], add_messages]
-
-    def call_model(state: State):
-        response = model.invoke(state["subgraph_messages"])
-        model.tool_calls = []
-        return {"subgraph_messages": response}
-
-    builder = StateGraph(State)
-    builder.add_node("agent", call_model)
-    builder.add_node("tools", ToolNode([add], messages_key="subgraph_messages"))
-    builder.add_conditional_edges(
-        "agent", partial(tools_condition, messages_key="subgraph_messages")
-    )
-    builder.add_edge(START, "agent")
-    builder.add_edge("tools", "agent")
-
-    graph = builder.compile()
-    result = graph.invoke({"subgraph_messages": [HumanMessage(content="hi")]})
-    assert result["subgraph_messages"] == [
-        _AnyIdHumanMessage(content="hi"),
-        AIMessage(
-            content="hi",
-            id="0",
-            tool_calls=[ToolCall(name=add.name, args={"a": 1, "b": 2}, id="test_id")],
-        ),
-        _AnyIdToolMessage(content="3", name=add.name, tool_call_id="test_id"),
-        AIMessage(content="hi-hi-3", id="1"),
-    ]
-
-
-def test_tool_node_stream_writer() -> None:
-    @dec_tool
-    def streaming_tool(x: int) -> str:
-        """Do something with writer."""
-        my_writer = get_stream_writer()
-        for value in ["foo", "bar", "baz"]:
-            my_writer({"custom_tool_value": value})
-
-        return x
-
-    tool_node = ToolNode([streaming_tool])
-    graph = (
-        StateGraph(MessagesState)
-        .add_node("tools", tool_node)
-        .add_edge(START, "tools")
-        .compile()
-    )
-
-    tool_call = {
-        "name": "streaming_tool",
-        "args": {"x": 1},
-        "id": "1",
-        "type": "tool_call",
-    }
-    inputs = {
-        "messages": [AIMessage("", tool_calls=[tool_call])],
-    }
-
-    assert list(graph.stream(inputs, stream_mode="custom")) == [
-        {"custom_tool_value": "foo"},
-        {"custom_tool_value": "bar"},
-        {"custom_tool_value": "baz"},
-    ]
-    assert list(graph.stream(inputs, stream_mode=["custom", "updates"])) == [
-        ("custom", {"custom_tool_value": "foo"}),
-        ("custom", {"custom_tool_value": "bar"}),
-        ("custom", {"custom_tool_value": "baz"}),
-        (
-            "updates",
-            {
-                "tools": {
-                    "messages": [
-                        _AnyIdToolMessage(
-                            content="1",
-                            name="streaming_tool",
-                            tool_call_id="1",
-                        ),
-                    ],
-                },
-            },
-        ),
-    ]

libs/prebuilt/uv.lock

@@ -1,6 +1,6 @@
 version = 1
 revision = 2
-requires-python = ">=3.10"
+requires-python = ">=3.9"
 
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@@ -291,7 +316,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.6.4"
+version = "0.6.6"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -484,7 +509,6 @@ dev = [
 
 [[package]]
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-version = "0.2.0"
 source = { editable = "../sdk-py" }
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libs/sdk-py/Makefile

@@ -1,7 +1,7 @@
-.PHONY: lint format
+.PHONY: lint format test
 
 test:
-	echo "No tests to run"
+	uv run pytest tests
 
 ######################
 # LINTING AND FORMATTING
@@ -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 $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) || uv run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
+	uvx ty check .
 
 format format_diff:
 	uv run ruff check --select I --fix $(PYTHON_FILES)

libs/sdk-py/langgraph_sdk/__init__.py

@@ -1,11 +1,6 @@
 from langgraph_sdk.auth import Auth
 from langgraph_sdk.client import get_client, get_sync_client
 
-try:
-    from importlib import metadata
-
-    __version__ = metadata.version(__package__)
-except metadata.PackageNotFoundError:
-    __version__ = "unknown"
+__version__ = "0.2.5"
 
 __all__ = ["Auth", "get_client", "get_sync_client"]

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

@@ -385,6 +385,8 @@ class _ResourceOn(typing.Generic[VCreate, VRead, VUpdate, VDelete, VSearch]):
                 _register_handler(self.auth, self.resource, "*", handler),
             )
 
+        # Accept keyword-only parameters for future filtering behavior; referenced to satisfy linters.
+        _ = resources, actions
         return decorator
 
 
@@ -701,7 +703,7 @@ def _validate_handler(fn: Callable[..., typing.Any]) -> None:
     """
     if not inspect.iscoroutinefunction(fn):
         raise ValueError(
-            f"Auth handler '{fn.__name__}' must be an async function. "
+            f"Auth handler '{getattr(fn, '__name__', fn)}' must be an async function. "
             "Add 'async' before 'def' to make it asynchronous and ensure"
             " any IO operations are non-blocking."
         )
@@ -709,18 +711,20 @@ def _validate_handler(fn: Callable[..., typing.Any]) -> None:
     sig = inspect.signature(fn)
     if "ctx" not in sig.parameters:
         raise ValueError(
-            f"Auth handler '{fn.__name__}' must have a 'ctx: AuthContext' parameter. "
+            f"Auth handler '{getattr(fn, '__name__', fn)}' must have a 'ctx: AuthContext' parameter. "
             "Update the function signature to include this required parameter."
         )
     if "value" not in sig.parameters:
         raise ValueError(
-            f"Auth handler '{fn.__name__}' must have a 'value' parameter. "
+            f"Auth handler '{getattr(fn, '__name__', fn)}' must have a 'value' parameter. "
             " The value contains the mutable data being sent to the endpoint."
             "Update the function signature to include this required parameter."
         )
 
 
-def is_studio_user(user: types.MinimalUser | types.User | types.UserDict) -> bool:
+def is_studio_user(
+    user: types.MinimalUser | types.BaseUser | types.MinimalUserDict,
+) -> bool:
     return (
         isinstance(user, types.StudioUser)
         or isinstance(user, dict)

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

@@ -489,6 +489,9 @@ class ThreadsSearch(typing.TypedDict, total=False):
     offset: int
     """Offset for pagination."""
 
+    ids: Sequence[UUID] | None
+    """typing.Optional list of thread IDs to filter by."""
+
     thread_id: UUID | None
     """typing.Optional thread ID to filter by."""
 

libs/sdk-py/langgraph_sdk/schema.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from collections.abc import Sequence
+from collections.abc import Mapping, Sequence
 from datetime import datetime
 from typing import (
     Any,
@@ -10,6 +10,7 @@ from typing import (
     NamedTuple,
     Optional,
     TypedDict,
+    Union,
 )
 
 from typing_extensions import TypeAlias
@@ -37,6 +38,14 @@ Represents the status of a thread:
 - "error": An exception occurred during task processing.
 """
 
+ThreadStreamMode = Literal["run_modes", "lifecycle", "state_update"]
+"""
+Defines the mode of streaming:
+- "run_modes": Stream the same events as the runs on thread, as well as run_done events.
+- "lifecycle": Stream only run start/end events.
+- "state_update": Stream state updates on the thread.
+"""
+
 StreamMode = Literal[
     "values",
     "messages",
@@ -90,6 +99,12 @@ Defines action after completion:
 - "keep": Retain resources after completion.
 """
 
+Durability = Literal["sync", "async", "exit"]
+"""Durability mode for the graph execution.
+- `"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."""
+
 All = Literal["*"]
 """Represents a wildcard or 'all' selector."""
 
@@ -348,6 +363,72 @@ class Cron(TypedDict):
     """The metadata of the cron."""
 
 
+# Select field aliases for client-side typing of `select` parameters.
+# These mirror the server's allowed field sets.
+
+AssistantSelectField = Literal[
+    "assistant_id",
+    "graph_id",
+    "name",
+    "description",
+    "config",
+    "context",
+    "created_at",
+    "updated_at",
+    "metadata",
+    "version",
+]
+
+ThreadSelectField = Literal[
+    "thread_id",
+    "created_at",
+    "updated_at",
+    "metadata",
+    "config",
+    "context",
+    "status",
+    "values",
+    "interrupts",
+]
+
+RunSelectField = Literal[
+    "run_id",
+    "thread_id",
+    "assistant_id",
+    "created_at",
+    "updated_at",
+    "status",
+    "metadata",
+    "kwargs",
+    "multitask_strategy",
+]
+
+CronSelectField = Literal[
+    "cron_id",
+    "assistant_id",
+    "thread_id",
+    "end_time",
+    "schedule",
+    "created_at",
+    "updated_at",
+    "user_id",
+    "payload",
+    "next_run_date",
+    "metadata",
+    "now",
+]
+
+PrimitiveData = Optional[Union[str, int, float, bool]]
+
+QueryParamTypes = Union[
+    Mapping[str, Union[PrimitiveData, Sequence[PrimitiveData]]],
+    list[tuple[str, PrimitiveData]],
+    tuple[tuple[str, PrimitiveData], ...],
+    str,
+    bytes,
+]
+
+
 class RunCreate(TypedDict):
     """Defines the parameters for initiating a background run."""
 

libs/sdk-py/langgraph_sdk/sse.py

@@ -95,7 +95,7 @@ class SSEDecoder:
 
             sse = StreamPart(
                 event=self._event,
-                data=orjson.loads(self._data) if self._data else None,
+                data=orjson.loads(self._data) if self._data else None,  # type: ignore[invalid-argument-type]
             )
 
             # NOTE: as per the SSE spec, do not reset last_event_id.

libs/sdk-py/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-sdk"
-version = "0.2.0"
+dynamic = ["version"]
 description = "SDK for interacting with LangGraph API"
 authors = []
 requires-python = ">=3.9"
@@ -16,6 +16,9 @@ dependencies = [
     "orjson>=3.10.1",
 ]
 
+[tool.hatch.version]
+path = "langgraph_sdk/__init__.py"
+
 [project.urls]
 Repository = "https://www.github.com/langchain-ai/langgraph"
 
@@ -47,5 +50,6 @@ lint.select = [
   "UP", # pyupgrade
   "B",  # flake8-bugbear
   "I",  # isort
+  "ARG", # flake8-unused-arguments
 ]
 lint.ignore = ["E501", "B008"]

libs/sdk-py/tests/test_api_parity.py

@@ -0,0 +1,120 @@
+from __future__ import annotations
+
+import inspect
+import re
+
+import pytest
+
+from langgraph_sdk.client import (
+    AssistantsClient,
+    CronClient,
+    RunsClient,
+    StoreClient,
+    SyncAssistantsClient,
+    SyncCronClient,
+    SyncRunsClient,
+    SyncStoreClient,
+    SyncThreadsClient,
+    ThreadsClient,
+)
+
+
+def _public_methods(cls) -> dict[str, object]:
+    methods: dict[str, object] = {}
+    # Use the raw class dict to avoid runtime wrappers from plugins/decorators
+    for name, member in cls.__dict__.items():
+        if name.startswith("_"):
+            continue
+        if inspect.isfunction(member):
+            methods[name] = member
+    return methods
+
+
+def _strip_self(sig: inspect.Signature) -> inspect.Signature:
+    params = list(sig.parameters.values())
+    if params and params[0].name == "self":
+        params = params[1:]
+    return sig.replace(parameters=params)
+
+
+def _normalize_return_annotation(ann: object) -> str:
+    s = str(ann)
+    s = re.sub(r"\s+", "", s)
+    s = s.replace("typing.", "").replace("collections.abc.", "")
+    s = re.sub(r"AsyncGenerator\[([^,\]]+)(?:,[^\]]*)?\]", r"Iterator[\1]", s)
+    s = re.sub(r"Generator\[([^,\]]+)(?:,[^\]]*)?\]", r"Iterator[\1]", s)
+    s = re.sub(r"AsyncIterator\[(.+)\]", r"Iterator[\1]", s)
+    s = re.sub(r"AsyncIterable\[(.+)\]", r"Iterable[\1]", s)
+    return s
+
+
+@pytest.mark.parametrize(
+    "async_cls,sync_cls",
+    [
+        (AssistantsClient, SyncAssistantsClient),
+        (ThreadsClient, SyncThreadsClient),
+        (RunsClient, SyncRunsClient),
+        (CronClient, SyncCronClient),
+        (StoreClient, SyncStoreClient),
+    ],
+)
+def test_sync_api_matches_async(async_cls, sync_cls):
+    async_methods = _public_methods(async_cls)
+    sync_methods = _public_methods(sync_cls)
+
+    # Method name parity
+    assert set(sync_methods.keys()) == set(async_methods.keys()), (
+        f"Method sets differ: async-only={set(async_methods) - set(sync_methods)}, sync-only={set(sync_methods) - set(async_methods)}"
+    )
+
+    for name, async_fn in async_methods.items():
+        sync_fn = sync_methods[name]
+
+        # Use inspect.signature for parameter names (robust across versions)
+        async_sig = _strip_self(inspect.signature(async_fn))
+        sync_sig = _strip_self(inspect.signature(sync_fn))
+
+        a_names = list(async_sig.parameters.keys())
+        s_names = list(sync_sig.parameters.keys())
+
+        assert set(a_names) == set(s_names), (
+            f"Parameter names differ for {async_cls.__name__}.{name}: "
+            f"async={a_names}, sync={s_names}"
+        )
+
+        # Compare default presence and parameter kinds (with some tolerance)
+        a_params = async_sig.parameters
+        s_params = sync_sig.parameters
+
+        def kinds_compatible(
+            akind: inspect._ParameterKind, skind: inspect._ParameterKind
+        ) -> bool:
+            if akind == skind:
+                return True
+            return {
+                inspect.Parameter.KEYWORD_ONLY,
+                inspect.Parameter.POSITIONAL_OR_KEYWORD,
+            } == {akind, skind}
+
+        for pname in set(a_names) & set(s_names):
+            apar = a_params[pname]
+            spar = s_params[pname]
+            assert kinds_compatible(apar.kind, spar.kind), (
+                f"Parameter kind mismatch for {async_cls.__name__}.{name}.{pname}: "
+                f"async={apar.kind}, sync={spar.kind}"
+            )
+            assert (apar.default is inspect._empty) == (
+                spar.default is inspect._empty
+            ), (
+                f"Default presence mismatch for {async_cls.__name__}.{name}.{pname}: "
+                f"async_has_default={apar.default is not inspect._empty}, "
+                f"sync_has_default={spar.default is not inspect._empty}"
+            )
+
+        # Return annotations must match or be iterator-equivalent
+        a_ret = _normalize_return_annotation(async_sig.return_annotation)
+        s_ret = _normalize_return_annotation(sync_sig.return_annotation)
+        assert a_ret == s_ret, (
+            f"Return annotation mismatch for {async_cls.__name__}.{name}: "
+            f"async={a_ret}, sync={s_ret}"
+        )

libs/sdk-py/tests/test_select_fields_sync.py

@@ -0,0 +1,76 @@
+import functools
+import json
+import os
+from pathlib import Path
+from typing import get_args
+
+from langgraph_sdk.schema import (
+    AssistantSelectField,
+    CronSelectField,
+    RunSelectField,
+    ThreadSelectField,
+)
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+
+@functools.cache
+def _load_spec() -> dict:
+    with (
+        Path(current_dir).parents[2]
+        / "docs"
+        / "docs"
+        / "cloud"
+        / "reference"
+        / "api"
+        / "openapi.json"
+    ).open() as f:
+        return json.load(f)
+
+
+def _enum_from_request_select(spec: dict, path: str, method: str) -> set[str]:
+    schema = spec["paths"][path][method]["requestBody"]["content"]["application/json"][
+        "schema"
+    ]
+    if "properties" in schema:
+        props = schema["properties"]
+    elif "$ref" in schema:
+        component = spec
+        index = schema["$ref"].split("/")[1:]
+        for part in index:
+            component = component[part]
+        props = component["properties"]
+    else:
+        raise ValueError(f"Unknown schema: {schema}")
+    sel = props["select"]
+    return set(sel["items"]["enum"])
+
+
+def _enum_from_query_select(spec: dict, path: str, method: str) -> set[str]:
+    params = spec["paths"][path][method]["parameters"]
+    sel = next(p for p in params if p["name"] == "select")
+    return set(sel["schema"]["items"]["enum"])
+
+
+def test_assistants_select_enum_matches_sdk():
+    spec = _load_spec()
+    expected = set(get_args(AssistantSelectField))
+    assert _enum_from_request_select(spec, "/assistants/search", "post") == expected
+
+
+def test_threads_select_enum_matches_sdk():
+    spec = _load_spec()
+    expected = set(get_args(ThreadSelectField))
+    assert _enum_from_request_select(spec, "/threads/search", "post") == expected
+
+
+def test_runs_select_enum_matches_sdk():
+    spec = _load_spec()
+    expected = set(get_args(RunSelectField))
+    assert _enum_from_query_select(spec, "/threads/{thread_id}/runs", "get") == expected
+
+
+def test_crons_select_enum_matches_sdk():
+    spec = _load_spec()
+    expected = set(get_args(CronSelectField))
+    assert _enum_from_request_select(spec, "/runs/crons/search", "post") == expected

libs/sdk-py/uv.lock

@@ -4,7 +4,7 @@ requires-python = ">=3.9"
 
 [[package]]
 name = "anyio"
-version = "4.9.0"
+version = "4.10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
@@ -12,18 +12,27 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949, upload-time = "2025-03-17T00:02:54.77Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252, upload-time = "2025-08-04T08:54:26.451Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/6f/12/e5e0282d673bb9746bacfb6e2dba8719989d3660cdb2ea79aee9a9651afb/anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1", size = 107213, upload-time = "2025-08-04T08:54:24.882Z" },
+]
+
+[[package]]
+name = "backports-asyncio-runner"
+version = "1.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8e/ff/70dca7d7cb1cbc0edb2c6cc0c38b65cba36cccc491eca64cabd5fe7f8670/backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162", size = 69893, upload-time = "2025-07-02T02:27:15.685Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a0/59/76ab57e3fe74484f48a53f8e337171b4a2349e506eabe136d7e01d059086/backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5", size = 12313, upload-time = "2025-07-02T02:27:14.263Z" },
 ]
 
 [[package]]
 name = "certifi"
-version = "2025.7.14"
+version = "2025.8.3"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b3/76/52c535bcebe74590f296d6c77c86dabf761c41980e1347a2422e4aa2ae41/certifi-2025.7.14.tar.gz", hash = "sha256:8ea99dbdfaaf2ba2f9bac77b9249ef62ec5218e7c2b2e903378ed5fccf765995", size = 163981, upload-time = "2025-07-14T03:29:28.449Z" }
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+    { url = "https://files.pythonhosted.org/packages/e5/48/1549795ba7742c948d2ad169c1c8cdbae65bc450d6cd753d124b17c8cd32/certifi-2025.8.3-py3-none-any.whl", hash = "sha256:f6c12493cfb1b06ba2ff328595af9350c65d6644968e5d3a2ffd78699af217a5", size = 161216, upload-time = "2025-08-03T03:07:45.777Z" },
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 [[package]]
@@ -119,7 +128,6 @@ wheels = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.2.0"
 source = { editable = "." }
 dependencies = [
     { name = "httpx" },
@@ -156,7 +164,7 @@ dev = [
 
 [[package]]
 name = "mypy"
-version = "1.17.0"
+version = "1.17.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mypy-extensions" },
@@ -164,39 +172,45 @@ dependencies = [
     { name = "tomli", marker = "python_full_version < '3.11'" },
     { name = "typing-extensions" },
 ]
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