Apply patch [skip ci]

Also add support for pinning to a semantic version.

.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/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"

debug_deprecation.py

@@ -0,0 +1,47 @@
+#!/usr/bin/env python
+"""Debug why InjectedState deprecation warning is not being emitted."""
+
+from typing import Annotated
+from langgraph.prebuilt import ToolNode, InjectedState, InjectedStore
+from langgraph.prebuilt.tool_node import _get_state_args, _get_store_arg, _get_reserved_keyword_args
+from langgraph.store.base import BaseStore
+from langchain_core.tools.base import create_schema_from_function
+from langchain_core.tools import StructuredTool
+
+
+def tool_with_injected_state(x: int, state: Annotated[dict, InjectedState]) -> str:
+    """Tool using deprecated InjectedState annotation."""
+    return f"state: {state.get('foo', 'none')}"
+
+def tool_with_injected_store(x: int, store: Annotated[BaseStore, InjectedStore()]) -> str:
+    """Tool using deprecated InjectedStore annotation."""
+    return "has store"
+
+# Convert to tools
+tool1 = create_tool(tool_with_injected_state)
+tool2 = create_tool(tool_with_injected_store)
+
+print("Debugging annotation detection:")
+print(f"\nTool 1 (InjectedState):")
+print(f"  _get_state_args: {_get_state_args(tool1)}")
+print(f"  _get_reserved_keyword_args: {_get_reserved_keyword_args(tool1)}")
+
+print(f"\nTool 2 (InjectedStore):")
+print(f"  _get_store_arg: {_get_store_arg(tool2)}")
+print(f"  _get_reserved_keyword_args: {_get_reserved_keyword_args(tool2)}")
+
+# Check the condition for warnings
+state_args = _get_state_args(tool1)
+reserved_args = _get_reserved_keyword_args(tool1)
+print(f"\nTool 1 warning condition:")
+print(f"  state_args: {state_args}")
+print(f"  reserved_args.get('state'): {reserved_args.get('state')}")
+print(f"  Should warn: {bool(state_args and not reserved_args.get('state'))}")
+
+store_arg = _get_store_arg(tool2)
+reserved_args2 = _get_reserved_keyword_args(tool2)
+print(f"\nTool 2 warning condition:")
+print(f"  store_arg: {store_arg}")
+print(f"  reserved_args.get('runtime'): {reserved_args2.get('runtime')}")
+print(f"  Should warn: {bool(store_arg and not reserved_args2.get('runtime'))}")
+

debug_injected_state.py

@@ -0,0 +1,47 @@
+#!/usr/bin/env python
+"""Debug why InjectedState warning is not being emitted."""
+
+from typing import Annotated, get_args
+from langgraph.prebuilt import InjectedState, InjectedStore
+from langgraph.prebuilt.tool_node import get_all_basemodel_annotations, _is_injection
+from langchain_core.tools import StructuredTool
+from langgraph.store.base import BaseStore
+
+
+def tool_with_injected_state(x: int, state: Annotated[dict, InjectedState]) -> str:
+    """Tool using deprecated InjectedState annotation."""
+    return f"state: {state.get('foo', 'none')}"
+
+def tool_with_injected_store(x: int, store: Annotated[BaseStore, InjectedStore()]) -> str:
+    """Tool using deprecated InjectedStore annotation."""
+    return "has store"
+
+# Convert to tools
+tool1 = StructuredTool.from_function(tool_with_injected_state)
+tool2 = StructuredTool.from_function(tool_with_injected_store)
+
+print("Debugging annotation detection:")
+
+print(f"\nTool 1 (InjectedState):")
+schema1 = tool1.get_input_schema()
+print(f"  Schema fields: {list(schema1.__fields__.keys()) if hasattr(schema1, '__fields__') else list(schema1.model_fields.keys())}")
+
+for name, type_ in get_all_basemodel_annotations(schema1).items():
+    print(f"  Field '{name}': type={type_}")
+    args = get_args(type_)
+    print(f"    Type args: {args}")
+    for arg in args:
+        print(f"      Is InjectedState? {_is_injection(arg, InjectedState)}")
+        print(f"      Type of arg: {type(arg)}")
+
+print(f"\nTool 2 (InjectedStore):")
+schema2 = tool2.get_input_schema()
+print(f"  Schema fields: {list(schema2.__fields__.keys()) if hasattr(schema2, '__fields__') else list(schema2.model_fields.keys())}")
+
+for name, type_ in get_all_basemodel_annotations(schema2).items():
+    print(f"  Field '{name}': type={type_}")
+    args = get_args(type_)
+    print(f"    Type args: {args}")
+    for arg in args:
+        print(f"      Is InjectedStore? {_is_injection(arg, InjectedStore)}")
+        print(f"      Type of arg: {type(arg)}")

debug_reserved_detection.py

@@ -0,0 +1,49 @@
+#!/usr/bin/env python
+"""Debug reserved keyword detection for annotated tools."""
+
+from typing import Annotated
+from langgraph.prebuilt import InjectedState, InjectedStore
+from langgraph.prebuilt.tool_node import _get_reserved_keyword_args
+from langchain_core.tools import StructuredTool
+from langgraph.store.base import BaseStore
+
+
+def tool_with_injected_state(x: int, state: Annotated[dict, InjectedState]) -> str:
+    """Tool using deprecated InjectedState annotation."""
+    return f"state: {state.get('foo', 'none')}"
+
+def tool_with_injected_store(x: int, store: Annotated[BaseStore, InjectedStore()]) -> str:
+    """Tool using deprecated InjectedStore annotation."""
+    return "has store"
+
+def tool_with_reserved_state(x: int, state) -> str:
+    """Tool using reserved keyword 'state'."""
+    return f"state: {state.get('foo', 'none')}"
+
+# Convert to tools
+tool1 = StructuredTool.from_function(tool_with_injected_state)
+tool2 = StructuredTool.from_function(tool_with_injected_store)
+tool3 = StructuredTool.from_function(tool_with_reserved_state)
+
+print("Debugging reserved keyword detection:")
+
+print(f"\nTool 1 (Annotated[dict, InjectedState]):")
+reserved1 = _get_reserved_keyword_args(tool1)
+print(f"  Reserved args: {reserved1}")
+print(f"  Has 'state' as reserved? {'state' in reserved1}")
+
+print(f"\nTool 2 (Annotated[BaseStore, InjectedStore()]):")
+reserved2 = _get_reserved_keyword_args(tool2)
+print(f"  Reserved args: {reserved2}")
+print(f"  Has 'runtime' as reserved? {'runtime' in reserved2}")
+
+print(f"\nTool 3 (plain 'state' parameter):")
+reserved3 = _get_reserved_keyword_args(tool3)
+print(f"  Reserved args: {reserved3}")
+print(f"  Has 'state' as reserved? {'state' in reserved3}")
+
+# The issue might be that 'state' is being detected as a reserved keyword
+# even when it has an annotation
+print("\nConclusion:")
+print("If tool1 shows 'state' as reserved, that's the bug - it shouldn't be")
+print("considered reserved when it has an InjectedState annotation.")

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/agents/multi-agent.md

@@ -367,13 +367,13 @@ To implement handoffs with `createReactAgent`, you need to:
 
 3.  Define a parent graph that contains individual agents as nodes:
 
-        ```typescript
-        import { StateGraph, MessagesZodState } from "@langchain/langgraph";
-        const multiAgentGraph = new StateGraph(MessagesZodState)
-          .addNode("flight_assistant", flightAssistant)
-          .addNode("hotel_assistant", hotelAssistant)
-          // ...
-        ```
+    ```typescript
+    import { StateGraph, MessagesZodState } from "@langchain/langgraph";
+    const multiAgentGraph = new StateGraph(MessagesZodState)
+      .addNode("flight_assistant", flightAssistant)
+      .addNode("hotel_assistant", hotelAssistant)
+      // ...
+    ```
 
     :::
 
@@ -384,7 +384,7 @@ Putting this together, here is how you can implement a simple multi-agent system
 ```python
 from typing import Annotated
 from langchain_core.tools import tool, InjectedToolCallId
-from langgraph.prebuilt import create_react_agent, InjectedState
+from langgraph.prebuilt import create_react_agent
 from langgraph.graph import StateGraph, START, MessagesState
 from langgraph.types import Command
 
@@ -395,7 +395,7 @@ def create_handoff_tool(*, agent_name: str, description: str | None = None):
     @tool(name, description=description)
     def handoff_tool(
         # highlight-next-line
-        state: Annotated[MessagesState, InjectedState], # (1)!
+        state,  # (1)! Reserved keyword - automatically injected
         # highlight-next-line
         tool_call_id: Annotated[str, InjectedToolCallId],
     ) -> Command:
@@ -619,7 +619,8 @@ for await (const chunk of multiAgentGraph.stream({
 3. Name of the agent or node to hand off to.
 4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
 5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
-   :::
+
+:::
 
 !!! Note
 
@@ -635,3 +636,5 @@ Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraph-super
 :::js
 Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-supervisor#customizing-handoff-tools) and [swarm](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-swarm#customizing-handoff-tools) documentation to learn how to customize handoffs.
 :::
+
+

docs/docs/cloud/reference/api/openapi.json

@@ -28,6 +28,14 @@
     {
       "name": "Store",
       "description": "Store is an API for managing persistent key-value store (long-term memory) that is available from any thread."
+    },
+    {
+      "name": "MCP",
+      "description": "Model Context Protocol related endpoints for exposing an agent as an MCP server."
+    },
+    {
+      "name": "System",
+      "description": "System endpoints for health checks, metrics, and server information."
     }
   ],
   "paths": {
@@ -149,6 +157,59 @@
         }
       }
     },
+    "/assistants/count": {
+      "post": {
+        "tags": [
+          "Assistants"
+        ],
+        "summary": "Count Assistants",
+        "description": "Get the count of assistants matching the specified criteria.",
+        "operationId": "count_assistants_assistants_count_post",
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/AssistantCountRequest"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "200": {
+            "description": "Success",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "type": "integer",
+                  "title": "Count"
+                }
+              }
+            }
+          },
+          "404": {
+            "description": "Not Found",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Validation Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
     "/assistants/{assistant_id}": {
       "get": {
         "tags": [
@@ -805,6 +866,59 @@
         }
       }
     },
+    "/threads/count": {
+      "post": {
+        "tags": [
+          "Threads"
+        ],
+        "summary": "Count Threads",
+        "description": "Get the count of threads matching the specified criteria.",
+        "operationId": "count_threads_threads_count_post",
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/ThreadCountRequest"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "200": {
+            "description": "Success",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "type": "integer",
+                  "title": "Count"
+                }
+              }
+            }
+          },
+          "404": {
+            "description": "Not Found",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Validation Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
     "/threads/{thread_id}/state": {
       "get": {
         "tags": [
@@ -1461,6 +1575,30 @@
             },
             "name": "status",
             "in": "query"
+          },
+          {
+            "required": false,
+            "schema": {
+              "type": "array",
+              "items": {
+                "type": "string",
+                "enum": [
+                  "run_id",
+                  "thread_id",
+                  "assistant_id",
+                  "created_at",
+                  "updated_at",
+                  "status",
+                  "metadata",
+                  "kwargs",
+                  "multitask_strategy"
+                ]
+              },
+              "title": "Select",
+              "description": "Specify which fields to return. If not provided, all fields are returned."
+            },
+            "name": "select",
+            "in": "query"
           }
         ],
         "responses": {
@@ -2312,6 +2450,59 @@
         }
       }
     },
+    "/runs/crons/count": {
+      "post": {
+        "tags": [
+          "Crons (Plus tier)"
+        ],
+        "summary": "Count Crons",
+        "description": "Get the count of crons matching the specified criteria.",
+        "operationId": "count_crons_runs_crons_count_post",
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/CronCountRequest"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "200": {
+            "description": "Success",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "type": "integer",
+                  "title": "Count"
+                }
+              }
+            }
+          },
+          "404": {
+            "description": "Not Found",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Validation Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
     "/runs/stream": {
       "post": {
         "tags": [
@@ -2996,6 +3187,153 @@
           "MCP"
         ]
       }
+    },
+    "/info": {
+      "get": {
+        "tags": [
+          "System"
+        ],
+        "summary": "Server Information",
+        "description": "Get server version information, feature flags, and metadata.",
+        "operationId": "server_info_info_get",
+        "responses": {
+          "200": {
+            "description": "Success",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "type": "object",
+                  "properties": {
+                    "version": {
+                      "type": "string",
+                      "title": "Version",
+                      "description": "LangGraph API version"
+                    },
+                    "langgraph_py_version": {
+                      "type": "string",
+                      "title": "LangGraph Python Version",
+                      "description": "LangGraph Python library version"
+                    },
+                    "flags": {
+                      "type": "object",
+                      "title": "Feature Flags",
+                      "description": "Enabled features and capabilities"
+                    },
+                    "metadata": {
+                      "type": "object",
+                      "title": "Metadata",
+                      "description": "Server deployment metadata"
+                    }
+                  },
+                  "required": ["version", "langgraph_py_version", "flags", "metadata"],
+                  "title": "ServerInfo"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
+    "/metrics": {
+      "get": {
+        "tags": [
+          "System"
+        ],
+        "summary": "System Metrics",
+        "description": "Get system metrics in Prometheus or JSON format for monitoring and observability.",
+        "operationId": "system_metrics_metrics_get",
+        "parameters": [
+          {
+            "name": "format",
+            "in": "query",
+            "required": false,
+            "schema": {
+              "type": "string",
+              "enum": ["prometheus", "json"],
+              "default": "prometheus",
+              "title": "Output Format",
+              "description": "Response format: prometheus (default) or json"
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "Success",
+            "content": {
+              "text/plain": {
+                "schema": {
+                  "type": "string",
+                  "title": "Prometheus Metrics",
+                  "description": "Metrics in Prometheus exposition format"
+                }
+              },
+              "application/json": {
+                "schema": {
+                  "type": "object",
+                  "title": "JSON Metrics",
+                  "description": "Metrics in JSON format including queue stats, worker stats, and HTTP metrics"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
+    "/ok": {
+      "get": {
+        "tags": [
+          "System"
+        ],
+        "summary": "Health Check",
+        "description": "Check the health status of the server. Optionally check database connectivity.",
+        "operationId": "health_check_ok_get",
+        "parameters": [
+          {
+            "name": "check_db",
+            "in": "query",
+            "required": false,
+            "schema": {
+              "type": "integer",
+              "enum": [0, 1],
+              "default": 0,
+              "title": "Check Database",
+              "description": "Whether to check database connectivity (0=false, 1=true)"
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "Success",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "type": "object",
+                  "properties": {
+                    "ok": {
+                      "type": "boolean",
+                      "const": true,
+                      "title": "OK",
+                      "description": "Indicates the server is healthy"
+                    }
+                  },
+                  "required": ["ok"],
+                  "title": "HealthResponse"
+                }
+              }
+            }
+          },
+          "500": {
+            "description": "Internal Server Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          }
+        }
+      }
     }
   },
   "components": {
@@ -3035,6 +3373,11 @@
             "title": "Config",
             "description": "The assistant config."
           },
+          "context": {
+            "type": "object",
+            "title": "Context",
+            "description": "Static context added to the assistant."
+          },
           "created_at": {
             "type": "string",
             "format": "date-time",
@@ -3100,6 +3443,11 @@
             "title": "Config",
             "description": "Configuration to use for the graph. Useful when graph is configurable and you want to create different assistants based on different configurations."
           },
+          "context": {
+            "type": "object",
+            "title": "Context",
+            "description": "Static context added to the assistant."
+          },
           "metadata": {
             "type": "object",
             "title": "Metadata",
@@ -3148,6 +3496,11 @@
             "title": "Config",
             "description": "Configuration to use for the graph. Useful when graph is configurable and you want to update the assistant's configuration."
           },
+          "context": {
+            "type": "object",
+            "title": "Context",
+            "description": "Static context added to the assistant."
+          },
           "metadata": {
             "type": "object",
             "title": "Metadata",
@@ -3209,6 +3562,12 @@
             "title": "Cron Id",
             "description": "The ID of the cron."
           },
+          "assistant_id": {
+            "type": ["string", "null"],
+            "format": "uuid",
+            "title": "Assistant Id",
+            "description": "The ID of the assistant."
+          },
           "thread_id": {
             "type": "string",
             "format": "uuid",
@@ -3238,10 +3597,26 @@
             "title": "Updated At",
             "description": "The last time the cron was updated."
           },
+          "user_id": {
+            "type": ["string", "null"],
+            "title": "User Id",
+            "description": "The ID of the user."
+          },
           "payload": {
             "type": "object",
             "title": "Payload",
             "description": "The run payload to use for creating new run."
+          },
+          "next_run_date": {
+            "type": ["string", "null"],
+            "format": "date-time",
+            "title": "Next Run Date",
+            "description": "The next run date of the cron."
+          },
+          "metadata": {
+            "type": "object",
+            "title": "Metadata",
+            "description": "The cron metadata."
           }
         },
         "type": "object",
@@ -3326,6 +3701,11 @@
             "title": "Config",
             "description": "The configuration for the assistant."
           },
+          "context": {
+            "type": "object",
+            "title": "Context",
+            "description": "Static context added to the assistant."
+          },
           "webhook": {
             "type": "string",
             "maxLength": 65536,
@@ -3380,7 +3760,7 @@
             ],
             "title": "Multitask Strategy",
             "description": "Multitask strategy to use. Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'.",
-            "default": "reject"
+            "default": "enqueue"
           }
         },
         "type": "object",
@@ -3397,7 +3777,7 @@
             "type": "string",
             "format": "uuid",
             "title": "Assistant Id",
-            "description": "The assistant ID or graph name to search for."
+            "description": "The assistant ID or graph name to filter by using exact match."
           },
           "thread_id": {
             "type": "string",
@@ -3433,6 +3813,28 @@
             "description": "The order to sort by.",
             "default": "desc",
             "enum": ["asc", "desc"]
+          },
+          "select": {
+            "type": "array",
+            "items": {
+              "type": "string",
+              "enum": [
+                "cron_id",
+                "assistant_id",
+                "thread_id",
+                "end_time",
+                "schedule",
+                "created_at",
+                "updated_at",
+                "user_id",
+                "payload",
+                "next_run_date",
+                "metadata",
+                "now"
+              ]
+            },
+            "title": "Select",
+            "description": "Specify which fields to return. If not provided, all fields are returned."
           }
         },
         "type": "object",
@@ -3440,6 +3842,25 @@
         "title": "CronSearch",
         "description": "Payload for listing crons"
       },
+      "CronCountRequest": {
+        "properties": {
+          "assistant_id": {
+            "type": "string",
+            "format": "uuid",
+            "title": "Assistant Id",
+            "description": "The assistant ID or graph name to search for."
+          },
+          "thread_id": {
+            "type": "string",
+            "format": "uuid",
+            "title": "Thread Id",
+            "description": "The thread ID to search for."
+          }
+        },
+        "type": "object",
+        "title": "CronCountRequest",
+        "description": "Payload for counting crons"
+      },
       "GraphSchema": {
         "properties": {
           "graph_id": {
@@ -3466,13 +3887,17 @@
             "type": "object",
             "title": "Config Schema",
             "description": "The schema for the graph config. Missing if unable to generate JSON schema from graph."
+          },
+          "context_schema": {
+            "type": "object",
+            "title": "Context Schema",
+            "description": "The schema for the graph context. Missing if unable to generate JSON schema from graph."
           }
         },
         "type": "object",
         "required": [
           "graph_id",
-          "state_schema",
-          "config_schema"
+          "state_schema"
         ],
         "title": "GraphSchema",
         "description": "Defines the structure and properties of a graph."
@@ -3498,14 +3923,18 @@
             "type": "object",
             "title": "Config Schema",
             "description": "The schema for the graph config. Missing if unable to generate JSON schema from graph."
+          },
+          "context_schema": {
+            "type": "object",
+            "title": "Context Schema",
+            "description": "The schema for the graph context. Missing if unable to generate JSON schema from graph."
           }
         },
         "type": "object",
         "required": [
           "input_schema",
           "output_schema",
-          "state_schema",
-          "config_schema"
+          "state_schema"
         ],
         "title": "GraphSchemaNoId",
         "description": "Defines the structure and properties of a graph without an ID."
@@ -3516,7 +3945,7 @@
           "$ref": "#/components/schemas/GraphSchemaNoId"
         },
         "title": "Subgraphs",
-        "description": "Map of graph name to graph schema metadata (`input_schema`, `output_schema`, `state_schema`, `config_schema`)."
+        "description": "Map of graph name to graph schema metadata (`input_schema`, `output_schema`, `state_schema`, `config_schema`, `context_schema`)."
       },
       "Run": {
         "properties": {
@@ -3766,6 +4195,11 @@
             "title": "Config",
             "description": "The configuration for the assistant."
           },
+          "context": {
+            "type": "object",
+            "title": "Context",
+            "description": "Static context added to the assistant."
+          },
           "webhook": {
             "type": "string",
             "maxLength": 65536,
@@ -3819,6 +4253,8 @@
                     "values",
                     "messages",
                     "messages-tuple",
+                    "tasks",
+                    "checkpoints",
                     "updates",
                     "events",
                     "debug",
@@ -3833,6 +4269,8 @@
                   "values",
                   "messages",
                   "messages-tuple",
+                  "tasks",
+                  "checkpoints",
                   "updates",
                   "events",
                   "debug",
@@ -3886,7 +4324,7 @@
             ],
             "title": "Multitask Strategy",
             "description": "Multitask strategy to use. Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'.",
-            "default": "reject"
+            "default": "enqueue"
           },
           "if_not_exists": {
             "type": "string",
@@ -4005,6 +4443,11 @@
             "title": "Config",
             "description": "The configuration for the assistant."
           },
+          "context": {
+            "type": "object",
+            "title": "Context",
+            "description": "Static context added to the assistant."
+          },
           "webhook": {
             "type": "string",
             "maxLength": 65536,
@@ -4058,6 +4501,8 @@
                     "values",
                     "messages",
                     "messages-tuple",
+                    "tasks",
+                    "checkpoints",
                     "updates",
                     "events",
                     "debug",
@@ -4072,6 +4517,8 @@
                   "values",
                   "messages",
                   "messages-tuple",
+                  "tasks",
+                  "checkpoints",
                   "updates",
                   "events",
                   "debug",
@@ -4191,12 +4638,49 @@
             ],
             "title": "Sort Order",
             "description": "The order to sort by."
+          },
+          "select": {
+            "type": "array",
+            "items": {
+              "type": "string",
+              "enum": [
+                "assistant_id",
+                "graph_id", 
+                "name",
+                "description",
+                "config",
+                "context",
+                "created_at",
+                "updated_at",
+                "metadata",
+                "version"
+              ]
+            },
+            "title": "Select",
+            "description": "Specify which fields to return. If not provided, all fields are returned."
           }
         },
         "type": "object",
         "title": "AssistantSearchRequest",
         "description": "Payload for listing assistants."
       },
+      "AssistantCountRequest": {
+        "properties": {
+          "metadata": {
+            "type": "object",
+            "title": "Metadata",
+            "description": "Metadata to filter by. Exact match filter for each KV pair."
+          },
+          "graph_id": {
+            "type": "string",
+            "title": "Graph Id",
+            "description": "The ID of the graph to filter by. The graph ID is normally set in your langgraph.json configuration."
+          }
+        },
+        "type": "object",
+        "title": "AssistantCountRequest",
+        "description": "Payload for counting assistants."
+      },
       "AssistantVersionsSearchRequest": {
         "properties": {
           "metadata": {
@@ -4281,12 +4765,59 @@
             ],
             "title": "Sort Order",
             "description": "Sort order."
+          },
+          "select": {
+            "type": "array",
+            "items": {
+              "type": "string",
+              "enum": [
+                "thread_id",
+                "created_at",
+                "updated_at",
+                "metadata",
+                "config",
+                "context",
+                "status",
+                "values",
+                "interrupts"
+              ]
+            },
+            "title": "Select",
+            "description": "Specify which fields to return. If not provided, all fields are returned."
           }
         },
         "type": "object",
         "title": "ThreadSearchRequest",
         "description": "Payload for listing threads."
       },
+      "ThreadCountRequest": {
+        "properties": {
+          "metadata": {
+            "type": "object",
+            "title": "Metadata",
+            "description": "Thread metadata to filter on."
+          },
+          "values": {
+            "type": "object",
+            "title": "Values",
+            "description": "State values to filter on."
+          },
+          "status": {
+            "type": "string",
+            "enum": [
+              "idle",
+              "busy",
+              "interrupted",
+              "error"
+            ],
+            "title": "Status",
+            "description": "Thread status to filter on."
+          }
+        },
+        "type": "object",
+        "title": "ThreadCountRequest",
+        "description": "Payload for counting threads."
+      },
       "Thread": {
         "properties": {
           "thread_id": {
@@ -4312,6 +4843,11 @@
             "title": "Metadata",
             "description": "The thread metadata."
           },
+          "config": {
+            "type": "object",
+            "title": "Config",
+            "description": "The thread config."
+          },
           "status": {
             "type": "string",
             "enum": [
@@ -4327,6 +4863,11 @@
             "type": "object",
             "title": "Values",
             "description": "The current state of the thread."
+          },
+          "interrupts": {
+            "type": "object",
+            "title": "Interrupts",
+            "description": "The current interrupts of the thread."
           }
         },
         "type": "object",
@@ -4476,7 +5017,9 @@
                 },
                 "interrupts": {
                   "type": "array",
-                  "items": {}
+                  "items": {
+                    "$ref": "#/components/schemas/Interrupt"
+                  }
                 },
                 "checkpoint": {
                   "$ref": "#/components/schemas/CheckpointConfig",
@@ -4509,6 +5052,12 @@
           "parent_checkpoint": {
             "type": "object",
             "title": "Parent Checkpoint"
+          },
+          "interrupts": {
+            "type": "array",
+            "items": {
+              "$ref": "#/components/schemas/Interrupt"
+            }
           }
         },
         "type": "object",
@@ -4527,7 +5076,7 @@
             "type": "integer",
             "title": "Limit",
             "description": "The maximum number of states to return.",
-            "default": 10,
+            "default": 1,
             "maximum": 1000,
             "minimum": 1
           },
@@ -4954,6 +5503,24 @@
             }
           }
         }
+      },
+      "Interrupt": {
+        "type": "object",
+        "properties": {
+          "id": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "value": {
+            "type": "object"
+          }
+        },
+        "title": "Interrupt",
+        "required": [
+          "value"
+        ]
       }
     }
   }

docs/docs/concepts/mcp.md

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

docs/docs/concepts/multi_agent.md

@@ -498,15 +498,14 @@ In this variant of the [supervisor](#supervisor) architecture, we define a super
 :::python
 
 ```python
-from typing import Annotated
 from langchain_openai import ChatOpenAI
-from langgraph.prebuilt import InjectedState, create_react_agent
+from langgraph.prebuilt import create_react_agent
 
 model = ChatOpenAI()
 
 # this is the agent function that will be called as tool
-# notice that you can pass the state to the tool via InjectedState annotation
-def agent_1(state: Annotated[dict, InjectedState]):
+# notice that you can pass the state to the tool via the reserved keyword 'state'
+def agent_1(state):  # 'state' is a reserved keyword - automatically injected
     # you can pass relevant parts of the state to the LLM (e.g., state["messages"])
     # and add any additional logic (different models, custom prompts, structured output, etc.)
     response = model.invoke(...)
@@ -515,7 +514,7 @@ def agent_1(state: Annotated[dict, InjectedState]):
     # by the prebuilt create_react_agent (supervisor)
     return response.content
 
-def agent_2(state: Annotated[dict, InjectedState]):
+def agent_2(state):  # 'state' is a reserved keyword - automatically injected
     response = model.invoke(...)
     return response.content
 
@@ -899,3 +898,4 @@ An agent might need to have a different state schema from the rest of the agents
 
 - Define [subgraph](./subgraphs.md) agents with a separate state schema. If there are no shared state keys (channels) between the subgraph and the parent graph, it's important to [add input / output transformations](../how-tos/subgraph.ipynb#different-state-schemas) so that the parent graph knows how to communicate with the subgraphs.
 - Define agent node functions with a [private input state schema](../how-tos/graph-api.ipynb#pass-private-state-between-nodes) that is distinct from the overall graph state schema. This allows passing information that is only needed for executing that particular agent.
+

docs/docs/concepts/persistence.md

@@ -315,7 +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/multi_agent.md

@@ -22,10 +22,11 @@ To set up communication between the agents in a multi-agent system you can use [
 
 To implement handoffs, you can return `Command` objects from your agent nodes or tools:
 
+:::python
 ```python
 from typing import Annotated
 from langchain_core.tools import tool, InjectedToolCallId
-from langgraph.prebuilt import create_react_agent, InjectedState
+from langgraph.prebuilt import create_react_agent
 from langgraph.graph import StateGraph, START, MessagesState
 from langgraph.types import Command
 
@@ -36,7 +37,7 @@ def create_handoff_tool(*, agent_name: str, description: str | None = None):
     @tool(name, description=description)
     def handoff_tool(
         # highlight-next-line
-        state: Annotated[MessagesState, InjectedState], # (1)!
+        state,  # (1)! Reserved keyword - automatically injected
         # highlight-next-line
         tool_call_id: Annotated[str, InjectedToolCallId],
     ) -> Command:
@@ -57,7 +58,7 @@ def create_handoff_tool(*, agent_name: str, description: str | None = None):
     return handoff_tool
 ```
 
-1. Access the [state](../concepts/low_level.md#state) of the agent that is calling the handoff tool using the @[InjectedState] annotation. 
+1. Access the [state](../concepts/low_level.md#state) of the agent using the reserved keyword `state`. No annotation needed - LangGraph automatically injects the state when it sees this parameter name. 
 2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.
 3. Name of the agent or node to hand off to.
 4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
@@ -73,32 +74,115 @@ def create_handoff_tool(*, agent_name: str, description: str | None = None):
         commands = [tools_by_name[tool_call["name"]].invoke(tool_call) for tool_call in tool_calls]
         return commands
     ```
+:::
+
+:::js
+```typescript
+import { tool } from "@langchain/core/tools";
+import { Command, MessagesZodState } from "@langchain/langgraph";
+import { z } from "zod";
+
+function createHandoffTool({
+  agentName,
+  description,
+}: {
+  agentName: string;
+  description?: string;
+}) {
+  const name = `transfer_to_${agentName}`;
+  const toolDescription = description || `Transfer to ${agentName}`;
+
+  return tool(
+    async (_, config) => {
+      // (1)!
+      const state = config.state;
+      const toolCallId = config.toolCall.id;
+
+      const toolMessage = {
+        role: "tool" as const,
+        content: `Successfully transferred to ${agentName}`,
+        name: name,
+        tool_call_id: toolCallId,
+      };
+
+      return new Command({
+        // (3)!
+        goto: agentName,
+        // (4)!
+        update: { messages: [...state.messages, toolMessage] },
+        // (5)!
+        graph: Command.PARENT,
+      });
+    },
+    {
+      name,
+      description: toolDescription,
+      schema: z.object({}),
+    }
+  );
+}
+```
+
+1. Access the [state](../concepts/low_level.md#state) of the agent that is calling the handoff tool through the `config` parameter.
+2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.
+3. Name of the agent or node to hand off to.
+4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
+5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
+
+!!! tip
+
+    If you want to use tools that return `Command`, you can either use prebuilt @[`create_react_agent`][create_react_agent] / @[`ToolNode`][ToolNode] components, or implement your own tool-executing node that collects `Command` objects returned by the tools and returns a list of them, e.g.:
+    
+    ```typescript
+    const callTools = async (state) => {
+      // ...
+      const commands = await Promise.all(
+        toolCalls.map(toolCall => toolsByName[toolCall.name].invoke(toolCall))
+      );
+      return commands;
+    };
+    ```
+:::
 
 !!! Important
 
     This handoff implementation assumes that:
     
-      - each agent receives overall message history (across all agents) in the multi-agent system as its input. If you want more control over agent inputs, see [this section](#control-agent-inputs)
-      - each agent outputs its internal messages history to the overall message history of the multi-agent system. If you want more control over **how agent outputs are added**, wrap the agent in a separate node function:
+    - each agent receives overall message history (across all agents) in the multi-agent system as its input. If you want more control over agent inputs, see [this section](#control-agent-inputs)
+    - each agent outputs its internal messages history to the overall message history of the multi-agent system. If you want more control over **how agent outputs are added**, wrap the agent in a separate node function:
 
-        ```python
-        def call_hotel_assistant(state):
-            # return agent's final response,
-            # excluding inner monologue
-            response = hotel_assistant.invoke(state)
-            # highlight-next-line
-            return {"messages": response["messages"][-1]}
-        ```
+      :::python
+      ```python
+      def call_hotel_assistant(state):
+          # return agent's final response,
+          # excluding inner monologue
+          response = hotel_assistant.invoke(state)
+          # highlight-next-line
+          return {"messages": response["messages"][-1]}
+      ```
+      :::
+
+      :::js
+      ```typescript
+      const callHotelAssistant = async (state) => {
+        // return agent's final response,
+        // excluding inner monologue
+        const response = await hotelAssistant.invoke(state);
+        // highlight-next-line
+        return { messages: [response.messages.at(-1)] };
+      };
+      ```
+      :::
 
 ### Control agent inputs
 
+:::python
 You can use the @[`Send()`][Send] primitive to directly send data to the worker agents during the handoff. For example, you can request that the calling agent populate a task description for the next agent:
 
 ```python
 
 from typing import Annotated
 from langchain_core.tools import tool, InjectedToolCallId
-from langgraph.prebuilt import InjectedState
 from langgraph.graph import StateGraph, START, MessagesState
 # highlight-next-line
 from langgraph.types import Command, Send
@@ -117,7 +201,7 @@ def create_task_description_handoff_tool(
             "Description of what the next agent should do, including all of the relevant context.",
         ],
         # these parameters are ignored by the LLM
-        state: Annotated[MessagesState, InjectedState],
+        state,  # Reserved keyword - automatically injected
     ) -> Command:
         task_description_message = {"role": "user", "content": task_description}
         agent_input = {**state, "messages": [task_description_message]}
@@ -129,6 +213,63 @@ def create_task_description_handoff_tool(
 
     return handoff_tool
 ```
+:::
+
+:::js
+You can use the @[`Send()`][Send] primitive to directly send data to the worker agents during the handoff. For example, you can request that the calling agent populate a task description for the next agent:
+
+```typescript
+import { tool } from "@langchain/core/tools";
+import { Command, Send, MessagesZodState } from "@langchain/langgraph";
+import { z } from "zod";
+
+function createTaskDescriptionHandoffTool({
+  agentName,
+  description,
+}: {
+  agentName: string;
+  description?: string;
+}) {
+  const name = `transfer_to_${agentName}`;
+  const toolDescription = description || `Ask ${agentName} for help.`;
+
+  return tool(
+    async (
+      { taskDescription },
+      config
+    ) => {
+      const state = config.state;
+      
+      const taskDescriptionMessage = {
+        role: "user" as const,
+        content: taskDescription,
+      };
+      const agentInput = {
+        ...state,
+        messages: [taskDescriptionMessage],
+      };
+      
+      return new Command({
+        // highlight-next-line
+        goto: [new Send(agentName, agentInput)],
+        graph: Command.PARENT,
+      });
+    },
+    {
+      name,
+      description: toolDescription,
+      schema: z.object({
+        taskDescription: z
+          .string()
+          .describe(
+            "Description of what the next agent should do, including all of the relevant context."
+          ),
+      }),
+    }
+  );
+}
+```
+:::
 
 See the multi-agent [supervisor](../tutorials/multi_agent/agent_supervisor.md#4-create-delegation-tasks) example for a full example of using @[`Send()`][Send] in handoffs.
 
@@ -136,6 +277,7 @@ See the multi-agent [supervisor](../tutorials/multi_agent/agent_supervisor.md#4-
 
 You can use handoffs in any agents built with LangGraph. We recommend using the prebuilt [agent](../agents/overview.md) or [`ToolNode`](./tool-calling.md#toolnode), as they natively support handoffs tools returning `Command`. Below is an example of how you can implement a multi-agent system for booking travel using handoffs:
 
+:::python
 ```python
 from langgraph.prebuilt import create_react_agent
 from langgraph.graph import StateGraph, START, MessagesState
@@ -176,14 +318,70 @@ multi_agent_graph = (
     .compile()
 )
 ```
+:::
+
+:::js
+```typescript
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { StateGraph, START, MessagesZodState } from "@langchain/langgraph";
+import { z } from "zod";
+
+function createHandoffTool({
+  agentName,
+  description,
+}: {
+  agentName: string;
+  description?: string;
+}) {
+  // same implementation as above
+  // ...
+  return new Command(/* ... */);
+}
+
+// Handoffs
+const transferToHotelAssistant = createHandoffTool({
+  agentName: "hotel_assistant",
+});
+const transferToFlightAssistant = createHandoffTool({
+  agentName: "flight_assistant",
+});
+
+// Define agents
+const flightAssistant = createReactAgent({
+  llm: model,
+  // highlight-next-line
+  tools: [/* ... */, transferToHotelAssistant],
+  // highlight-next-line
+  name: "flight_assistant",
+});
+
+const hotelAssistant = createReactAgent({
+  llm: model,
+  // highlight-next-line
+  tools: [/* ... */, transferToFlightAssistant],
+  // highlight-next-line
+  name: "hotel_assistant",
+});
+
+// Define multi-agent graph
+const multiAgentGraph = new StateGraph(MessagesZodState)
+  // highlight-next-line
+  .addNode("flight_assistant", flightAssistant)
+  // highlight-next-line
+  .addNode("hotel_assistant", hotelAssistant)
+  .addEdge(START, "flight_assistant")
+  .compile();
+```
+:::
 
 ??? example "Full example: Multi-agent system for booking travel"
 
+    :::python
     ```python
     from typing import Annotated
     from langchain_core.messages import convert_to_messages
     from langchain_core.tools import tool, InjectedToolCallId
-    from langgraph.prebuilt import create_react_agent, InjectedState
+    from langgraph.prebuilt import create_react_agent
     from langgraph.graph import StateGraph, START, MessagesState
     from langgraph.types import Command
     
@@ -236,7 +434,7 @@ multi_agent_graph = (
         @tool(name, description=description)
         def handoff_tool(
             # highlight-next-line
-            state: Annotated[MessagesState, InjectedState], # (1)!
+            state,  # (1)! Reserved keyword - automatically injected
             # highlight-next-line
             tool_call_id: Annotated[str, InjectedToolCallId],
         ) -> Command:
@@ -323,6 +521,183 @@ multi_agent_graph = (
     3. Name of the agent or node to hand off to.
     4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
     5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
+    :::
+
+    :::js
+    ```typescript
+    import { tool } from "@langchain/core/tools";
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+    import { StateGraph, START, MessagesZodState, Command } from "@langchain/langgraph";
+    import { ChatAnthropic } from "@langchain/anthropic";
+    import { isBaseMessage } from "@langchain/core/messages";
+    import { z } from "zod";
+
+    // We'll use a helper to render the streamed agent outputs nicely
+    const prettyPrintMessages = (update: Record<string, any>) => {
+      // Handle tuple case with namespace
+      if (Array.isArray(update)) {
+        const [ns, updateData] = update;
+        // Skip parent graph updates in the printouts
+        if (ns.length === 0) {
+          return;
+        }
+
+        const graphId = ns[ns.length - 1].split(":")[0];
+        console.log(`Update from subgraph ${graphId}:\n`);
+        update = updateData;
+      }
+
+      for (const [nodeName, updateValue] of Object.entries(update)) {
+        console.log(`Update from node ${nodeName}:\n`);
+
+        const messages = updateValue.messages || [];
+        for (const message of messages) {
+          if (isBaseMessage(message)) {
+            const textContent =
+              typeof message.content === "string"
+                ? message.content
+                : JSON.stringify(message.content);
+            console.log(`${message.getType()}: ${textContent}`);
+          }
+        }
+        console.log("\n");
+      }
+    };
+
+    function createHandoffTool({
+      agentName,
+      description,
+    }: {
+      agentName: string;
+      description?: string;
+    }) {
+      const name = `transfer_to_${agentName}`;
+      const toolDescription = description || `Transfer to ${agentName}`;
+
+      return tool(
+        async (_, config) => {
+          // highlight-next-line
+          const state = config.state; // (1)!
+          const toolCallId = config.toolCall.id;
+
+          const toolMessage = {
+            role: "tool" as const,
+            content: `Successfully transferred to ${agentName}`,
+            name: name,
+            tool_call_id: toolCallId,
+          };
+
+          return new Command({
+            // highlight-next-line
+            goto: agentName, // (3)!
+            // highlight-next-line
+            update: { messages: [...state.messages, toolMessage] }, // (4)!
+            // highlight-next-line
+            graph: Command.PARENT, // (5)!
+          });
+        },
+        {
+          name,
+          description: toolDescription,
+          schema: z.object({}),
+        }
+      );
+    }
+
+    // Handoffs
+    const transferToHotelAssistant = createHandoffTool({
+      agentName: "hotel_assistant",
+      description: "Transfer user to the hotel-booking assistant.",
+    });
+
+    const transferToFlightAssistant = createHandoffTool({
+      agentName: "flight_assistant",
+      description: "Transfer user to the flight-booking assistant.",
+    });
+
+    // Simple agent tools
+    const bookHotel = tool(
+      async ({ hotelName }) => {
+        return `Successfully booked a stay at ${hotelName}.`;
+      },
+      {
+        name: "book_hotel",
+        description: "Book a hotel",
+        schema: z.object({
+          hotelName: z.string(),
+        }),
+      }
+    );
+
+    const bookFlight = tool(
+      async ({ fromAirport, toAirport }) => {
+        return `Successfully booked a flight from ${fromAirport} to ${toAirport}.`;
+      },
+      {
+        name: "book_flight",
+        description: "Book a flight",
+        schema: z.object({
+          fromAirport: z.string(),
+          toAirport: z.string(),
+        }),
+      }
+    );
+
+    const model = new ChatAnthropic({
+      model: "claude-3-5-sonnet-latest",
+    });
+
+    // Define agents
+    const flightAssistant = createReactAgent({
+      llm: model,
+      // highlight-next-line
+      tools: [bookFlight, transferToHotelAssistant],
+      prompt: "You are a flight booking assistant",
+      // highlight-next-line
+      name: "flight_assistant",
+    });
+
+    const hotelAssistant = createReactAgent({
+      llm: model,
+      // highlight-next-line
+      tools: [bookHotel, transferToFlightAssistant],
+      prompt: "You are a hotel booking assistant",
+      // highlight-next-line
+      name: "hotel_assistant",
+    });
+
+    // Define multi-agent graph
+    const multiAgentGraph = new StateGraph(MessagesZodState)
+      .addNode("flight_assistant", flightAssistant)
+      .addNode("hotel_assistant", hotelAssistant)
+      .addEdge(START, "flight_assistant")
+      .compile();
+
+    // Run the multi-agent graph
+    const stream = await multiAgentGraph.stream(
+      {
+        messages: [
+          {
+            role: "user",
+            content: "book a flight from BOS to JFK and a stay at McKittrick Hotel",
+          },
+        ],
+      },
+      // highlight-next-line
+      { subgraphs: true }
+    );
+
+    for await (const chunk of stream) {
+      prettyPrintMessages(chunk);
+    }
+    ```
+
+    1. Access agent's state
+    2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.
+    3. Name of the agent or node to hand off to.
+    4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
+    5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
+    :::
 
 ## Multi-turn conversation
 
@@ -333,6 +708,7 @@ The agents can then be implemented as nodes in a graph that executes agent steps
 1. **Wait for user input** to continue the conversation, or  
 2. **Route to another agent** (or back to itself, such as in a loop) via a [handoff](#handoffs)
 
+:::python
 ```python
 def human(state) -> Command[Literal["agent", "another_agent"]]:
     """A node for collecting user input."""
@@ -360,6 +736,44 @@ def agent(state) -> Command[Literal["agent", "another_agent", "human"]]:
     else:
         return Command(goto="human") # Go to human node
 ```
+:::
+
+:::js
+```typescript
+import { interrupt, Command } from "@langchain/langgraph";
+
+function human(state: MessagesState): Command {
+  const userInput: string = interrupt("Ready for user input.");
+
+  // Determine the active agent
+  const activeAgent = /* ... */;
+
+  return new Command({
+    update: {
+      messages: [{
+        role: "human",
+        content: userInput,
+      }]
+    },
+    goto: activeAgent,
+  });
+}
+
+function agent(state: MessagesState): Command {
+  // The condition for routing/halting can be anything, e.g. LLM tool call / structured output, etc.
+  const goto = getNextAgent(/* ... */); // 'agent' / 'anotherAgent'
+
+  if (goto) {
+    return new Command({
+      goto,
+      update: { myStateKey: "myStateValue" }
+    });
+  }
+
+  return new Command({ goto: "human" });
+}
+```
+:::
 
 ??? example "Full example: multi-agent system for travel recommendations"
 
@@ -370,10 +784,11 @@ def agent(state) -> Command[Literal["agent", "another_agent", "human"]]:
     * travel_advisor: can help with travel destination recommendations. Can ask hotel_advisor for help.
     * hotel_advisor: can help with hotel recommendations. Can ask travel_advisor for help.
 
+    :::python
     ```python
     from langchain_anthropic import ChatAnthropic
     from langgraph.graph import MessagesState, StateGraph, START
-    from langgraph.prebuilt import create_react_agent, InjectedState
+    from langgraph.prebuilt import create_react_agent
     from langgraph.types import Command, interrupt
     from langgraph.checkpoint.memory import InMemorySaver
     
@@ -571,10 +986,272 @@ def agent(state) -> Command[Literal["agent", "another_agent", "human"]]:
     
     Would you like more specific information about any of these activities or would you like to know about other options in the area?
     ```
+    :::
+
+    :::js
+    ```typescript
+    import { ChatAnthropic } from "@langchain/anthropic";
+    import { StateGraph, START, MessagesZodState, Command, interrupt, MemorySaver } from "@langchain/langgraph";
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+    import { tool } from "@langchain/core/tools";
+    import { z } from "zod";
+
+    const model = new ChatAnthropic({ model: "claude-3-5-sonnet-latest" });
+
+    const MultiAgentState = MessagesZodState.extend({
+      lastActiveAgent: z.string().optional(),
+    });
+
+    // Define travel advisor tools
+    const getTravelRecommendations = tool(
+      async () => {
+        // Placeholder implementation
+        return "Based on current trends, I recommend visiting Japan, Portugal, or New Zealand.";
+      },
+      {
+        name: "get_travel_recommendations",
+        description: "Get current travel destination recommendations",
+        schema: z.object({}),
+      }
+    );
+
+    const makeHandoffTool = (agentName: string) => {
+      return tool(
+        async (_, config) => {
+          const state = config.state;
+          const toolCallId = config.toolCall.id;
+
+          const toolMessage = {
+            role: "tool" as const,
+            content: `Successfully transferred to ${agentName}`,
+            name: `transfer_to_${agentName}`,
+            tool_call_id: toolCallId,
+          };
+
+          return new Command({
+            goto: agentName,
+            update: { messages: [...state.messages, toolMessage] },
+            graph: Command.PARENT,
+          });
+        },
+        {
+          name: `transfer_to_${agentName}`,
+          description: `Transfer to ${agentName}`,
+          schema: z.object({}),
+        }
+      );
+    };
+
+    const travelAdvisorTools = [
+      getTravelRecommendations,
+      makeHandoffTool("hotel_advisor"),
+    ];
+
+    const travelAdvisor = createReactAgent({
+      llm: model,
+      tools: travelAdvisorTools,
+      prompt: [
+        "You are a general travel expert that can recommend travel destinations (e.g. countries, cities, etc). ",
+        "If you need hotel recommendations, ask 'hotel_advisor' for help. ",
+        "You MUST include human-readable response before transferring to another agent."
+      ].join("")
+    });
+
+    const callTravelAdvisor = async (
+      state: z.infer<typeof MultiAgentState>
+    ): Promise<Command> => {
+      const response = await travelAdvisor.invoke(state);
+      const update = { ...response, lastActiveAgent: "travel_advisor" };
+      return new Command({ update, goto: "human" });
+    };
+
+    // Define hotel advisor tools
+    const getHotelRecommendations = tool(
+      async () => {
+        // Placeholder implementation
+        return "I recommend the Ritz-Carlton for luxury stays or boutique hotels for unique experiences.";
+      },
+      {
+        name: "get_hotel_recommendations",
+        description: "Get hotel recommendations for destinations",
+        schema: z.object({}),
+      }
+    );
+
+    const hotelAdvisorTools = [
+      getHotelRecommendations,
+      makeHandoffTool("travel_advisor"),
+    ];
+
+    const hotelAdvisor = createReactAgent({
+      llm: model,
+      tools: hotelAdvisorTools,
+      prompt: [
+        "You are a hotel expert that can provide hotel recommendations for a given destination. ",
+        "If you need help picking travel destinations, ask 'travel_advisor' for help.",
+        "You MUST include human-readable response before transferring to another agent."
+      ].join("")
+    });
+
+    const callHotelAdvisor = async (
+      state: z.infer<typeof MultiAgentState>
+    ): Promise<Command> => {
+      const response = await hotelAdvisor.invoke(state);
+      const update = { ...response, lastActiveAgent: "hotel_advisor" };
+      return new Command({ update, goto: "human" });
+    };
+
+    const humanNode = async (
+      state: z.infer<typeof MultiAgentState>
+    ): Promise<Command> => {
+      const userInput: string = interrupt("Ready for user input.");
+      const activeAgent = state.lastActiveAgent || "travel_advisor";
+
+      return new Command({
+        update: {
+          messages: [
+            {
+              role: "human",
+              content: userInput,
+            }
+          ]
+        },
+        goto: activeAgent,
+      });
+    };
+
+    const builder = new StateGraph(MultiAgentState)
+      .addNode("travel_advisor", callTravelAdvisor)
+      .addNode("hotel_advisor", callHotelAdvisor)
+      .addNode("human", humanNode)
+      .addEdge(START, "travel_advisor");
+
+    const checkpointer = new MemorySaver();
+    const graph = builder.compile({ checkpointer });
+    ```
+    
+    Let's test a multi turn conversation with this application.
+
+    ```typescript
+    import { v4 as uuidv4 } from "uuid";
+    import { Command } from "@langchain/langgraph";
+
+    const threadConfig = { configurable: { thread_id: uuidv4() } };
+
+    const inputs = [
+      // 1st round of conversation
+      {
+        messages: [
+          { role: "user", content: "i wanna go somewhere warm in the caribbean" }
+        ]
+      },
+      // Since we're using `interrupt`, we'll need to resume using the Command primitive.
+      // 2nd round of conversation
+      new Command({
+        resume: "could you recommend a nice hotel in one of the areas and tell me which area it is."
+      }),
+      // 3rd round of conversation
+      new Command({
+        resume: "i like the first one. could you recommend something to do near the hotel?"
+      }),
+    ];
+
+    for (const [idx, userInput] of inputs.entries()) {
+      console.log();
+      console.log(`--- Conversation Turn ${idx + 1} ---`);
+      console.log();
+      console.log(`User: ${JSON.stringify(userInput)}`);
+      console.log();
+      
+      for await (const update of await graph.stream(
+        userInput,
+        { ...threadConfig, streamMode: "updates" }
+      )) {
+        for (const [nodeId, value] of Object.entries(update)) {
+          if (value?.messages?.length) {
+            const lastMessage = value.messages.at(-1);
+            if (lastMessage?.getType?.() === "ai") {
+              console.log(`${nodeId}: ${lastMessage.content}`);
+            }
+          }
+        }
+      }
+    }
+    ```
+    
+    ```
+    --- Conversation Turn 1 ---
+    
+    User: {"messages":[{"role":"user","content":"i wanna go somewhere warm in the caribbean"}]}
+    
+    travel_advisor: Based on the recommendations, Aruba would be an excellent choice for your Caribbean getaway! Aruba is known as "One Happy Island" and offers:
+    - Year-round warm weather with consistent temperatures around 82°F (28°C)
+    - Beautiful white sand beaches like Eagle Beach and Palm Beach
+    - Clear turquoise waters perfect for swimming and snorkeling
+    - Minimal rainfall and location outside the hurricane belt
+    - A blend of Caribbean and Dutch culture
+    - Great dining options and nightlife
+    - Various water sports and activities
+    
+    Would you like me to get some specific hotel recommendations in Aruba for your stay? I can transfer you to our hotel advisor who can help with accommodations.
+    
+    --- Conversation Turn 2 ---
+    
+    User: Command { resume: 'could you recommend a nice hotel in one of the areas and tell me which area it is.' }
+    
+    hotel_advisor: Based on the recommendations, I can suggest two excellent options:
+    
+    1. The Ritz-Carlton, Aruba - Located in Palm Beach
+    - This luxury resort is situated in the vibrant Palm Beach area
+    - Known for its exceptional service and amenities
+    - Perfect if you want to be close to dining, shopping, and entertainment
+    - Features multiple restaurants, a casino, and a world-class spa
+    - Located on a pristine stretch of Palm Beach
+    
+    2. Bucuti & Tara Beach Resort - Located in Eagle Beach
+    - An adults-only boutique resort on Eagle Beach
+    - Known for being more intimate and peaceful
+    - Award-winning for its sustainability practices
+    - Perfect for a romantic getaway or peaceful vacation
+    - Located on one of the most beautiful beaches in the Caribbean
+    
+    Would you like more specific information about either of these properties or their locations?
+    
+    --- Conversation Turn 3 ---
+    
+    User: Command { resume: 'i like the first one. could you recommend something to do near the hotel?' }
+    
+    travel_advisor: Near the Ritz-Carlton in Palm Beach, here are some highly recommended activities:
+    
+    1. Visit the Palm Beach Plaza Mall - Just a short walk from the hotel, featuring shopping, dining, and entertainment
+    2. Try your luck at the Stellaris Casino - It's right in the Ritz-Carlton
+    3. Take a sunset sailing cruise - Many depart from the nearby pier
+    4. Visit the California Lighthouse - A scenic landmark just north of Palm Beach
+    5. Enjoy water sports at Palm Beach:
+       - Jet skiing
+       - Parasailing
+       - Snorkeling
+       - Stand-up paddleboarding
+    
+    Would you like more specific information about any of these activities or would you like to know about other options in the area?
+    ```
+    :::
 
 ## Prebuilt implementations
 
 LangGraph comes with prebuilt implementations of two of the most popular multi-agent architectures:
 
+:::python
 - [supervisor](../agents/multi-agent.md#supervisor) — individual agents are coordinated by a central supervisor agent. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. You can use [`langgraph-supervisor`](https://github.com/langchain-ai/langgraph-supervisor-py) library to create a supervisor multi-agent systems.
-- [swarm](../agents/multi-agent.md#supervisor) — agents dynamically hand off control to one another based on their specializations. The system remembers which agent was last active, ensuring that on subsequent interactions, the conversation resumes with that agent. You can use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-py) library to create a swarm multi-agent systems. 
+- [swarm](../agents/multi-agent.md#supervisor) — agents dynamically hand off control to one another based on their specializations. The system remembers which agent was last active, ensuring that on subsequent interactions, the conversation resumes with that agent. You can use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-py) library to create a swarm multi-agent systems.
+:::
+
+:::js
+- [supervisor](../agents/multi-agent.md#supervisor) — individual agents are coordinated by a central supervisor agent. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. You can use [`langgraph-supervisor`](https://github.com/langchain-ai/langgraph-supervisor-js) library to create a supervisor multi-agent systems.
+- [swarm](../agents/multi-agent.md#supervisor) — agents dynamically hand off control to one another based on their specializations. The system remembers which agent was last active, ensuring that on subsequent interactions, the conversation resumes with that agent. You can use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-js) library to create a swarm multi-agent systems.
+:::
+
+
+
+
+

docs/docs/how-tos/subgraph.md

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

docs/docs/how-tos/tool-calling.md

@@ -1141,12 +1141,12 @@ await agent.invoke(
 Short-term memory maintains **dynamic** state that changes during a single execution.
 
 :::python
-To **access** (read) the graph state inside the tools, you can use a special parameter **annotation** — @[`InjectedState`][InjectedState]:
+To **access** (read) the graph state inside the tools, you can use the reserved keyword parameter `state`:
 
 ```python
-from typing import Annotated, NotRequired
+from typing import NotRequired
 from langchain_core.tools import tool
-from langgraph.prebuilt import InjectedState, create_react_agent
+from langgraph.prebuilt import create_react_agent
 from langgraph.prebuilt.chat_agent_executor import AgentState
 
 class CustomState(AgentState):
@@ -1156,7 +1156,7 @@ class CustomState(AgentState):
 @tool
 def get_user_name(
     # highlight-next-line
-    state: Annotated[CustomState, InjectedState]
+    state  # Reserved keyword - automatically injected
 ) -> str:
     """Retrieve the current user-name from state."""
     # Return stored name or a default if not set
@@ -1173,6 +1173,9 @@ agent = create_react_agent(
 agent.invoke({"messages": "what's my name?"})
 ```
 
+!!! note "Migration from Annotations"
+    The old annotation-based approach using `Annotated[CustomState, InjectedState]` is deprecated but still supported for backward compatibility. Simply use the reserved keyword `state` without any type annotation for cleaner code.
+
 :::
 
 :::js
@@ -2387,3 +2390,4 @@ Some commonly used tool categories include:
 
 These integrations can be configured and added to your agents using the same `tools` parameter shown in the examples above.
 :::
+

docs/docs/tutorials/multi_agent/agent_supervisor.md

@@ -383,7 +383,6 @@ We will implement handoffs via **handoff tools** and give these tools to the sup
 ```python
 from typing import Annotated
 from langchain_core.tools import tool, InjectedToolCallId
-from langgraph.prebuilt import InjectedState
 from langgraph.graph import StateGraph, START, MessagesState
 from langgraph.types import Command
 
@@ -394,7 +393,7 @@ def create_handoff_tool(*, agent_name: str, description: str | None = None):
 
     @tool(name, description=description)
     def handoff_tool(
-        state: Annotated[MessagesState, InjectedState],
+        state,  # Reserved keyword - automatically injected
         tool_call_id: Annotated[str, InjectedToolCallId],
     ) -> Command:
         tool_message = {
@@ -644,7 +643,7 @@ def create_task_description_handoff_tool(
             "Description of what the next agent should do, including all of the relevant context.",
         ],
         # these parameters are ignored by the LLM
-        state: Annotated[MessagesState, InjectedState],
+        state,  # Reserved keyword - automatically injected
     ) -> Command:
         task_description_message = {"role": "user", "content": task_description}
         agent_input = {**state, "messages": [task_description_message]}
@@ -766,3 +765,5 @@ Update from subgraph research_agent:
 	
 	{"query": "2024 United States GDP value from a reputable source", "follow_up_questions": null, "answer": null, "images": [], "results": [{"url": "https://www.focus-economics.com/countries/united-states/", "title": "United States Economy Overview - Focus Economics", "content": "The United States' Macroeconomic Analysis:\n------------------------------------------\n\n**Nominal GDP of USD 29,185 billion in 2024.**\n\n**Nominal GDP of USD 29,179 billion in 2024.**\n\n**GDP per capita of USD 86,635 compared to the global average of USD 10,589.**\n\n**GDP per capita of USD 86,652 compared to the global average of USD 10,589.**\n\n**Average real GDP growth of 2.5% over the last decade.**\n\n**Average real GDP growth of ```
 ``` 
+
+

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-postgres/uv.lock

@@ -329,6 +329,7 @@ dev = [
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
     { name = "pytest-watcher" },
+    { name = "redis" },
     { name = "ruff" },
 ]
 

libs/checkpoint-sqlite/uv.lock

@@ -341,6 +341,7 @@ dev = [
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
     { name = "pytest-watcher" },
+    { name = "redis" },
     { name = "ruff" },
 ]
 

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

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

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

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

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

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

libs/checkpoint/pyproject.toml

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

libs/checkpoint/tests/test_redis_cache.py

@@ -0,0 +1,309 @@
+"""Unit tests for Redis cache implementation."""
+
+import time
+
+import pytest
+import redis
+
+from langgraph.cache.redis import RedisCache
+
+
+class TestRedisCache:
+    @pytest.fixture(autouse=True)
+    def setup(self):
+        """Set up test Redis client and cache."""
+        self.client = redis.Redis(
+            host="localhost", port=6379, db=0, decode_responses=False
+        )
+        try:
+            self.client.ping()
+        except redis.ConnectionError:
+            pytest.skip("Redis server not available")
+
+        self.cache = RedisCache(self.client, prefix="test:cache:")
+
+        # Clean up before each test
+        self.client.flushdb()
+
+    def teardown_method(self):
+        """Clean up after each test."""
+        try:
+            self.client.flushdb()
+        except Exception:
+            pass
+
+    def test_basic_set_and_get(self):
+        """Test basic set and get operations."""
+        keys = [(("graph", "node"), "key1")]
+        values = {keys[0]: ({"result": 42}, None)}
+
+        # Set value
+        self.cache.set(values)
+
+        # Get value
+        result = self.cache.get(keys)
+        assert len(result) == 1
+        assert result[keys[0]] == {"result": 42}
+
+    def test_batch_operations(self):
+        """Test batch set and get operations."""
+        keys = [
+            (("graph", "node1"), "key1"),
+            (("graph", "node2"), "key2"),
+            (("other", "node"), "key3"),
+        ]
+        values = {
+            keys[0]: ({"result": 1}, None),
+            keys[1]: ({"result": 2}, 60),  # With TTL
+            keys[2]: ({"result": 3}, None),
+        }
+
+        # Set values
+        self.cache.set(values)
+
+        # Get all values
+        result = self.cache.get(keys)
+        assert len(result) == 3
+        assert result[keys[0]] == {"result": 1}
+        assert result[keys[1]] == {"result": 2}
+        assert result[keys[2]] == {"result": 3}
+
+    def test_ttl_behavior(self):
+        """Test TTL (time-to-live) functionality."""
+        key = (("graph", "node"), "ttl_key")
+        values = {key: ({"data": "expires_soon"}, 1)}  # 1 second TTL
+
+        # Set with TTL
+        self.cache.set(values)
+
+        # Should be available immediately
+        result = self.cache.get([key])
+        assert len(result) == 1
+        assert result[key] == {"data": "expires_soon"}
+
+        # Wait for expiration
+        time.sleep(1.1)
+
+        # Should be expired
+        result = self.cache.get([key])
+        assert len(result) == 0
+
+    def test_namespace_isolation(self):
+        """Test that different namespaces are isolated."""
+        key1 = (("graph1", "node"), "same_key")
+        key2 = (("graph2", "node"), "same_key")
+
+        values = {key1: ({"graph": 1}, None), key2: ({"graph": 2}, None)}
+
+        self.cache.set(values)
+
+        result = self.cache.get([key1, key2])
+        assert result[key1] == {"graph": 1}
+        assert result[key2] == {"graph": 2}
+
+    def test_clear_all(self):
+        """Test clearing all cached values."""
+        keys = [(("graph", "node1"), "key1"), (("graph", "node2"), "key2")]
+        values = {keys[0]: ({"result": 1}, None), keys[1]: ({"result": 2}, None)}
+
+        self.cache.set(values)
+
+        # Verify data exists
+        result = self.cache.get(keys)
+        assert len(result) == 2
+
+        # Clear all
+        self.cache.clear()
+
+        # Verify data is gone
+        result = self.cache.get(keys)
+        assert len(result) == 0
+
+    def test_clear_by_namespace(self):
+        """Test clearing cached values by namespace."""
+        keys = [
+            (("graph1", "node"), "key1"),
+            (("graph2", "node"), "key2"),
+            (("graph1", "other"), "key3"),
+        ]
+        values = {
+            keys[0]: ({"result": 1}, None),
+            keys[1]: ({"result": 2}, None),
+            keys[2]: ({"result": 3}, None),
+        }
+
+        self.cache.set(values)
+
+        # Clear only graph1 namespace
+        self.cache.clear([("graph1", "node"), ("graph1", "other")])
+
+        # graph1 should be cleared, graph2 should remain
+        result = self.cache.get(keys)
+        assert len(result) == 1
+        assert result[keys[1]] == {"result": 2}
+
+    def test_empty_operations(self):
+        """Test behavior with empty keys/values."""
+        # Empty get
+        result = self.cache.get([])
+        assert result == {}
+
+        # Empty set
+        self.cache.set({})  # Should not raise error
+
+    def test_nonexistent_keys(self):
+        """Test getting keys that don't exist."""
+        keys = [(("graph", "node"), "nonexistent")]
+        result = self.cache.get(keys)
+        assert len(result) == 0
+
+    @pytest.mark.asyncio
+    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)
+        try:
+            client.ping()
+        except Exception:
+            pytest.skip("Redis not available")
+
+        cache = RedisCache(client, prefix="test:async:")
+
+        keys = [(("graph", "node"), "async_key")]
+        values = {keys[0]: ({"async": True}, None)}
+
+        # Async set (delegates to sync)
+        await cache.aset(values)
+
+        # Async get (delegates to sync)
+        result = await cache.aget(keys)
+        assert len(result) == 1
+        assert result[keys[0]] == {"async": True}
+
+        # Cleanup
+        client.flushdb()
+
+    @pytest.mark.asyncio
+    async def test_async_clear(self):
+        """Test async clear operations with sync Redis client."""
+        # Create sync Redis client and cache (like main integration tests)
+        client = redis.Redis(host="localhost", port=6379, db=1, decode_responses=False)
+        try:
+            client.ping()
+        except Exception:
+            pytest.skip("Redis not available")
+
+        cache = RedisCache(client, prefix="test:async:")
+
+        keys = [(("graph", "node"), "key")]
+        values = {keys[0]: ({"data": "test"}, None)}
+
+        await cache.aset(values)
+
+        # Verify data exists
+        result = await cache.aget(keys)
+        assert len(result) == 1
+
+        # Clear all (delegates to sync)
+        await cache.aclear()
+
+        # Verify data is gone
+        result = await cache.aget(keys)
+        assert len(result) == 0
+
+        # Cleanup
+        client.flushdb()
+
+    def test_redis_unavailable_get(self):
+        """Test behavior when Redis is unavailable during get operations."""
+        # Create cache with non-existent Redis server
+        bad_client = redis.Redis(
+            host="nonexistent", port=9999, socket_connect_timeout=0.1
+        )
+        cache = RedisCache(bad_client, prefix="test:cache:")
+
+        keys = [(("graph", "node"), "key")]
+        result = cache.get(keys)
+
+        # Should return empty dict when Redis unavailable
+        assert result == {}
+
+    def test_redis_unavailable_set(self):
+        """Test behavior when Redis is unavailable during set operations."""
+        # Create cache with non-existent Redis server
+        bad_client = redis.Redis(
+            host="nonexistent", port=9999, socket_connect_timeout=0.1
+        )
+        cache = RedisCache(bad_client, prefix="test:cache:")
+
+        keys = [(("graph", "node"), "key")]
+        values = {keys[0]: ({"data": "test"}, None)}
+
+        # Should not raise exception when Redis unavailable
+        cache.set(values)  # Should silently fail
+
+    @pytest.mark.asyncio
+    async def test_redis_unavailable_async(self):
+        """Test async behavior when Redis is unavailable."""
+        # Create sync cache with non-existent Redis server (like main integration tests)
+        bad_client = redis.Redis(
+            host="nonexistent", port=9999, socket_connect_timeout=0.1
+        )
+        cache = RedisCache(bad_client, prefix="test:cache:")
+
+        keys = [(("graph", "node"), "key")]
+        values = {keys[0]: ({"data": "test"}, None)}
+
+        # Should return empty dict for get (delegates to sync)
+        result = await cache.aget(keys)
+        assert result == {}
+
+        # Should not raise exception for set (delegates to sync)
+        await cache.aset(values)  # Should silently fail
+
+    def test_corrupted_data_handling(self):
+        """Test handling of corrupted data in Redis."""
+        # Set some valid data first
+        keys = [(("graph", "node"), "valid_key")]
+        values = {keys[0]: ({"data": "valid"}, None)}
+        self.cache.set(values)
+
+        # Manually insert corrupted data
+        corrupted_key = self.cache._make_key(("graph", "node"), "corrupted_key")
+        self.client.set(corrupted_key, b"invalid:data:format:too:many:colons")
+
+        # Should skip corrupted entry and return only valid ones
+        all_keys = [keys[0], (("graph", "node"), "corrupted_key")]
+        result = self.cache.get(all_keys)
+
+        assert len(result) == 1
+        assert result[keys[0]] == {"data": "valid"}
+
+    def test_key_parsing_edge_cases(self):
+        """Test key parsing with edge cases."""
+        # Test empty namespace
+        key1 = ((), "empty_ns")
+        values = {key1: ({"data": "empty_ns"}, None)}
+        self.cache.set(values)
+        result = self.cache.get([key1])
+        assert result[key1] == {"data": "empty_ns"}
+
+        # Test namespace with special characters
+        key2 = (("graph:with:colons", "node-with-dashes"), "key_with_underscores")
+        values = {key2: ({"data": "special_chars"}, None)}
+        self.cache.set(values)
+        result = self.cache.get([key2])
+        assert result[key2] == {"data": "special_chars"}
+
+    def test_large_data_serialization(self):
+        """Test handling of large data objects."""
+        # Create a large data structure
+        large_data = {"large_list": list(range(1000)), "nested": {"data": "x" * 1000}}
+        key = (("graph", "node"), "large_key")
+        values = {key: (large_data, None)}
+
+        self.cache.set(values)
+        result = self.cache.get([key])
+
+        assert len(result) == 1
+        assert result[key] == large_data

libs/checkpoint/tests/test_store.py

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

libs/checkpoint/uv.lock

@@ -32,6 +32,15 @@ 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" },
 ]
 
+[[package]]
+name = "async-timeout"
+version = "5.0.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a5/ae/136395dfbfe00dfc94da3f3e136d0b13f394cba8f4841120e34226265780/async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3", size = 9274, upload-time = "2024-11-06T16:41:39.6Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fe/ba/e2081de779ca30d473f21f5b30e0e737c438205440784c7dfc81efc2b029/async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c", size = 6233, upload-time = "2024-11-06T16:41:37.9Z" },
+]
+
 [[package]]
 name = "certifi"
 version = "2025.7.9"
@@ -345,6 +354,7 @@ dev = [
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
     { name = "pytest-watcher" },
+    { name = "redis" },
     { name = "ruff" },
 ]
 
@@ -366,6 +376,7 @@ dev = [
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
     { name = "pytest-watcher" },
+    { name = "redis" },
     { name = "ruff" },
 ]
 
@@ -1153,6 +1164,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/19/87/5124b1c1f2412bb95c59ec481eaf936cd32f0fe2a7b16b97b81c4c017a6a/PyYAML-6.0.2-cp39-cp39-win_amd64.whl", hash = "sha256:39693e1f8320ae4f43943590b49779ffb98acb81f788220ea932a6b6c51004d8", size = 162312, upload-time = "2024-08-06T20:33:49.073Z" },
 ]
 
+[[package]]
+name = "redis"
+version = "6.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "async-timeout", marker = "python_full_version < '3.11.3'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/21/cd/030274634a1a052b708756016283ea3d84e91ae45f74d7f5dcf55d753a0f/redis-6.3.0.tar.gz", hash = "sha256:3000dbe532babfb0999cdab7b3e5744bcb23e51923febcfaeb52c8cfb29632ef", size = 4647275, upload-time = "2025-08-05T08:12:31.648Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/df/a7/2fe45801534a187543fc45d28b3844d84559c1589255bc2ece30d92dc205/redis-6.3.0-py3-none-any.whl", hash = "sha256:92f079d656ded871535e099080f70fab8e75273c0236797126ac60242d638e9b", size = 280018, upload-time = "2025-08-05T08:12:30.093Z" },
+]
+
 [[package]]
 name = "requests"
 version = "2.32.4"

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__ = "0.3.8"

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."""
 
@@ -369,6 +372,13 @@ 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 +388,11 @@ 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 +598,28 @@ 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 +634,10 @@ 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 +674,17 @@ 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 +1495,7 @@ 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 +1504,6 @@ 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 +1516,8 @@ 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>=0.2.120,<0.4.0 ; python_version >= '3.11'",
+    "langgraph-runtime-inmem>=0.6.8 ; 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": [

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": [

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'" },
 ]
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 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]]
@@ -53,11 +53,11 @@ wheels = [
 
 [[package]]
 name = "certifi"
-version = "2025.7.14"
+version = "2025.8.3"
 source = { registry = "https://pypi.org/simple" }
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 wheels = [
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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" },
 ]
 
 [[package]]
@@ -131,76 +131,77 @@ wheels = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.2"
+version = "3.4.3"
 source = { registry = "https://pypi.org/simple" }
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 ]

libs/langgraph/Makefile

@@ -37,11 +37,11 @@ coverage:
 		--cov-report xml \
 		--cov-report term-missing:skip-covered
 
-start-postgres:
-	docker compose -f tests/compose-postgres.yml up -V --force-recreate --wait --remove-orphans
+start-services:
+	docker compose -f tests/compose-postgres.yml -f tests/compose-redis.yml up -V --force-recreate --wait --remove-orphans
 
-stop-postgres:
-	docker compose -f tests/compose-postgres.yml down -v
+stop-services:
+	docker compose -f tests/compose-postgres.yml -f tests/compose-redis.yml down -v
 
 start-dev-server:
 	LOG_LEVEL=warning uv run langgraph dev --config tests/example_app/langgraph.json --no-browser & echo "$$!" > .devserver.pid
@@ -60,11 +60,11 @@ NO_DOCKER ?= $(sh command -v docker >/dev/null 2>&1 && echo "false" || echo "tru
 
 test:
 	if [ "$(NO_DOCKER)" = "false" ]; then \
-		make start-postgres &&\
+		make start-services &&\
 		make start-dev-server &&\
 		uv run pytest $(TEST); \
 		EXIT_CODE=$$?; \
-		make stop-postgres; \
+		make stop-services; \
 		make stop-dev-server; \
 		exit $$EXIT_CODE; \
 	else \
@@ -74,11 +74,11 @@ test:
 	fi
 
 test_parallel:
-	make start-postgres &&\
+	make start-services &&\
 	make start-dev-server &&\
 	uv run pytest -n auto --dist worksteal $(TEST); \
 	EXIT_CODE=$$?; \
-	make stop-postgres; \
+	make stop-services; \
 	make stop-dev-server; \
 	exit $$EXIT_CODE
 
@@ -93,11 +93,11 @@ MAXFAIL_ARGS := $(if $(MAXFAIL),--maxfail $(MAXFAIL),)
 XDIST_ARGS := $(if $(WORKERS),-x $(XDIST_ARGS),)
 
 test_watch:
-	make start-postgres &&\
+	make start-services &&\
 	make start-dev-server &&\
 	uv run ptw . -- --ff -vv $(XDIST_ARGS) $(MAXFAIL_ARGS) $(TEST); \
 	EXIT_CODE=$$?; \
-	make stop-postgres; \
+	make stop-services; \
 	make stop-dev-server; \
 	exit $$EXIT_CODE
 

libs/langgraph/langgraph/graph/_branch.py

@@ -41,16 +41,16 @@ _Writer = Callable[
 
 
 def _get_branch_path_input_schema(
-    path: Callable[..., Hashable | list[Hashable]]
-    | Callable[..., Awaitable[Hashable | list[Hashable]]]
-    | Runnable[Any, Hashable | list[Hashable]],
+    path: Callable[..., Hashable | Sequence[Hashable]]
+    | Callable[..., Awaitable[Hashable | Sequence[Hashable]]]
+    | Runnable[Any, Hashable | Sequence[Hashable]],
 ) -> type[Any] | None:
     input = None
     # detect input schema annotation in the branch callable
     try:
         callable_: (
-            Callable[..., Hashable | list[Hashable]]
-            | Callable[..., Awaitable[Hashable | list[Hashable]]]
+            Callable[..., Hashable | Sequence[Hashable]]
+            | Callable[..., Awaitable[Hashable | Sequence[Hashable]]]
             | None
         ) = None
         if isinstance(path, (RunnableCallable, RunnableLambda)):

libs/langgraph/langgraph/graph/message.py

@@ -22,10 +22,11 @@ from langchain_core.messages import (
     convert_to_messages,
     message_chunk_to_message,
 )
-from typing_extensions import TypedDict
+from typing_extensions import TypedDict, deprecated
 
 from langgraph._internal._constants import CONF, CONFIG_KEY_SEND, NS_SEP
 from langgraph.graph.state import StateGraph
+from langgraph.warnings import LangGraphDeprecatedSinceV10
 
 __all__ = (
     "add_messages",
@@ -233,9 +234,16 @@ def add_messages(
     return merged
 
 
+@deprecated(
+    "MessageGraph is deprecated in LangGraph v1.0.0, to be removed in v2.0.0. Please use StateGraph with a `messages` key instead.",
+    category=None,
+)
 class MessageGraph(StateGraph):
     """A StateGraph where every node receives a list of messages as input and returns one or more messages as output.
 
+    !!! warning "Deprecation"
+        MessageGraph is deprecated in LangGraph v1.0.0, to be removed in v2.0.0. Please use StateGraph with a `messages` key instead.
+
     MessageGraph is a subclass of StateGraph whose entire state is a single, append-only* list of messages.
     Each node in a MessageGraph takes a list of messages as input and returns zero or more
     messages as output. The `add_messages` function is used to merge the output messages from each node
@@ -281,6 +289,11 @@ class MessageGraph(StateGraph):
     """
 
     def __init__(self) -> None:
+        warnings.warn(
+            "MessageGraph is deprecated in LangGraph v1.0.0, to be removed in v2.0.0. Please use StateGraph with a `messages` key instead.",
+            category=LangGraphDeprecatedSinceV10,
+            stacklevel=2,
+        )
         super().__init__(Annotated[list[AnyMessage], add_messages])  # type: ignore[arg-type]
 
 

libs/langgraph/langgraph/graph/state.py

@@ -607,9 +607,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
     def add_conditional_edges(
         self,
         source: str,
-        path: Callable[..., Hashable | list[Hashable]]
-        | Callable[..., Awaitable[Hashable | list[Hashable]]]
-        | Runnable[Any, Hashable | list[Hashable]],
+        path: Callable[..., Hashable | Sequence[Hashable]]
+        | Callable[..., Awaitable[Hashable | Sequence[Hashable]]]
+        | Runnable[Any, Hashable | Sequence[Hashable]],
         path_map: dict[Hashable, str] | list[str] | None = None,
     ) -> Self:
         """Add a conditional edge from the starting node to any number of destination nodes.
@@ -710,9 +710,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
 
     def set_conditional_entry_point(
         self,
-        path: Callable[..., Hashable | list[Hashable]]
-        | Callable[..., Awaitable[Hashable | list[Hashable]]]
-        | Runnable[Any, Hashable | list[Hashable]],
+        path: Callable[..., Hashable | Sequence[Hashable]]
+        | Callable[..., Awaitable[Hashable | Sequence[Hashable]]]
+        | Runnable[Any, Hashable | Sequence[Hashable]],
         path_map: dict[Hashable, str] | list[str] | None = None,
     ) -> Self:
         """Sets a conditional entry point in the graph.

libs/langgraph/langgraph/pregel/_checkpoint.py

@@ -29,6 +29,7 @@ def create_checkpoint(
     step: int,
     *,
     id: str | None = None,
+    updated_channels: set[str] | None = None,
 ) -> Checkpoint:
     """Create a checkpoint for the given channels."""
     ts = datetime.now(timezone.utc).isoformat()
@@ -49,6 +50,7 @@ def create_checkpoint(
         channel_values=values,
         channel_versions=checkpoint["channel_versions"],
         versions_seen=checkpoint["versions_seen"],
+        updated_channels=None if updated_channels is None else sorted(updated_channels),
     )
 
 
@@ -81,4 +83,5 @@ def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
         channel_values=checkpoint["channel_values"].copy(),
         channel_versions=checkpoint["channel_versions"].copy(),
         versions_seen={k: v.copy() for k, v in checkpoint["versions_seen"].items()},
+        updated_channels=checkpoint.get("updated_channels", None),
     )

libs/langgraph/langgraph/pregel/_loop.py

@@ -568,7 +568,9 @@ class PregelLoop:
             if task := tasks.get(tid):
                 task.writes.append((k, v))
 
-    def _first(self, *, input_keys: str | Sequence[str]) -> set[str] | None:
+    def _first(
+        self, *, input_keys: str | Sequence[str], updated_channels: set[str] | None
+    ) -> set[str] | None:
         # resuming from previous checkpoint requires
         # - finding a previous checkpoint
         # - receiving None input (outer graph) or RESUMING flag (subgraph)
@@ -585,8 +587,6 @@ class PregelLoop:
                 ),
             )
         )
-        # this can be set only when there are input_writes
-        updated_channels: set[str] | None = None
 
         # map command to writes
         if isinstance(self.input, Command):
@@ -614,13 +614,15 @@ class PregelLoop:
         if null_writes := [
             w[1:] for w in self.checkpoint_pending_writes if w[0] == NULL_TASK_ID
         ]:
-            apply_writes(
+            null_updated_channels = apply_writes(
                 self.checkpoint,
                 self.channels,
                 [PregelTaskWrites((), INPUT, null_writes, [])],
                 self.checkpointer_get_next_version,
                 self.trigger_to_nodes,
             )
+            if updated_channels is not None:
+                updated_channels.update(null_updated_channels)
         # proceed past previous checkpoint
         if is_resuming:
             self.checkpoint["versions_seen"].setdefault(INTERRUPT, {})
@@ -648,6 +650,7 @@ class PregelLoop:
                 store=None,
                 checkpointer=None,
                 manager=None,
+                updated_channels=updated_channels,
             )
             # apply input writes
             updated_channels = apply_writes(
@@ -661,6 +664,7 @@ class PregelLoop:
                 self.trigger_to_nodes,
             )
             # save input checkpoint
+            self.updated_channels = updated_channels
             self._put_checkpoint({"source": "input"})
         elif CONFIG_KEY_RESUMING not in configurable:
             raise EmptyInputError(f"Received no input for {input_keys}")
@@ -693,6 +697,7 @@ class PregelLoop:
             self.channels if do_checkpoint else None,
             self.step,
             id=self.checkpoint["id"] if exiting else None,
+            updated_channels=self.updated_channels,
         )
         # bail if no checkpointer
         if do_checkpoint and self._checkpointer_put_after_previous is not None:
@@ -1036,7 +1041,12 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
         self.step = self.checkpoint_metadata["step"] + 1
         self.stop = self.step + self.config["recursion_limit"] + 1
         self.checkpoint_previous_versions = self.checkpoint["channel_versions"].copy()
-        self.updated_channels = self._first(input_keys=self.input_keys)
+        self.updated_channels = self._first(
+            input_keys=self.input_keys,
+            updated_channels=set(self.checkpoint.get("updated_channels"))  # type: ignore[arg-type]
+            if self.checkpoint.get("updated_channels")
+            else None,
+        )
 
         return self
 
@@ -1212,7 +1222,12 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
         self.step = self.checkpoint_metadata["step"] + 1
         self.stop = self.step + self.config["recursion_limit"] + 1
         self.checkpoint_previous_versions = self.checkpoint["channel_versions"].copy()
-        self.updated_channels = self._first(input_keys=self.input_keys)
+        self.updated_channels = self._first(
+            input_keys=self.input_keys,
+            updated_channels=set(self.checkpoint.get("updated_channels"))  # type: ignore[arg-type]
+            if self.checkpoint.get("updated_channels")
+            else None,
+        )
 
         return self
 

libs/langgraph/langgraph/pregel/_messages.py

@@ -29,16 +29,51 @@ Meta = tuple[tuple[str, ...], dict[str, Any]]
 
 class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
     """A callback handler that implements stream_mode=messages.
-    Collects messages from (1) chat model stream events and (2) node outputs."""
+
+    Collects messages from:
+    (1) chat model stream events; and
+    (2) node outputs.
+    """
 
     run_inline = True
-    """We want this callback to run in the main thread, to avoid order/locking issues."""
+    """We want this callback to run in the main thread to avoid order/locking issues."""
 
-    def __init__(self, stream: Callable[[StreamChunk], None], subgraphs: bool):
+    def __init__(
+        self,
+        stream: Callable[[StreamChunk], None],
+        subgraphs: bool,
+        *,
+        parent_ns: tuple[str, ...] | None = None,
+    ) -> None:
+        """Configure the handler to stream messages from LLMs and nodes.
+
+        Args:
+            stream: A callable that takes a StreamChunk and emits it.
+            subgraphs: Whether to emit messages from subgraphs.
+            parent_ns: The namespace where the handler was created.
+                We keep track of this namespace to allow calls to subgraphs that
+                were explicitly requested as a stream with `messages` mode
+                configured.
+
+        Example:
+            parent_ns is used to handle scenarios where the subgraph is explicitly
+            streamed with `stream_mode="messages"`.
+
+            ```python
+            def parent_graph_node():
+                # This node is in the parent graph.
+                async for event in some_subgraph(..., stream_mode="messages"):
+                    do something with event # <-- these events will be emitted
+                return ...
+
+            parent_graph.invoke(subgraphs=False)
+            ```
+        """
         self.stream = stream
         self.subgraphs = subgraphs
         self.metadata: dict[UUID, Meta] = {}
         self.seen: set[int | str] = set()
+        self.parent_ns = parent_ns
 
     def _emit(self, meta: Meta, message: BaseMessage, *, dedupe: bool = False) -> None:
         if dedupe and message.id in self.seen:
@@ -100,7 +135,7 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
             ns = tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP))[
                 :-1
             ]
-            if not self.subgraphs and len(ns) > 0:
+            if not self.subgraphs and len(ns) > 0 and ns != self.parent_ns:
                 return
             if tags:
                 if filtered_tags := [t for t in tags if not t.startswith("seq:step")]:

libs/langgraph/langgraph/pregel/main.py

@@ -11,7 +11,7 @@ from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
 from dataclasses import is_dataclass
 from functools import partial
 from inspect import isclass
-from typing import Any, Callable, Generic, Union, cast, get_type_hints
+from typing import Any, Callable, Generic, Optional, Union, cast, get_type_hints
 from uuid import UUID, uuid5
 
 from langchain_core.globals import get_debug
@@ -2534,8 +2534,13 @@ class Pregel(
                 config[CONF][CONFIG_KEY_CHECKPOINT_NS] = recast_checkpoint_ns(ns)
             # set up messages stream mode
             if "messages" in stream_modes:
+                ns_ = cast(Optional[str], config[CONF].get(CONFIG_KEY_CHECKPOINT_NS))
                 run_manager.inheritable_handlers.append(
-                    StreamMessagesHandler(stream.put, subgraphs)
+                    StreamMessagesHandler(
+                        stream.put,
+                        subgraphs,
+                        parent_ns=tuple(ns_.split(NS_SEP)) if ns_ else None,
+                    )
                 )
 
             # set up custom stream mode
@@ -2814,8 +2819,14 @@ class Pregel(
                 config[CONF][CONFIG_KEY_CHECKPOINT_NS] = recast_checkpoint_ns(ns)
             # set up messages stream mode
             if "messages" in stream_modes:
+                # namespace can be None in a root level graph?
+                ns_ = cast(Optional[str], config[CONF].get(CONFIG_KEY_CHECKPOINT_NS))
                 run_manager.inheritable_handlers.append(
-                    StreamMessagesHandler(stream_put, subgraphs)
+                    StreamMessagesHandler(
+                        stream_put,
+                        subgraphs,
+                        parent_ns=tuple(ns_.split(NS_SEP)) if ns_ else None,
+                    )
                 )
 
             # set up custom stream mode

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 = "0.6.3"
+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",
@@ -49,6 +49,7 @@ dev = [
     "types-requests",
     "pycryptodome",
     "langgraph-cli[inmem]",
+    "redis",
 ]
 
 [tool.uv]

libs/langgraph/tests/compose-redis.yml

@@ -0,0 +1,16 @@
+name: langgraph-tests
+services:
+  redis-test:
+    image: redis:7-alpine
+    ports:
+      - "6379:6379"
+    command: redis-server --maxmemory 256mb --maxmemory-policy allkeys-lru
+    healthcheck:
+      test: redis-cli ping
+      start_period: 10s
+      timeout: 1s
+      retries: 5
+      interval: 5s
+      start_interval: 1s
+    tmpfs:
+      - /data  # Use tmpfs for faster testing

libs/langgraph/tests/conftest.py

@@ -3,10 +3,12 @@ from collections.abc import AsyncIterator, Iterator
 from uuid import UUID
 
 import pytest
+import redis
 from pytest_mock import MockerFixture
 
 from langgraph.cache.base import BaseCache
 from langgraph.cache.memory import InMemoryCache
+from langgraph.cache.redis import RedisCache
 from langgraph.cache.sqlite import SqliteCache
 from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.store.base import BaseStore
@@ -55,12 +57,34 @@ def durability(request: pytest.FixtureRequest) -> Durability:
     return request.param
 
 
-@pytest.fixture(scope="function", params=["sqlite", "memory"])
+@pytest.fixture(
+    scope="function",
+    params=["sqlite", "memory"] if NO_DOCKER else ["sqlite", "memory", "redis"],
+)
 def cache(request: pytest.FixtureRequest) -> Iterator[BaseCache]:
     if request.param == "sqlite":
         yield SqliteCache(path=":memory:")
     elif request.param == "memory":
         yield InMemoryCache()
+    elif request.param == "redis":
+        # Get worker ID for parallel test isolation
+        worker_id = getattr(request.config, "workerinput", {}).get("workerid", "master")
+
+        redis_client = redis.Redis(
+            host="localhost", port=6379, db=0, decode_responses=False
+        )
+        # Use worker-specific prefix to avoid cache pollution between parallel tests
+        cache = RedisCache(redis_client, prefix=f"test:cache:{worker_id}:")
+        yield cache
+
+        try:
+            # Only clear keys with our specific prefix
+            pattern = f"test:cache:{worker_id}:*"
+            keys = redis_client.keys(pattern)
+            if keys:
+                redis_client.delete(*keys)
+        except Exception:
+            pass
     else:
         raise ValueError(f"Unknown cache type: {request.param}")
 

libs/langgraph/tests/test_checkpoint_migration.py

@@ -330,6 +330,7 @@ SAVED_CHECKPOINTS = {
                     "docs": ["doc1", "doc2", "doc3", "doc4"],
                     "answer": "doc1,doc2,doc3,doc4",
                 },
+                "updated_channels": None,
             },
             metadata={
                 "source": "loop",
@@ -390,6 +391,7 @@ SAVED_CHECKPOINTS = {
                     "docs": ["doc1", "doc2", "doc3", "doc4"],
                     "branch:to:qa": None,
                 },
+                "updated_channels": None,
             },
             metadata={
                 "source": "loop",
@@ -465,6 +467,7 @@ SAVED_CHECKPOINTS = {
                     "branch:to:retriever_one": None,
                     "docs": ["doc3", "doc4"],
                 },
+                "updated_channels": None,
             },
             metadata={
                 "source": "loop",
@@ -516,6 +519,7 @@ SAVED_CHECKPOINTS = {
                     "branch:to:analyzer_one": None,
                     "branch:to:retriever_two": None,
                 },
+                "updated_channels": None,
             },
             metadata={
                 "source": "loop",
@@ -570,6 +574,7 @@ SAVED_CHECKPOINTS = {
                     "query": "what is weather in sf",
                     "branch:to:rewrite_query": None,
                 },
+                "updated_channels": None,
             },
             metadata={
                 "source": "loop",
@@ -618,6 +623,7 @@ SAVED_CHECKPOINTS = {
                 },
                 "versions_seen": {"__input__": {}},
                 "channel_values": {"__start__": {"query": "what is weather in sf"}},
+                "updated_channels": None,
             },
             metadata={
                 "source": "input",

libs/langgraph/tests/test_deprecation.py

@@ -12,6 +12,7 @@ from langgraph.channels.last_value import LastValue
 from langgraph.errors import NodeInterrupt
 from langgraph.func import entrypoint, task
 from langgraph.graph import StateGraph
+from langgraph.graph.message import MessageGraph
 from langgraph.pregel import NodeBuilder, Pregel
 from langgraph.types import Interrupt, RetryPolicy
 from langgraph.warnings import LangGraphDeprecatedSinceV05, LangGraphDeprecatedSinceV10
@@ -332,3 +333,11 @@ def test_config_parameter_incorrect_typing() -> None:
 
         builder.add_node(async_node_with_untyped_config)
         assert len(w) == 0
+
+
+def test_message_graph_deprecation() -> None:
+    with pytest.warns(
+        LangGraphDeprecatedSinceV10,
+        match="MessageGraph is deprecated in LangGraph v1.0.0, to be removed in v2.0.0. Please use StateGraph with a `messages` key instead.",
+    ):
+        MessageGraph()

libs/langgraph/tests/test_interruption.py

@@ -1,12 +1,9 @@
-import operator
-from typing import Annotated
-
 import pytest
 from typing_extensions import TypedDict
 
 from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.graph import END, START, StateGraph
-from langgraph.types import Command, Durability, Send, interrupt
+from langgraph.types import Durability
 
 pytestmark = pytest.mark.anyio
 
@@ -93,162 +90,3 @@ async def test_interruption_without_state_updates_async(
     assert (await graph.aget_state(thread)).next == ()
     n_checkpoints = len([c async for c in graph.aget_state_history(thread)])
     assert n_checkpoints == (5 if durability != "exit" else 3)
-
-
-def test_interrupt_with_send_payloads(sync_checkpointer: BaseCheckpointSaver) -> None:
-    """Test interruption in map node with Send payloads and human-in-the-loop resume."""
-
-    # Global counter to track node executions
-    node_counter = {"entry": 0, "map_node": 0}
-
-    class State(TypedDict):
-        items: list[str]
-        processed: Annotated[list[str], operator.add]
-
-    def entry_node(state: State):
-        node_counter["entry"] += 1
-        return {}  # No state updates in entry node
-
-    def send_to_map(state: State):
-        return [Send("map_node", {"item": item}) for item in state["items"]]
-
-    def map_node(state: State):
-        node_counter["map_node"] += 1
-        if "dangerous" in state["item"]:
-            value = interrupt({"processing": state["item"]})
-            return {"processed": [f"processed_{value}"]}
-        else:
-            return {"processed": [f"processed_{state['item']}_auto"]}
-
-    builder = StateGraph(State)
-    builder.add_node("entry", entry_node)
-    builder.add_node("map_node", map_node)
-    builder.add_edge(START, "entry")
-    builder.add_conditional_edges("entry", send_to_map, ["map_node"])
-    builder.add_edge("map_node", END)
-
-    graph = builder.compile(checkpointer=sync_checkpointer)
-
-    config = {"configurable": {"thread_id": "test_interrupt_send"}}
-
-    # Run until interrupts
-    result = graph.invoke(
-        {"items": ["item1", "dangerous_item1", "dangerous_item2"]}, config=config
-    )
-
-    # Verify we have interrupts (only one for dangerous_item)
-    interrupts = result.get("__interrupt__", [])
-    assert len(interrupts) == 2
-    assert "dangerous_item" in interrupts[0].value["processing"]
-
-    # Resume with mapping of interrupt IDs to values
-    resume_map = {
-        i.interrupt_id: f"human_input_{i.value['processing']}" for i in interrupts
-    }
-
-    final_result = graph.invoke(Command(resume=resume_map), config=config)
-
-    # Verify final result contains processed items
-    assert "processed" in final_result
-    processed_items = final_result["processed"]
-    assert len(processed_items) == 3
-    assert "processed_item1_auto" in processed_items  # item1 processed automatically
-    assert any(
-        "processed_human_input_dangerous_item1" in item for item in processed_items
-    )  # dangerous_item1 processed after interrupt
-    assert any(
-        "processed_human_input_dangerous_item2" in item for item in processed_items
-    )  # dangerous_item2 processed after interrupt
-
-    # Verify node execution counts
-    assert node_counter["entry"] == 1  # Entry node runs once
-    # Map node runs 3 times initially (item1 completes, 2 dangerous_items interrupt),
-    # then 2 times on resume
-    assert node_counter["map_node"] == 5
-
-
-def test_interrupt_with_send_payloads_sequential_resume(
-    sync_checkpointer: BaseCheckpointSaver,
-) -> None:
-    """Test interruption in map node with Send payloads and sequential resume."""
-
-    # Global counter to track node executions
-    node_counter = {"entry": 0, "map_node": 0}
-
-    class State(TypedDict):
-        items: list[str]
-        processed: Annotated[list[str], operator.add]
-
-    def entry_node(state: State):
-        node_counter["entry"] += 1
-        return {}  # No state updates in entry node
-
-    def send_to_map(state: State):
-        return [Send("map_node", {"item": item}) for item in state["items"]]
-
-    def map_node(state: State):
-        node_counter["map_node"] += 1
-        if "dangerous" in state["item"]:
-            value = interrupt({"processing": state["item"]})
-            return {"processed": [f"processed_{value}"]}
-        else:
-            return {"processed": [f"processed_{state['item']}_auto"]}
-
-    builder = StateGraph(State)
-    builder.add_node("entry", entry_node)
-    builder.add_node("map_node", map_node)
-    builder.add_edge(START, "entry")
-    builder.add_conditional_edges("entry", send_to_map, ["map_node"])
-    builder.add_edge("map_node", END)
-
-    graph = builder.compile(checkpointer=sync_checkpointer)
-
-    config = {"configurable": {"thread_id": "test_interrupt_send_sequential"}}
-
-    # Run until interrupts
-    result = graph.invoke(
-        {"items": ["item1", "dangerous_item1", "dangerous_item2"]}, config=config
-    )
-
-    # Verify we have interrupts
-    interrupts = result.get("__interrupt__", [])
-    assert len(interrupts) == 2
-    assert "dangerous_item" in interrupts[0].value["processing"]
-
-    # Resume first interrupt only
-    first_interrupt = interrupts[0]
-    first_resume_map = {
-        first_interrupt.interrupt_id: f"human_input_{first_interrupt.value['processing']}"
-    }
-
-    partial_result = graph.invoke(Command(resume=first_resume_map), config=config)
-
-    # Verify we still have one pending interrupt
-    remaining_interrupts = partial_result.get("__interrupt__", [])
-    assert len(remaining_interrupts) == 1
-
-    # Resume second interrupt
-    second_interrupt = remaining_interrupts[0]
-    second_resume_map = {
-        second_interrupt.interrupt_id: f"human_input_{second_interrupt.value['processing']}"
-    }
-
-    final_result = graph.invoke(Command(resume=second_resume_map), config=config)
-
-    # Verify final result contains processed items
-    assert "processed" in final_result
-    processed_items = final_result["processed"]
-    assert len(processed_items) == 3
-    assert "processed_item1_auto" in processed_items  # item1 processed automatically
-    assert any(
-        "processed_human_input_dangerous_item1" in item for item in processed_items
-    )  # dangerous_item1 processed after interrupt
-    assert any(
-        "processed_human_input_dangerous_item2" in item for item in processed_items
-    )  # dangerous_item2 processed after interrupt
-
-    # Verify node execution counts
-    assert node_counter["entry"] == 1  # Entry node runs once
-    # Map node runs 3 times initially (item1 completes, 2 dangerous_items interrupt),
-    # then 1 time on first resume, then 1 time on second resume
-    assert node_counter["map_node"] == 5

libs/langgraph/tests/test_large_cases.py

@@ -6,7 +6,9 @@ from dataclasses import replace
 from typing import Annotated, Any, Literal, Optional, Union, cast
 
 import pytest
+from langchain_core.messages import AIMessage, AnyMessage, ToolCall
 from langchain_core.runnables import RunnableConfig, RunnableMap, RunnablePick
+from langchain_core.tools import tool
 from pytest_mock import MockerFixture
 from syrupy import SnapshotAssertion
 from typing_extensions import TypedDict
@@ -18,7 +20,7 @@ from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.constants import END, START
 from langgraph.graph import StateGraph
-from langgraph.graph.message import MessageGraph, MessagesState, add_messages
+from langgraph.graph.message import MessagesState, add_messages
 from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.pregel import NodeBuilder, Pregel
@@ -2441,7 +2443,7 @@ def test_message_graph(
             return "continue"
 
     # Define a new graph
-    workflow = MessageGraph()
+    workflow = StateGraph(state_schema=Annotated[list[AnyMessage], add_messages])  # type: ignore[arg-type]
 
     # Define the two nodes we will cycle between
     workflow.add_node("agent", model)
@@ -2487,7 +2489,7 @@ def test_message_graph(
         assert json.dumps(app.get_graph().to_json(), indent=2) == snapshot
         assert app.get_graph().draw_mermaid(with_styles=False) == snapshot
 
-    assert app.invoke(HumanMessage(content="what is weather in sf")) == [
+    assert app.invoke([HumanMessage(content="what is weather in sf")]) == [
         _AnyIdHumanMessage(
             content="what is weather in sf",
         ),
@@ -6435,10 +6437,6 @@ def test_weather_subgraph(
     from langchain_core.language_models.fake_chat_models import (
         FakeMessagesListChatModel,
     )
-    from langchain_core.messages import AIMessage, ToolCall
-    from langchain_core.tools import tool
-
-    from langgraph.graph import MessagesState
 
     # setup subgraph
 

libs/langgraph/tests/test_large_cases_async.py

@@ -11,7 +11,7 @@ from typing import (
 )
 
 import pytest
-from langchain_core.messages import ToolCall
+from langchain_core.messages import AnyMessage, ToolCall
 from langchain_core.runnables import RunnableConfig, RunnablePick
 from pytest_mock import MockerFixture
 from typing_extensions import TypedDict
@@ -21,7 +21,7 @@ from langgraph.channels.last_value import LastValue
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.constants import END, START
-from langgraph.graph.message import MessageGraph, add_messages
+from langgraph.graph.message import add_messages
 from langgraph.graph.state import StateGraph
 from langgraph.prebuilt.chat_agent_executor import create_react_agent
 from langgraph.prebuilt.tool_node import ToolNode
@@ -2117,7 +2117,7 @@ async def test_message_graph(async_checkpointer: BaseCheckpointSaver) -> None:
             return "continue"
 
     # Define a new graph
-    workflow = MessageGraph()
+    workflow = StateGraph(state_schema=Annotated[list[AnyMessage], add_messages])  # type: ignore[arg-type]
 
     # Define the two nodes we will cycle between
     workflow.add_node("agent", model)
@@ -2157,7 +2157,7 @@ async def test_message_graph(async_checkpointer: BaseCheckpointSaver) -> None:
     # meaning you can use it as you would any other runnable
     app = workflow.compile()
 
-    assert await app.ainvoke(HumanMessage(content="what is weather in sf")) == [
+    assert await app.ainvoke([HumanMessage(content="what is weather in sf")]) == [
         _AnyIdHumanMessage(
             content="what is weather in sf",
         ),

libs/langgraph/tests/test_pregel.py

@@ -16,6 +16,7 @@ from typing import Annotated, Any, Literal, Optional, Union, get_type_hints
 
 import pytest
 from langchain_core.language_models import GenericFakeChatModel
+from langchain_core.messages import AnyMessage
 from langchain_core.runnables import (
     RunnableConfig,
     RunnableLambda,
@@ -26,7 +27,7 @@ from langsmith import traceable
 from pydantic import BaseModel, ConfigDict, Field, ValidationError
 from pytest_mock import MockerFixture
 from syrupy import SnapshotAssertion
-from typing_extensions import TypedDict
+from typing_extensions import NotRequired, TypedDict
 
 from langgraph._internal._constants import CONFIG_KEY_NODE_FINISHED, ERROR, PULL
 from langgraph.cache.base import BaseCache
@@ -45,7 +46,7 @@ from langgraph.config import get_stream_writer
 from langgraph.errors import GraphRecursionError, InvalidUpdateError, ParentCommand
 from langgraph.func import entrypoint, task
 from langgraph.graph import END, START, StateGraph
-from langgraph.graph.message import MessageGraph, MessagesState, add_messages
+from langgraph.graph.message import MessagesState, add_messages
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.pregel import (
     NodeBuilder,
@@ -967,6 +968,7 @@ def test_pending_writes_resume(
                 "branch:to:two": AnyVersion(),
             },
             "channel_values": {"value": 6},
+            "updated_channels": ["value"],
         },
         metadata={
             "parents": {},
@@ -1014,6 +1016,7 @@ def test_pending_writes_resume(
                 "branch:to:one": None,
                 "branch:to:two": None,
             },
+            "updated_channels": ["branch:to:one", "branch:to:two", "value"],
         },
         metadata={
             "parents": {},
@@ -1065,6 +1068,7 @@ def test_pending_writes_resume(
                 "__start__": AnyVersion(),
             },
             "channel_values": {"__start__": {"value": 1}},
+            "updated_channels": ["__start__"],
         },
         metadata={
             "parents": {},
@@ -3907,7 +3911,7 @@ def test_remove_message_via_state_update(
 ) -> None:
     from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
 
-    workflow = MessageGraph()
+    workflow = StateGraph(state_schema=Annotated[list[AnyMessage], add_messages])  # type: ignore[arg-type]
     workflow.add_node(
         "chatbot",
         lambda state: [
@@ -3940,7 +3944,7 @@ def test_remove_message_via_state_update(
 def test_remove_message_from_node():
     from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
 
-    workflow = MessageGraph()
+    workflow = StateGraph(state_schema=Annotated[list[AnyMessage], add_messages])  # type: ignore[arg-type]
     workflow.add_node(
         "chatbot",
         lambda state: [
@@ -4801,7 +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]
 
@@ -4817,7 +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(
@@ -4828,12 +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(
@@ -4847,7 +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 "},
@@ -8262,3 +8283,53 @@ def test_fork_and_update_task_results(sync_checkpointer: BaseCheckpointSaver) ->
             ],
         ],
     ]
+
+
+def test_subgraph_streaming_sync() -> None:
+    """Test subgraph streaming when used as a node in sync version"""
+
+    # Create a fake chat model that returns a simple response
+    model = GenericFakeChatModel(messages=iter(["The weather is sunny today."]))
+
+    # Create a subgraph that uses the fake chat model
+    def call_model_node(state: MessagesState, config: RunnableConfig) -> MessagesState:
+        """Node that calls the model with the last message."""
+        messages = state["messages"]
+        last_message = messages[-1].content if messages else ""
+        response = model.invoke([("user", last_message)], config)
+        return {"messages": [response]}
+
+    # Build the subgraph
+    subgraph = StateGraph(MessagesState)
+    subgraph.add_node("call_model", call_model_node)
+    subgraph.add_edge(START, "call_model")
+    compiled_subgraph = subgraph.compile()
+
+    class SomeCustomState(TypedDict):
+        last_chunk: NotRequired[str]
+        num_chunks: NotRequired[int]
+
+    # Will invoke a subgraph as a function
+    def parent_node(state: SomeCustomState, config: RunnableConfig) -> dict:
+        """Node that runs the subgraph."""
+        msgs = {"messages": [("user", "What is the weather in Tokyo?")]}
+        events = []
+        for event in compiled_subgraph.stream(msgs, config, stream_mode="messages"):
+            events.append(event)
+        ai_msg_chunks = [ai_msg_chunk for ai_msg_chunk, _ in events]
+        return {
+            "last_chunk": ai_msg_chunks[-1],
+            "num_chunks": len(ai_msg_chunks),
+        }
+
+    # Build the main workflow
+    workflow = StateGraph(SomeCustomState)
+    workflow.add_node("subgraph", parent_node)
+    workflow.add_edge(START, "subgraph")
+    compiled_workflow = workflow.compile()
+
+    # Test the basic functionality
+    result = compiled_workflow.invoke({})
+
+    assert result["last_chunk"].content == "today."
+    assert result["num_chunks"] == 9

libs/langgraph/tests/test_pregel_async.py

@@ -26,7 +26,7 @@ from langchain_core.utils.aiter import aclosing
 from pydantic import BaseModel, ConfigDict, Field, ValidationError
 from pytest_mock import MockerFixture
 from syrupy import SnapshotAssertion
-from typing_extensions import TypedDict
+from typing_extensions import NotRequired, TypedDict
 
 from langgraph._internal._constants import CONFIG_KEY_NODE_FINISHED, ERROR, PULL
 from langgraph.cache.base import BaseCache
@@ -1908,6 +1908,7 @@ async def test_pending_writes_resume(
                 "branch:to:two": AnyVersion(),
             },
             "channel_values": {"value": 6},
+            "updated_channels": ["value"],
         },
         metadata={
             "parents": {},
@@ -1955,6 +1956,7 @@ async def test_pending_writes_resume(
                 "branch:to:one": None,
                 "branch:to:two": None,
             },
+            "updated_channels": ["branch:to:one", "branch:to:two", "value"],
         },
         metadata={
             "parents": {},
@@ -2002,6 +2004,7 @@ async def test_pending_writes_resume(
                 "__start__": AnyVersion(),
             },
             "channel_values": {"__start__": {"value": 1}},
+            "updated_channels": ["__start__"],
         },
         metadata={
             "parents": {},
@@ -9050,3 +9053,57 @@ async def test_fork_and_update_task_results(
             ],
         ],
     ]
+
+
+async def test_subgraph_streaming_async() -> None:
+    """Test subgraph streaming when used as a node in async version"""
+
+    # Create a fake chat model that returns a simple response
+    model = GenericFakeChatModel(messages=iter(["The weather is sunny today."]))
+
+    # Create a subgraph that uses the fake chat model
+    async def call_model_node(
+        state: MessagesState, config: RunnableConfig
+    ) -> MessagesState:
+        """Node that calls the model with the last message."""
+        messages = state["messages"]
+        last_message = messages[-1].content if messages else ""
+        response = await model.ainvoke([("user", last_message)], config)
+        return {"messages": [response]}
+
+    # Build the subgraph
+    subgraph = StateGraph(MessagesState)
+    subgraph.add_node("call_model", call_model_node)
+    subgraph.add_edge(START, "call_model")
+    compiled_subgraph = subgraph.compile()
+
+    class SomeCustomState(TypedDict):
+        last_chunk: NotRequired[str]
+        num_chunks: NotRequired[int]
+
+    # Will invoke a subgraph as a function
+    async def parent_node(state: SomeCustomState, config: RunnableConfig) -> dict:
+        """Node that runs the subgraph."""
+        msgs = {"messages": [("user", "What is the weather in Tokyo?")]}
+        events = []
+        async for event in compiled_subgraph.astream(
+            msgs, config, stream_mode="messages"
+        ):
+            events.append(event)
+        ai_msg_chunks = [ai_msg_chunk for ai_msg_chunk, _ in events]
+        return {
+            "last_chunk": ai_msg_chunks[-1],
+            "num_chunks": len(ai_msg_chunks),
+        }
+
+    # Build the main workflow
+    workflow = StateGraph(SomeCustomState)
+    workflow.add_node("subgraph", parent_node)
+    workflow.add_edge(START, "subgraph")
+    compiled_workflow = workflow.compile()
+
+    # Test the basic functionality
+    result = await compiled_workflow.ainvoke({})
+
+    assert result["last_chunk"].content == "today."
+    assert result["num_chunks"] == 9

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/Makefile

@@ -7,11 +7,11 @@ all: help
 # TESTING AND COVERAGE
 ######################
 
-start-postgres:
-	docker compose -f tests/compose-postgres.yml up -V --force-recreate --wait --remove-orphans
+start-services:
+	docker compose -f tests/compose-postgres.yml -f tests/compose-redis.yml up -V --force-recreate --wait --remove-orphans
 
-stop-postgres:
-	docker compose -f tests/compose-postgres.yml down -v
+stop-services:
+	docker compose -f tests/compose-postgres.yml -f tests/compose-redis.yml down -v
 
 TEST ?= .
 
@@ -19,15 +19,15 @@ test-fast:
 	LANGGRAPH_TEST_FAST=1 uv run pytest $(TEST)
 
 test:
-	make start-postgres && LANGGRAPH_TEST_FAST=0 uv run pytest $(TEST); \
+	make start-services && LANGGRAPH_TEST_FAST=0 uv run pytest $(TEST); \
 	EXIT_CODE=$$?; \
-	make stop-postgres; \
+	make stop-services; \
 	exit $$EXIT_CODE
 
 test_watch:
-	make start-postgres && LANGGRAPH_TEST_FAST=0 uv run ptw $(TEST); \
+	make start-services && LANGGRAPH_TEST_FAST=0 uv run ptw $(TEST); \
 	EXIT_CODE=$$?; \
-	make stop-postgres; \
+	make stop-services; \
 	exit $$EXIT_CODE
 
 ######################

libs/prebuilt/langgraph/prebuilt/_internal.py

@@ -1,26 +0,0 @@
-from typing import Any, Literal, TypedDict
-
-from langchain_core.messages import ToolCall
-
-
-class ToolCallWithContext(TypedDict):
-    """ToolCall with additional context for graph state.
-
-    This is an internal data-structure meant to help the ToolNode accept
-    tools calls with additional context (e.g. state) when dispatched using the
-    `Send` API.
-
-    The Send API is used in create_react_agent to be able to distribute the tool
-    calls in parallel and support human-in-the-loop workflows where graph execution
-    may be paused for an indefinite time.
-    """
-
-    tool_call: ToolCall
-    __type: Literal["tool_call_with_context"]
-    """Type to parameterize the payload.
-    
-    Using "__" as a prefix to be defensive against potential name collisions with
-    regular user state.
-    """
-    state: Any
-    """The state is provided as additional context."""

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -43,7 +43,6 @@ from langgraph.graph import END, StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.graph.state import CompiledStateGraph
 from langgraph.managed import RemainingSteps
-from langgraph.prebuilt._internal import ToolCallWithContext
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.runtime import Runtime
 from langgraph.store.base import BaseStore
@@ -474,6 +473,11 @@ def create_react_agent(
         if context_schema is None:
             context_schema = config_schema
 
+    if len(deprecated_kwargs) > 0:
+        raise TypeError(
+            f"create_react_agent() got unexpected keyword arguments: {deprecated_kwargs}"
+        )
+
     if version not in ("v1", "v2"):
         raise ValueError(
             f"Invalid version {version}. Supported versions are 'v1' and 'v2'."
@@ -790,17 +794,11 @@ def create_react_agent(
             elif version == "v2":
                 if post_model_hook is not None:
                     return "post_model_hook"
-                return [
-                    Send(
-                        "tools",
-                        ToolCallWithContext(
-                            __type="tool_call_with_context",
-                            tool_call=tool_call,
-                            state=state,
-                        ),
-                    )
-                    for tool_call in last_message.tool_calls
+                tool_calls = [
+                    tool_node.inject_tool_args(call, state, store)  # type: ignore[arg-type]
+                    for call in last_message.tool_calls
                 ]
+                return [Send("tools", [tool_call]) for tool_call in tool_calls]
 
     # Define a new graph
     workflow = StateGraph(
@@ -881,17 +879,11 @@ def create_react_agent(
             ]
 
             if pending_tool_calls:
-                return [
-                    Send(
-                        "tools",
-                        ToolCallWithContext(
-                            __type="tool_call_with_context",
-                            tool_call=tool_call,
-                            state=state,
-                        ),
-                    )
-                    for tool_call in pending_tool_calls
+                pending_tool_calls = [
+                    tool_node.inject_tool_args(call, state, store)  # type: ignore[arg-type]
+                    for call in pending_tool_calls
                 ]
+                return [Send("tools", [tool_call]) for tool_call in pending_tool_calls]
             elif isinstance(messages[-1], ToolMessage):
                 return entrypoint
             elif response_format is not None:
@@ -901,13 +893,13 @@ def create_react_agent(
 
         workflow.add_conditional_edges(
             "post_model_hook",
-            post_model_hook_router,  # type: ignore[arg-type]
+            post_model_hook_router,
             path_map=post_model_hook_paths,
         )
 
     workflow.add_conditional_edges(
         "agent",
-        should_continue,  # type: ignore[arg-type]
+        should_continue,
         path_map=agent_paths,
     )
 

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -34,6 +34,7 @@ Typical Usage:
 import asyncio
 import inspect
 import json
+import warnings
 from copy import copy, deepcopy
 from dataclasses import replace
 from typing import (
@@ -74,7 +75,7 @@ from typing_extensions import Annotated, get_args, get_origin
 from langgraph._internal._runnable import RunnableCallable
 from langgraph.errors import GraphBubbleUp
 from langgraph.graph.message import REMOVE_ALL_MESSAGES
-from langgraph.prebuilt._internal import ToolCallWithContext
+from langgraph.runtime import Runtime
 from langgraph.store.base import BaseStore
 from langgraph.types import Command, Send
 
@@ -341,14 +342,71 @@ class ToolNode(RunnableCallable):
         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.tool_to_runtime_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_)
+
+            # Check for deprecated annotation usage and emit warnings
+            # We need to check for annotations directly, not just the presence of state/store args
+            # because _get_state_args returns both reserved keywords and annotations
+            reserved_args = _get_reserved_keyword_args(tool_)
+
+            # Check for InjectedState and InjectedStore annotations
+            full_schema = tool_.get_input_schema()
+            has_injected_state = False
+            has_injected_store = False
+
+            for name, type_ in get_all_basemodel_annotations(full_schema).items():
+                type_args = get_args(type_)
+
+                # Check for InjectedState (can be class or instance)
+                for type_arg in type_args:
+                    if _is_injection(type_arg, InjectedState):
+                        if "state" not in reserved_args:
+                            has_injected_state = True
+                            break
+
+                # Check for InjectedStore (can be class or instance)
+                for type_arg in type_args:
+                    if _is_injection(type_arg, InjectedStore):
+                        if "runtime" not in reserved_args:
+                            has_injected_store = True
+                            break
+
+            # Emit deprecation warnings
+            if has_injected_state:
+                warnings.warn(
+                    f"Tool '{tool_.name}' uses deprecated InjectedState annotation. "
+                    f"Please update to use reserved keyword 'state' instead. "
+                    f"Example: def {tool_.name}(..., state) instead of "
+                    f"def {tool_.name}(..., state: Annotated[dict, InjectedState]). "
+                    f"The annotation-based approach will be removed in a future version.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+
+            if has_injected_store:
+                warnings.warn(
+                    f"Tool '{tool_.name}' uses deprecated InjectedStore annotation. "
+                    f"Please update to use reserved keyword 'runtime' instead. "
+                    f"Example: def {tool_.name}(..., runtime) and access store via runtime.store. "
+                    f"The annotation-based approach will be removed in a future version.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+
+            # Check for reserved keywords and wrap the tool if needed
+            # Only wrap tools with reserved keywords for now to avoid breaking existing functionality
+            if reserved_args:
+                tool_ = _wrap_tool_with_reserved_keywords(tool_, reserved_args)
+
             self.tools_by_name[tool_.name] = tool_
             self.tool_to_state_args[tool_.name] = _get_state_args(tool_)
             self.tool_to_store_arg[tool_.name] = _get_store_arg(tool_)
+            self.tool_to_runtime_arg[tool_.name] = _get_runtime_arg(tool_)
 
     def _func(
         self,
@@ -361,8 +419,7 @@ class ToolNode(RunnableCallable):
         *,
         store: Optional[BaseStore],
     ) -> Any:
-        tool_calls, input_type = self._parse_input(input)
-        tool_calls = [self.inject_tool_args(call, input, store) for call in tool_calls]
+        tool_calls, input_type = self._parse_input(input, store, config)
         config_list = get_config_list(config, len(tool_calls))
         input_types = [input_type] * len(tool_calls)
         with get_executor_for_config(config) as executor:
@@ -383,8 +440,7 @@ class ToolNode(RunnableCallable):
         *,
         store: Optional[BaseStore],
     ) -> Any:
-        tool_calls, input_type = self._parse_input(input)
-        tool_calls = [self.inject_tool_args(call, input, store) for call in tool_calls]
+        tool_calls, input_type = self._parse_input(input, store, config)
         outputs = await asyncio.gather(
             *(self._arun_one(call, input_type, config) for call in tool_calls)
         )
@@ -502,13 +558,14 @@ class ToolNode(RunnableCallable):
             return invalid_tool_message
 
         try:
-            input = {**call, **{"type": "tool_call"}}
-            response = await self.tools_by_name[call["name"]].ainvoke(input, config)
+            call_args = {**call, **{"type": "tool_call"}}
+            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:
-        # (1) a NodeInterrupt is raised inside a tool
-        # (2) a NodeInterrupt is raised inside a graph node for a graph called as a tool
+        # It can be triggered in the following scenarios,
+        # Where GraphInterrupt(GraphBubbleUp) is raised from an `interrupt` invocation most commonly:
+        # (1) a GraphInterrupt is raised inside a tool
+        # (2) a GraphInterrupt is raised inside a graph node for a graph called as a tool
         # (3) a GraphInterrupt is raised when a subgraph is interrupted inside a graph called as a tool
         # (2 and 3 can happen in a "supervisor w/ tools" multi-agent architecture)
         except GraphBubbleUp as e:
@@ -555,6 +612,8 @@ class ToolNode(RunnableCallable):
             dict[str, Any],
             BaseModel,
         ],
+        store: Optional[BaseStore],
+        config: Optional[RunnableConfig] = None,
     ) -> Tuple[list[ToolCall], Literal["list", "dict", "tool_calls"]]:
         input_type: Literal["list", "dict", "tool_calls"]
         if isinstance(input, list):
@@ -565,15 +624,6 @@ class ToolNode(RunnableCallable):
             else:
                 input_type = "list"
                 messages = input
-        elif (
-            isinstance(input, dict) and input.get("__type") == "tool_call_with_context"
-        ):
-            # mypy will not be able to type narrow correctly since the signature
-            # for input contains dict[str, Any]. We'd need to type dict[str, Any]
-            # before we can apply correct typing.
-            input = cast(ToolCallWithContext, input)  # type: ignore[assignment]
-            input_type = "tool_calls"
-            return [input["tool_call"]], input_type
         elif isinstance(input, dict) and (messages := input.get(self.messages_key, [])):
             input_type = "dict"
         elif messages := getattr(input, self.messages_key, []):
@@ -589,7 +639,10 @@ class ToolNode(RunnableCallable):
         except StopIteration:
             raise ValueError("No AIMessage found in input")
 
-        tool_calls = [call for call in latest_ai_message.tool_calls]
+        tool_calls = [
+            self.inject_tool_args(call, input, store, config)
+            for call in latest_ai_message.tool_calls
+        ]
         return tool_calls, input_type
 
     def _validate_tool_call(self, call: ToolCall) -> Optional[ToolMessage]:
@@ -632,19 +685,14 @@ class ToolNode(RunnableCallable):
                     err_msg += f" State should contain fields {required_fields_str}."
                 raise ValueError(err_msg)
 
-        if isinstance(input, dict) and input.get("__type") == "tool_call_with_context":
-            state = input["state"]
-        else:
-            state = input
-
-        if isinstance(state, dict):
+        if isinstance(input, dict):
             tool_state_args = {
-                tool_arg: state[state_field] if state_field else state
+                tool_arg: input[state_field] if state_field else input
                 for tool_arg, state_field in state_args.items()
             }
         else:
             tool_state_args = {
-                tool_arg: getattr(state, state_field) if state_field else state
+                tool_arg: getattr(input, state_field) if state_field else input
                 for tool_arg, state_field in state_args.items()
             }
 
@@ -673,6 +721,44 @@ class ToolNode(RunnableCallable):
         }
         return tool_call
 
+    def _inject_runtime(
+        self,
+        tool_call: ToolCall,
+        store: Optional[BaseStore],
+        config: RunnableConfig,
+    ) -> ToolCall:
+        """Inject runtime object into tool call arguments.
+
+        This method creates and injects a Runtime object containing store and
+        context into tools that have a 'runtime' reserved keyword parameter.
+
+        Args:
+            tool_call: The tool call dictionary to augment with runtime.
+            store: The persistent store instance to include in runtime.
+            config: The runnable configuration containing context.
+
+        Returns:
+            The tool call with runtime injected if needed.
+        """
+        runtime_arg = self.tool_to_runtime_arg[tool_call["name"]]
+        if not runtime_arg:
+            return tool_call
+
+        # Create a Runtime object with store and context from config
+        # The context will be available from the config if set
+        runtime = Runtime(
+            context=config.get("configurable", {}).get("context"),
+            store=store,
+            stream_writer=lambda _: None,  # Default no-op stream writer
+            previous=None,
+        )
+
+        tool_call["args"] = {
+            **tool_call["args"],
+            runtime_arg: runtime,
+        }
+        return tool_call
+
     def inject_tool_args(
         self,
         tool_call: ToolCall,
@@ -682,13 +768,15 @@ class ToolNode(RunnableCallable):
             BaseModel,
         ],
         store: Optional[BaseStore],
+        config: Optional[RunnableConfig] = None,
     ) -> ToolCall:
-        """Inject graph state and store into tool call arguments.
+        """Inject graph state, store, and runtime into tool call arguments.
 
         This method enables tools to access graph context that should not be controlled
-        by the model. Tools can declare dependencies on graph state or persistent storage
-        using InjectedState and InjectedStore annotations. This method automatically
-        identifies these dependencies and injects the appropriate values.
+        by the model. Tools can declare dependencies using either reserved keywords
+        ('state' and 'runtime') or annotations (InjectedState and InjectedStore for
+        backward compatibility). This method automatically identifies these dependencies
+        and injects the appropriate values.
 
         The injection process preserves the original tool call structure while adding
         the necessary context arguments. This allows tools to be both model-callable
@@ -701,10 +789,11 @@ class ToolNode(RunnableCallable):
                 Can be a message list, state dictionary, or BaseModel instance.
             store: The persistent store instance to inject into tools requiring storage.
                 Will be None if no store is configured for the graph.
+            config: The runnable configuration containing context for runtime injection.
 
         Returns:
             A new ToolCall dictionary with the same structure as the input but with
-            additional arguments injected based on the tool's annotation requirements.
+            additional arguments injected based on the tool's requirements.
 
         Raises:
             ValueError: If a tool requires store injection but no store is provided,
@@ -722,6 +811,11 @@ class ToolNode(RunnableCallable):
         tool_call_copy: ToolCall = copy(tool_call)
         tool_call_with_state = self._inject_state(tool_call_copy, input)
         tool_call_with_store = self._inject_store(tool_call_with_state, store)
+        if config:
+            tool_call_with_runtime = self._inject_runtime(
+                tool_call_with_store, store, config
+            )
+            return tool_call_with_runtime
         return tool_call_with_store
 
     def _validate_tool_command(
@@ -802,7 +896,6 @@ def tools_condition(
 
     Args:
         state: The current graph state to examine for tool calls. Supported formats:
-            - List of messages (for MessageGraph)
             - Dictionary containing a messages key (for StateGraph)
             - BaseModel instance with a messages attribute
         messages_key: The key or attribute name containing the message list in the state.
@@ -867,6 +960,16 @@ def tools_condition(
 class InjectedState(InjectedToolArg):
     """Annotation for injecting graph state into tool arguments.
 
+    .. deprecated:: 0.2.0
+        Use reserved keyword 'state' instead of InjectedState annotation.
+        The annotation-based approach will be removed in a future version.
+
+        Instead of:
+            def tool(x: int, state: Annotated[dict, InjectedState]) -> str:
+
+        Use:
+            def tool(x: int, state) -> str:
+
     This annotation enables tools to access graph state without exposing state
     management details to the language model. Tools annotated with InjectedState
     receive state data automatically during execution while remaining invisible
@@ -941,6 +1044,17 @@ class InjectedState(InjectedToolArg):
 class InjectedStore(InjectedToolArg):
     """Annotation for injecting persistent store into tool arguments.
 
+    .. deprecated:: 0.2.0
+        Use reserved keyword 'runtime' instead of InjectedStore annotation.
+        The annotation-based approach will be removed in a future version.
+
+        Instead of:
+            def tool(x: int, store: Annotated[BaseStore, InjectedStore()]) -> str:
+
+        Use:
+            def tool(x: int, runtime) -> str:
+                # Access store via runtime.store
+
     This annotation enables tools to access LangGraph's persistent storage system
     without exposing storage details to the language model. Tools annotated with
     InjectedStore receive the store instance automatically during execution while
@@ -1040,12 +1154,159 @@ def _is_injection(
     return False
 
 
-def _get_state_args(tool: BaseTool) -> dict[str, Optional[str]]:
-    """Extract state injection mappings from tool annotations.
+def _get_reserved_keyword_args(tool: BaseTool) -> dict[str, str]:
+    """Extract reserved keyword arguments from tool function signature.
 
-    This function analyzes a tool's input schema to identify arguments that should
-    be injected with graph state. It processes InjectedState annotations to build
-    a mapping of tool argument names to state field names.
+    This function inspects the tool's underlying function signature to identify
+    parameters with reserved names ('state' and 'runtime') that should be injected
+    automatically without requiring annotations.
+
+    Args:
+        tool: The tool to analyze for reserved keyword parameters.
+
+    Returns:
+        A dictionary mapping reserved parameter names to their injection type.
+        Keys are parameter names, values are either 'state' or 'runtime'.
+    """
+    reserved_args: dict[str, str] = {}
+
+    # Get the underlying function from the tool
+    if hasattr(tool, "func"):
+        func = tool.func
+    elif hasattr(tool, "_run"):
+        func = tool._run
+    else:
+        return reserved_args
+
+    # Inspect the function signature
+    try:
+        sig = inspect.signature(func)
+        for param_name, param in sig.parameters.items():
+            # Check for reserved keywords only if they don't have injection annotations
+            # Parameters with InjectedState or InjectedStore annotations should not be
+            # considered reserved keywords (they use the old annotation-based approach)
+            if param_name == "state" and param.annotation == inspect.Parameter.empty:
+                # Only consider 'state' as reserved if it has no annotation
+                reserved_args["state"] = "state"
+            elif (
+                param_name == "runtime" and param.annotation == inspect.Parameter.empty
+            ):
+                # Only consider 'runtime' as reserved if it has no annotation
+                reserved_args["runtime"] = "runtime"
+    except (ValueError, TypeError):
+        # If we can't inspect the signature, return empty
+        pass
+
+    return reserved_args
+
+
+def _wrap_tool_with_reserved_keywords(
+    tool: BaseTool, reserved_args: dict[str, str]
+) -> BaseTool:
+    """Wrap a tool to exclude reserved keyword parameters from its schema.
+
+    This function creates a wrapper around tools that use reserved keywords
+    ('state' and 'runtime') to ensure these parameters are excluded from the
+    schema presented to LLMs, similar to how InjectedToolArg annotations work.
+
+    Args:
+        tool: The original tool to wrap.
+        reserved_args: Dictionary of reserved keyword parameters to exclude.
+
+    Returns:
+        A wrapped tool with reserved keywords excluded from its schema.
+    """
+    if not reserved_args:
+        return tool
+
+    # Create a wrapper tool that filters the schema
+    # Use type: ignore to suppress mypy error for dynamic class creation
+    class FilteredTool(tool.__class__):  # type: ignore[name-defined]
+        """Tool wrapper that excludes reserved keywords from schema."""
+
+        def get_input_schema(
+            self, config: Optional[RunnableConfig] = None
+        ) -> Type[BaseModel]:
+            """Return the filtered schema without reserved keywords."""
+            original_schema = super().get_input_schema(config)
+
+            # If no reserved args to filter, return original
+            if not reserved_args:
+                return original_schema
+
+            # Create a new schema class dynamically
+            from pydantic import create_model
+
+            # Get fields to keep (exclude reserved keywords)
+            if hasattr(original_schema, "model_fields"):
+                # Pydantic v2
+                fields_to_keep: dict[str, Any] = {}
+                for name, field in original_schema.model_fields.items():
+                    if name not in reserved_args:
+                        # For create_model, we need to properly extract field information
+                        field_type = (
+                            field.annotation if hasattr(field, "annotation") else Any
+                        )
+
+                        # Handle field defaults and constraints
+                        if hasattr(field, "default") and field.default is not ...:
+                            # Field has a default value
+                            fields_to_keep[name] = (field_type, field.default)
+                        else:
+                            # Field has no default (required field)
+                            fields_to_keep[name] = (field_type, ...)
+            else:
+                # Pydantic v1 fallback
+                fields_to_keep = {}
+
+            # Create filtered schema
+            try:
+                filtered_schema = create_model(
+                    f"{original_schema.__name__}Filtered",
+                    **fields_to_keep,
+                )
+                return filtered_schema
+            except Exception:
+                # If schema creation fails, return original
+                return original_schema
+
+    # Create the filtered tool instance
+    filtered_tool = FilteredTool(
+        name=tool.name,
+        description=tool.description,
+        func=getattr(tool, "func", None),
+        args_schema=tool.get_input_schema(),  # Use original schema for initialization
+    )
+
+    # Copy over other attributes
+    for attr in [
+        "return_direct",
+        "verbose",
+        "callbacks",
+        "tags",
+        "metadata",
+        "handle_tool_error",
+        "handle_validation_error",
+        "response_format",
+    ]:
+        if hasattr(tool, attr):
+            setattr(filtered_tool, attr, getattr(tool, attr))
+
+    # Copy run methods
+    if hasattr(tool, "_run"):
+        filtered_tool._run = tool._run
+    if hasattr(tool, "_arun"):
+        filtered_tool._arun = tool._arun
+
+    return filtered_tool
+
+
+def _get_state_args(tool: BaseTool) -> dict[str, Optional[str]]:
+    """Extract state injection mappings from tool annotations or reserved keywords.
+
+    This function analyzes a tool to identify arguments that should be injected
+    with graph state. It first checks for the reserved keyword 'state', then
+    falls back to processing InjectedState annotations for backward compatibility.
 
     Args:
         tool: The tool to analyze for state injection requirements.
@@ -1054,10 +1315,20 @@ def _get_state_args(tool: BaseTool) -> dict[str, Optional[str]]:
         A dictionary mapping tool argument names to state field names. If a field
         name is None, the entire state should be injected for that argument.
     """
-    full_schema = tool.get_input_schema()
     tool_args_to_state_fields: dict = {}
 
+    # First check for reserved keywords
+    reserved_args = _get_reserved_keyword_args(tool)
+    if "state" in reserved_args:
+        tool_args_to_state_fields["state"] = None
+
+    # Then check for annotation-based injection (backward compatibility)
+    full_schema = tool.get_input_schema()
     for name, type_ in get_all_basemodel_annotations(full_schema).items():
+        # Skip if already handled by reserved keyword
+        if name in tool_args_to_state_fields:
+            continue
+
         injections = [
             type_arg
             for type_arg in get_args(type_)
@@ -1086,6 +1357,9 @@ def _get_store_arg(tool: BaseTool) -> Optional[str]:
     should be injected with the graph store. Only one store argument is supported
     per tool.
 
+    Note: With the new reserved keyword approach, store is accessed via the
+    'runtime' parameter which provides both store and context access.
+
     Args:
         tool: The tool to analyze for store injection requirements.
 
@@ -1096,6 +1370,7 @@ def _get_store_arg(tool: BaseTool) -> Optional[str]:
     Raises:
         ValueError: If a tool argument has multiple InjectedStore annotations.
     """
+    # Check for annotation-based injection (backward compatibility)
     full_schema = tool.get_input_schema()
     for name, type_ in get_all_basemodel_annotations(full_schema).items():
         injections = [
@@ -1114,3 +1389,19 @@ def _get_store_arg(tool: BaseTool) -> Optional[str]:
             pass
 
     return None
+
+
+def _get_runtime_arg(tool: BaseTool) -> Optional[str]:
+    """Extract runtime injection argument from tool signature.
+
+    This function checks if a tool has a 'runtime' reserved keyword parameter
+    that should be injected with a Runtime object containing store and context.
+
+    Args:
+        tool: The tool to analyze for runtime injection requirements.
+
+    Returns:
+        The string 'runtime' if the tool has a runtime parameter, or None otherwise.
+    """
+    reserved_args = _get_reserved_keyword_args(tool)
+    return "runtime" if "runtime" in reserved_args else None

libs/prebuilt/langgraph/prebuilt/tool_validator.py

@@ -2,8 +2,7 @@
 in a langchain graph. It applies a pydantic schema to tool_calls in the models' outputs,
 and returns a ToolMessage with the validated content. If the schema is not valid, it
 returns a ToolMessage with the error message. The ValidationNode can be used in a
-StateGraph with a "messages" key or in a MessageGraph. If multiple tool calls are
-requested, they will be run in parallel.
+StateGraph with a "messages" key. If multiple tool calls are requested, they will be run in parallel.
 """
 
 from typing import (
@@ -49,7 +48,7 @@ def _default_format_error(
 class ValidationNode(RunnableCallable):
     """A node that validates all tools requests from the last AIMessage.
 
-    It can be used either in StateGraph with a "messages" key or in MessageGraph.
+    It can be used either in StateGraph with a "messages" key.
 
     !!! note
 

libs/prebuilt/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-prebuilt"
-version = "0.6.3"
+version = "0.6.4"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.9"

libs/prebuilt/tests/compose-redis.yml

@@ -0,0 +1,16 @@
+name: langgraph-tests-redis
+services:
+  redis-test:
+    image: redis:7-alpine
+    ports:
+      - "6379:6379"
+    command: redis-server --maxmemory 256mb --maxmemory-policy allkeys-lru
+    healthcheck:
+      test: redis-cli ping
+      start_period: 10s
+      timeout: 1s
+      retries: 5
+      interval: 5s
+      start_interval: 1s
+    tmpfs:
+      - /data  # Use tmpfs for faster testing

libs/prebuilt/tests/test_deprecation.py

@@ -31,3 +31,11 @@ def test_config_schema_deprecation() -> None:
         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.py

@@ -24,7 +24,7 @@ from langchain_core.messages import (
     ToolCall,
     ToolMessage,
 )
-from langchain_core.runnables import RunnableLambda
+from langchain_core.runnables import RunnableConfig, RunnableLambda
 from langchain_core.tools import InjectedToolCallId, ToolException
 from langchain_core.tools import tool as dec_tool
 from pydantic import BaseModel, Field
@@ -910,6 +910,142 @@ def test_tool_node_inject_store() -> None:
         failing_graph.invoke({"messages": [msg], "bar": "baz"})
 
 
+def test_tool_node_inject_state_reserved_keyword() -> None:
+    """Test that tools can use 'state' as a reserved keyword parameter."""
+
+    def tool1(some_val: int, state) -> str:
+        """Tool 1 with reserved keyword 'state'."""
+        if isinstance(state, dict):
+            return state["foo"]
+        else:
+            return getattr(state, "foo")
+
+    def tool2(some_val: int, state) -> str:
+        """Tool 2 with reserved keyword 'state'."""
+        if isinstance(state, dict):
+            return f"val: {some_val}, foo: {state['foo']}"
+        else:
+            return f"val: {some_val}, foo: {getattr(state, 'foo')}"
+
+    def tool3(some_val: int, y: str, state) -> str:
+        """Tool 3 with reserved keyword 'state' and other params."""
+        if isinstance(state, dict):
+            return f"{y}: {state['foo']}"
+        else:
+            return f"{y}: {getattr(state, 'foo')}"
+
+    # Test with dict state
+    node = ToolNode([tool1, tool2, tool3])
+
+    # Verify that 'state' is excluded from tool schemas
+    for tool in [tool1, tool2, tool3]:
+        schema = node.tools_by_name[tool.__name__].get_input_schema()
+        if hasattr(schema, "model_fields"):
+            assert "state" not in schema.model_fields, (
+                f"'state' should be excluded from {tool.__name__} schema"
+            )
+        else:
+            assert "state" not in schema.__fields__, (
+                f"'state' should be excluded from {tool.__name__} schema"
+            )
+
+    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])
+        result = node.invoke({"messages": [msg], "foo": "bar"})
+        tool_message = result["messages"][-1]
+        if tool_name == "tool1":
+            assert tool_message.content == "bar", f"Failed for tool={tool_name}"
+        else:
+            assert tool_message.content == "val: 1, foo: bar", (
+                f"Failed for tool={tool_name}"
+            )
+
+    # Test tool3 with additional parameter
+    tool_call = {
+        "name": "tool3",
+        "args": {"some_val": 1, "y": "test"},
+        "id": "some 0",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+    result = node.invoke({"messages": [msg], "foo": "bar"})
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "test: bar"
+
+    # Test with Pydantic state
+    class State(MessagesState):
+        foo: str
+
+    node_pydantic = ToolNode([tool1, tool2, tool3])
+    for tool_name in ("tool1", "tool2"):
+        tool_call = {
+            "name": tool_name,
+            "args": {"some_val": 2},
+            "id": "some 1",
+            "type": "tool_call",
+        }
+        msg = AIMessage("hi?", tool_calls=[tool_call])
+        result = node_pydantic.invoke(State(messages=[msg], foo="baz"))
+        tool_message = result["messages"][-1]
+        if tool_name == "tool1":
+            assert tool_message.content == "baz", (
+                f"Failed for tool={tool_name} with Pydantic state"
+            )
+        else:
+            assert tool_message.content == "val: 2, foo: baz", (
+                f"Failed for tool={tool_name} with Pydantic state"
+            )
+
+
+def test_tool_node_inject_runtime_reserved_keyword() -> None:
+    """Test that tools can use 'runtime' as a reserved keyword parameter."""
+    from langgraph.runtime import Runtime
+
+    def tool1(some_val: int, runtime) -> str:
+        """Tool 1 with reserved keyword 'runtime'."""
+        assert isinstance(runtime, Runtime)
+        if runtime.store:
+            store_val = runtime.store.get(("test",), "test_key")
+            if store_val:
+                return f"val: {some_val}, store: {store_val.value['foo']}"
+        return f"val: {some_val}, no store"
+
+    store = InMemoryStore()
+    store.put(("test",), "test_key", {"foo": "bar"})
+
+    node = ToolNode([tool1])
+
+    # Verify that 'runtime' is excluded from tool schemas
+    schema = node.tools_by_name[tool1.__name__].get_input_schema()
+    if hasattr(schema, "model_fields"):
+        assert "runtime" not in schema.model_fields
+    else:
+        assert "runtime" not in schema.__fields__
+
+    # Test with store
+    tool_call = {
+        "name": "tool1",
+        "args": {"some_val": 1},
+        "id": "some 0",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+    result = node.invoke({"messages": [msg]}, store=store)
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "val: 1, store: bar"
+
+
+def test_tool_node_mixed_injection_styles() -> None:
+    """Test that tools can mix reserved keywords and annotations."""
+    pass  # TODO: Implementation to be added
+
+
 def test_tool_node_ensure_utf8() -> None:
     @dec_tool
     def get_day_list(days: list[str]) -> list[str]:
@@ -1236,6 +1372,190 @@ def test_tool_node_stream_writer() -> None:
     ]
 
 
+@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
+def test_react_agent_subgraph_streaming_sync(version: Literal["v1", "v2"]) -> None:
+    """Test React agent streaming when used as a subgraph node sync version"""
+
+    @dec_tool
+    def get_weather(city: str) -> str:
+        """Get the weather of a city."""
+        return f"The weather of {city} is sunny."
+
+    # Create a React agent
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [{"args": {"city": "Tokyo"}, "id": "1", "name": "get_weather"}],
+            [],
+        ]
+    )
+
+    agent = create_react_agent(
+        model,
+        tools=[get_weather],
+        prompt="You are a helpful travel assistant.",
+        version=version,
+    )
+
+    # Create a subgraph that uses the React agent as a node
+    def react_agent_node(state: MessagesState, config: RunnableConfig) -> MessagesState:
+        """Node that runs the React agent and collects streaming output."""
+        collected_content = ""
+
+        # Stream the agent output and collect content
+        for msg_chunk, msg_metadata in agent.stream(
+            {"messages": [("user", state["messages"][-1].content)]},
+            config,
+            stream_mode="messages",
+        ):
+            if hasattr(msg_chunk, "content") and msg_chunk.content:
+                collected_content += msg_chunk.content
+
+        return {"messages": [("assistant", collected_content)]}
+
+    # Create the main workflow with the React agent as a subgraph node
+    workflow = StateGraph(MessagesState)
+    workflow.add_node("react_agent", react_agent_node)
+    workflow.add_edge(START, "react_agent")
+    workflow.add_edge("react_agent", "__end__")
+    compiled_workflow = workflow.compile()
+
+    # Test the streaming functionality
+    result = compiled_workflow.invoke(
+        {"messages": [("user", "What is the weather in Tokyo?")]}
+    )
+
+    # Verify the result contains expected structure
+    assert len(result["messages"]) == 2
+    assert result["messages"][0].content == "What is the weather in Tokyo?"
+    assert "assistant" in str(result["messages"][1])
+
+    # Test streaming with subgraphs = True
+    result = compiled_workflow.invoke(
+        {"messages": [("user", "What is the weather in Tokyo?")]},
+        subgraphs=True,
+    )
+    assert len(result["messages"]) == 2
+
+    events = []
+    for event in compiled_workflow.stream(
+        {"messages": [("user", "What is the weather in Tokyo?")]},
+        stream_mode="messages",
+        subgraphs=False,
+    ):
+        events.append(event)
+
+    assert len(events) == 0
+
+    events = []
+    for event in compiled_workflow.stream(
+        {"messages": [("user", "What is the weather in Tokyo?")]},
+        stream_mode="messages",
+        subgraphs=True,
+    ):
+        events.append(event)
+
+    assert len(events) == 3
+    namespace, (msg, metadata) = events[0]
+    # FakeToolCallingModel returns a single AIMessage with tool calls
+    # The content of the AIMessage reflects the input message
+    assert msg.content.startswith("You are a helpful travel assistant")
+    namespace, (msg, metadata) = events[1]  # ToolMessage
+    assert msg.content.startswith("The weather of Tokyo is sunny.")
+
+
+@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
+async def test_react_agent_subgraph_streaming(version: Literal["v1", "v2"]) -> None:
+    """Test React agent streaming when used as a subgraph node."""
+
+    @dec_tool
+    def get_weather(city: str) -> str:
+        """Get the weather of a city."""
+        return f"The weather of {city} is sunny."
+
+    # Create a React agent
+    model = FakeToolCallingModel(
+        tool_calls=[
+            [{"args": {"city": "Tokyo"}, "id": "1", "name": "get_weather"}],
+            [],
+        ]
+    )
+
+    agent = create_react_agent(
+        model,
+        tools=[get_weather],
+        prompt="You are a helpful travel assistant.",
+        version=version,
+    )
+
+    # Create a subgraph that uses the React agent as a node
+    async def react_agent_node(
+        state: MessagesState, config: RunnableConfig
+    ) -> MessagesState:
+        """Node that runs the React agent and collects streaming output."""
+        collected_content = ""
+
+        # Stream the agent output and collect content
+        async for msg_chunk, msg_metadata in agent.astream(
+            {"messages": [("user", state["messages"][-1].content)]},
+            config,
+            stream_mode="messages",
+        ):
+            if hasattr(msg_chunk, "content") and msg_chunk.content:
+                collected_content += msg_chunk.content
+
+        return {"messages": [("assistant", collected_content)]}
+
+    # Create the main workflow with the React agent as a subgraph node
+    workflow = StateGraph(MessagesState)
+    workflow.add_node("react_agent", react_agent_node)
+    workflow.add_edge(START, "react_agent")
+    workflow.add_edge("react_agent", "__end__")
+    compiled_workflow = workflow.compile()
+
+    # Test the streaming functionality
+    result = await compiled_workflow.ainvoke(
+        {"messages": [("user", "What is the weather in Tokyo?")]}
+    )
+
+    # Verify the result contains expected structure
+    assert len(result["messages"]) == 2
+    assert result["messages"][0].content == "What is the weather in Tokyo?"
+    assert "assistant" in str(result["messages"][1])
+
+    # Test streaming with subgraphs = True
+    result = await compiled_workflow.ainvoke(
+        {"messages": [("user", "What is the weather in Tokyo?")]},
+        subgraphs=True,
+    )
+    assert len(result["messages"]) == 2
+
+    events = []
+    async for event in compiled_workflow.astream(
+        {"messages": [("user", "What is the weather in Tokyo?")]},
+        stream_mode="messages",
+        subgraphs=False,
+    ):
+        events.append(event)
+
+    assert len(events) == 0
+
+    events = []
+    async for event in compiled_workflow.astream(
+        {"messages": [("user", "What is the weather in Tokyo?")]},
+        stream_mode="messages",
+        subgraphs=True,
+    ):
+        events.append(event)
+
+    assert len(events) == 3
+    namespace, (msg, metadata) = events[0]
+    # FakeToolCallingModel returns a single AIMessage with tool calls
+    # The content of the AIMessage reflects the input message
+    assert msg.content.startswith("You are a helpful travel assistant")
+    namespace, (msg, metadata) = events[1]  # ToolMessage
+    assert msg.content.startswith("The weather of Tokyo is sunny.")
+
+
 @pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
 def test_tool_node_node_interrupt(
     sync_checkpointer: BaseCheckpointSaver, version: str

libs/prebuilt/tests/test_reserved_keywords.py

@@ -0,0 +1,184 @@
+"""Test reserved keywords for tool injection."""
+
+from typing import Annotated
+
+from langchain_core.messages import AIMessage
+
+from langgraph.prebuilt import InjectedState, InjectedStore, ToolNode
+from langgraph.store.base import BaseStore
+from langgraph.store.memory import InMemoryStore
+
+
+def test_tool_node_inject_runtime_reserved_keyword() -> None:
+    """Test that tools can use 'runtime' as a reserved keyword parameter."""
+    from langgraph.runtime import Runtime
+
+    def tool1(some_val: int, runtime) -> str:
+        """Tool 1 with reserved keyword 'runtime'."""
+        assert isinstance(runtime, Runtime), "runtime should be a Runtime instance"
+        # Access store from runtime
+        if runtime.store:
+            store_val = runtime.store.get(("test",), "test_key")
+            if store_val:
+                return f"val: {some_val}, store: {store_val.value['foo']}"
+        return f"val: {some_val}, no store"
+
+    def tool2(some_val: int, runtime) -> str:
+        """Tool 2 with reserved keyword 'runtime'."""
+        assert isinstance(runtime, Runtime), "runtime should be a Runtime instance"
+        # Access context from runtime
+        if runtime.context:
+            return (
+                f"val: {some_val}, context: {runtime.context.get('user_id', 'unknown')}"
+            )
+        return f"val: {some_val}, no context"
+
+    def tool3(x: int, y: str, runtime) -> str:
+        """Tool 3 with reserved keyword 'runtime' and other params."""
+        assert isinstance(runtime, Runtime), "runtime should be a Runtime instance"
+        has_store = "yes" if runtime.store else "no"
+        has_context = "yes" if runtime.context else "no"
+        return f"x: {x}, y: {y}, store: {has_store}, context: {has_context}"
+
+    store = InMemoryStore()
+    store.put(("test",), "test_key", {"foo": "bar"})
+
+    node = ToolNode([tool1, tool2, tool3])
+
+    # Verify that 'runtime' is excluded from tool schemas
+    for tool in [tool1, tool2, tool3]:
+        schema = node.tools_by_name[tool.__name__].get_input_schema()
+        if hasattr(schema, "model_fields"):
+            assert "runtime" not in schema.model_fields, (
+                f"'runtime' should be excluded from {tool.__name__} schema"
+            )
+        else:
+            assert "runtime" not in schema.__fields__, (
+                f"'runtime' should be excluded from {tool.__name__} schema"
+            )
+
+    # Test with store
+    tool_call = {
+        "name": "tool1",
+        "args": {"some_val": 1},
+        "id": "some 0",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+    result = node.invoke({"messages": [msg]}, store=store)
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "val: 1, store: bar"
+
+    # Test with context
+    from langchain_core.runnables import RunnableConfig
+
+    config = RunnableConfig(configurable={"context": {"user_id": "test_user"}})
+
+    tool_call = {
+        "name": "tool2",
+        "args": {"some_val": 2},
+        "id": "some 1",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+    result = node.invoke({"messages": [msg]}, config=config)
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "val: 2, context: test_user"
+
+    # Test with both store and context
+    tool_call = {
+        "name": "tool3",
+        "args": {"x": 3, "y": "test"},
+        "id": "some 2",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+    result = node.invoke({"messages": [msg]}, store=store, config=config)
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "x: 3, y: test, store: yes, context: yes"
+
+
+def test_tool_node_mixed_injection_styles() -> None:
+    """Test that tools can mix reserved keywords and annotations."""
+    from langgraph.runtime import Runtime
+
+    def tool1(some_val: int, state) -> str:
+        """Tool with reserved keyword 'state'."""
+        if isinstance(state, dict):
+            return f"reserved state: {state['foo']}"
+        else:
+            return f"reserved state: {getattr(state, 'foo')}"
+
+    def tool2(some_val: int, state: Annotated[dict, InjectedState]) -> str:
+        """Tool with annotation-based state injection."""
+        return f"annotated state: {state['foo']}"
+
+    def tool3(some_val: int, runtime) -> str:
+        """Tool with reserved keyword 'runtime'."""
+        assert isinstance(runtime, Runtime)
+        return f"reserved runtime: {runtime.context.get('user_id', 'none') if runtime.context else 'none'}"
+
+    def tool4(some_val: int, store: Annotated[BaseStore, InjectedStore()]) -> str:
+        """Tool with annotation-based store injection."""
+        store_val = store.get(("test",), "test_key")
+        return f"annotated store: {store_val.value['foo'] if store_val else 'none'}"
+
+    def tool5(x: int, state, runtime) -> str:
+        """Tool with both reserved keywords."""
+        assert isinstance(runtime, Runtime)
+        if isinstance(state, dict):
+            return f"both: state={state['foo']}, runtime={runtime.context.get('user_id', 'none') if runtime.context else 'none'}"
+        else:
+            return f"both: state={getattr(state, 'foo')}, runtime={runtime.context.get('user_id', 'none') if runtime.context else 'none'}"
+
+    store = InMemoryStore()
+    store.put(("test",), "test_key", {"foo": "bar"})
+
+    node = ToolNode([tool1, tool2, tool3, tool4, tool5])
+
+    # Verify schemas exclude injected parameters
+    for tool_name, expected_excluded in [
+        ("tool1", ["state"]),
+        ("tool2", ["state"]),
+        ("tool3", ["runtime"]),
+        ("tool4", ["store"]),
+        ("tool5", ["state", "runtime"]),
+    ]:
+        schema = node.tools_by_name[tool_name].get_input_schema()
+        if hasattr(schema, "model_fields"):
+            fields = schema.model_fields
+        else:
+            fields = schema.__fields__
+        for param in expected_excluded:
+            assert param not in fields, (
+                f"'{param}' should be excluded from {tool_name} schema"
+            )
+
+    from langchain_core.runnables import RunnableConfig
+
+    config = RunnableConfig(configurable={"context": {"user_id": "test_user"}})
+
+    # Test each tool
+    test_cases = [
+        ("tool1", {"some_val": 1}, "reserved state: baz"),
+        ("tool2", {"some_val": 2}, "annotated state: baz"),
+        ("tool3", {"some_val": 3}, "reserved runtime: test_user"),
+        ("tool4", {"some_val": 4}, "annotated store: bar"),
+        ("tool5", {"x": 5}, "both: state=baz, runtime=test_user"),
+    ]
+
+    for tool_name, args, expected in test_cases:
+        tool_call = {
+            "name": tool_name,
+            "args": args,
+            "id": f"id_{tool_name}",
+            "type": "tool_call",
+        }
+        msg = AIMessage("test", tool_calls=[tool_call])
+        result = node.invoke(
+            {"messages": [msg], "foo": "baz"}, store=store, config=config
+        )
+        tool_message = result["messages"][-1]
+        assert tool_message.content == expected, (
+            f"Failed for {tool_name}: got {tool_message.content}, expected {expected}"
+        )

libs/prebuilt/uv.lock

@@ -316,7 +316,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.6.3"
+version = "0.6.6"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -359,6 +359,7 @@ dev = [
     { name = "pytest-repeat" },
     { name = "pytest-watcher" },
     { name = "pytest-xdist", extras = ["psutil"] },
+    { name = "redis" },
     { name = "ruff" },
     { name = "syrupy" },
     { name = "types-requests" },
@@ -392,6 +393,7 @@ dev = [
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
     { name = "pytest-watcher" },
+    { name = "redis" },
     { name = "ruff" },
 ]
 
@@ -460,7 +462,7 @@ dev = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.6.3"
+version = "0.6.4"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -507,7 +509,6 @@ dev = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.2.0"
 source = { editable = "../sdk-py" }
 dependencies = [
     { name = "httpx" },

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__ = "0.2.3"
 
 __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/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
@@ -348,6 +349,72 @@ 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 = [
-    { 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]]
+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" }
+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]]
@@ -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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 [[package]]

test_deprecation_warnings.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python
+"""Test that deprecation warnings are properly emitted for InjectedState and InjectedStore."""
+
+import warnings
+from typing import Annotated
+from langchain_core.messages import AIMessage
+from langgraph.prebuilt import ToolNode, InjectedState, InjectedStore
+from langgraph.store.memory import InMemoryStore
+from langgraph.store.base import BaseStore
+
+
+def test_deprecation_warnings():
+    """Test that deprecation warnings are emitted for annotation-based injection."""
+    
+    # Define tools using deprecated annotations
+    def tool_with_injected_state(x: int, state: Annotated[dict, InjectedState]) -> str:
+        """Tool using deprecated InjectedState annotation."""
+        return f"state: {state.get('foo', 'none')}"
+    
+    def tool_with_injected_store(x: int, store: Annotated[BaseStore, InjectedStore()]) -> str:
+        """Tool using deprecated InjectedStore annotation."""
+        return "has store"
+    
+    # Define tools using new reserved keywords (should not trigger warnings)
+    def tool_with_reserved_state(x: int, state) -> str:
+        """Tool using reserved keyword 'state'."""
+        return f"state: {state.get('foo', 'none')}"
+    
+    def tool_with_reserved_runtime(x: int, runtime) -> str:
+        """Tool using reserved keyword 'runtime'."""
+        return "has runtime"
+    
+    print("Testing deprecation warnings...")
+    
+    # Capture warnings
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        
+        # Create ToolNode with deprecated annotation tools
+        print("\n1. Creating ToolNode with deprecated annotation tools...")
+        node1 = ToolNode([tool_with_injected_state, tool_with_injected_store])
+        
+        # Check that warnings were emitted
+        print(f"   Got {len(w)} warnings:")
+        for warning in w:
+            print(f"     - {warning.message}")
+        assert len(w) >= 1, f"Expected at least 1 warning, got {len(w)}"
+        
+        # Check warning messages
+        warning_messages = [str(warning.message) for warning in w]
+        assert any("InjectedState" in msg for msg in warning_messages), "Missing InjectedState warning"
+        assert any("InjectedStore" in msg for msg in warning_messages), "Missing InjectedStore warning"
+        
+        print(f"   ✓ Emitted {len(w)} deprecation warnings for annotation-based tools")
+        for warning in w:
+            print(f"     - {warning.message}")
+    
+    # Test that reserved keywords don't trigger warnings
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        
+        print("\n2. Creating ToolNode with reserved keyword tools...")
+        node2 = ToolNode([tool_with_reserved_state, tool_with_reserved_runtime])
+        
+        # Check that no warnings were emitted
+        assert len(w) == 0, f"Expected 0 warnings for reserved keywords, got {len(w)}"
+        print(f"   ✓ No warnings emitted for reserved keyword tools")
+    
+    # Test mixed usage
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        
+        print("\n3. Creating ToolNode with mixed tools...")
+        node3 = ToolNode([
+            tool_with_injected_state,  # Should warn
+            tool_with_reserved_state,  # Should not warn
+            tool_with_injected_store,  # Should warn
+            tool_with_reserved_runtime  # Should not warn
+        ])
+        
+        # Check that only 2 warnings were emitted (for the annotation-based tools)
+        assert len(w) == 2, f"Expected 2 warnings for mixed tools, got {len(w)}"
+        print(f"   ✓ Emitted {len(w)} warnings for annotation-based tools only")
+    
+    print("\n✅ All deprecation warning tests passed!")
+
+
+if __name__ == "__main__":
+    test_deprecation_warnings()
+

test_reserved_keywords.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+"""Test script to verify reserved keyword injection works correctly."""
+
+from typing import Any
+from langchain_core.tools import tool
+from langgraph.prebuilt import ToolNode
+from langgraph.runtime import Runtime
+from langgraph.store.memory import InMemoryStore
+from langchain_core.messages import AIMessage, ToolCall
+
+
+# Test tool with reserved keyword 'state'
+@tool
+def tool_with_state(x: int, state) -> str:
+    """Tool that uses reserved keyword 'state'."""
+    return f"x={x}, state_keys={list(state.keys()) if isinstance(state, dict) else 'not_dict'}"
+
+
+# Test tool with reserved keyword 'runtime'
+@tool
+def tool_with_runtime(x: int, runtime) -> str:
+    """Tool that uses reserved keyword 'runtime'."""
+    has_store = runtime.store is not None if hasattr(runtime, 'store') else False
+    return f"x={x}, has_store={has_store}"
+
+
+# Test tool with both reserved keywords
+@tool
+def tool_with_both(x: int, state, runtime) -> str:
+    """Tool that uses both reserved keywords."""
+    has_store = runtime.store is not None if hasattr(runtime, 'store') else False
+    return f"x={x}, state_keys={list(state.keys()) if isinstance(state, dict) else 'not_dict'}, has_store={has_store}"
+
+
+# Test regular tool without injection
+@tool
+def regular_tool(x: int, y: str) -> str:
+    """Regular tool without injection."""
+    return f"x={x}, y={y}"
+
+
+def test_reserved_keywords():
+    """Test that reserved keywords work correctly."""
+    
+    # Create ToolNode with all test tools
+    tools = [tool_with_state, tool_with_runtime, tool_with_both, regular_tool]
+    node = ToolNode(tools)
+    
+    # Check that reserved keywords are detected
+    print("Tool to state args:", node.tool_to_state_args)
+    print("Tool to runtime args:", node.tool_to_runtime_arg)
+    
+    # Check tool schemas - reserved keywords should be excluded
+    for tool_name, tool_obj in node.tools_by_name.items():
+        schema = tool_obj.get_input_schema()
+        print(f"\n{tool_name} schema fields:", list(schema.__fields__.keys()))
+        
+        # Verify reserved keywords are not in the schema
+        if tool_name == "tool_with_state":
+            assert "state" not in schema.__fields__, f"'state' should be excluded from {tool_name} schema"
+        elif tool_name == "tool_with_runtime":
+            assert "runtime" not in schema.__fields__, f"'runtime' should be excluded from {tool_name} schema"
+        elif tool_name == "tool_with_both":
+            assert "state" not in schema.__fields__, f"'state' should be excluded from {tool_name} schema"
+            assert "runtime" not in schema.__fields__, f"'runtime' should be excluded from {tool_name} schema"
+    
+    print("\nAll schema checks passed!")
+    
+    # Test actual injection
+    store = InMemoryStore()
+    state = {"messages": [], "foo": "bar"}
+    
+    # Create tool calls
+    tool_call1: ToolCall = {
+        "name": "tool_with_state",
+        "args": {"x": 1},
+        "id": "1",
+        "type": "tool_call"
+    }
+    
+    tool_call2: ToolCall = {
+        "name": "tool_with_runtime", 
+        "args": {"x": 2},
+        "id": "2",
+        "type": "tool_call"
+    }
+    
+    tool_call3: ToolCall = {
+        "name": "regular_tool",
+        "args": {"x": 3, "y": "test"},
+        "id": "3",
+        "type": "tool_call"
+    }
+    
+    # Test injection
+    from langchain_core.runnables import RunnableConfig
+    config = RunnableConfig(configurable={"context": {"user_id": "test_user"}})
+    
+    injected1 = node.inject_tool_args(tool_call1, state, store, config)
+    print(f"\nInjected args for tool_with_state: {injected1['args']}")
+    assert "state" in injected1["args"], "State should be injected"
+    
+    injected2 = node.inject_tool_args(tool_call2, state, store, config)
+    print(f"Injected args for tool_with_runtime: {injected2['args']}")
+    assert "runtime" in injected2["args"], "Runtime should be injected"
+    
+    injected3 = node.inject_tool_args(tool_call3, state, store, config)
+    print(f"Injected args for regular_tool: {injected3['args']}")
+    assert "state" not in injected3["args"], "State should not be injected"
+    assert "runtime" not in injected3["args"], "Runtime should not be injected"
+    
+    print("\nAll injection tests passed!")
+
+
+if __name__ == "__main__":
+    test_reserved_keywords()
+

test_reserved_keywords_full.py

@@ -0,0 +1,182 @@
+"""Test reserved keywords for tool injection."""
+from typing import Annotated, List
+from langchain_core.messages import AIMessage, AnyMessage
+from langgraph.prebuilt import ToolNode, InjectedState, InjectedStore
+from langgraph.store.memory import InMemoryStore
+from langgraph.store.base import BaseStore
+from langgraph.graph import MessagesState
+
+
+def test_tool_node_inject_runtime_reserved_keyword() -> None:
+    """Test that tools can use 'runtime' as a reserved keyword parameter."""
+    from langgraph.runtime import Runtime
+    
+    def tool1(some_val: int, runtime) -> str:
+        """Tool 1 with reserved keyword 'runtime'."""
+        assert isinstance(runtime, Runtime), "runtime should be a Runtime instance"
+        # Access store from runtime
+        if runtime.store:
+            store_val = runtime.store.get(("test",), "test_key")
+            if store_val:
+                return f"val: {some_val}, store: {store_val.value['foo']}"
+        return f"val: {some_val}, no store"
+
+    def tool2(some_val: int, runtime) -> str:
+        """Tool 2 with reserved keyword 'runtime'."""
+        assert isinstance(runtime, Runtime), "runtime should be a Runtime instance"
+        # Access context from runtime
+        if runtime.context:
+            return f"val: {some_val}, context: {runtime.context.get('user_id', 'unknown')}"
+        return f"val: {some_val}, no context"
+
+    def tool3(x: int, y: str, runtime) -> str:
+        """Tool 3 with reserved keyword 'runtime' and other params."""
+        assert isinstance(runtime, Runtime), "runtime should be a Runtime instance"
+        has_store = "yes" if runtime.store else "no"
+        has_context = "yes" if runtime.context else "no"
+        return f"x: {x}, y: {y}, store: {has_store}, context: {has_context}"
+
+    store = InMemoryStore()
+    store.put(("test",), "test_key", {"foo": "bar"})
+    
+    node = ToolNode([tool1, tool2, tool3])
+    
+    # Verify that 'runtime' is excluded from tool schemas
+    for tool in [tool1, tool2, tool3]:
+        schema = node.tools_by_name[tool.__name__].get_input_schema()
+        if hasattr(schema, 'model_fields'):
+            assert "runtime" not in schema.model_fields, f"'runtime' should be excluded from {tool.__name__} schema"
+        else:
+            assert "runtime" not in schema.__fields__, f"'runtime' should be excluded from {tool.__name__} schema"
+    
+    # Test with store
+    tool_call = {
+        "name": "tool1",
+        "args": {"some_val": 1},
+        "id": "some 0",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+    result = node.invoke({"messages": [msg]}, store=store)
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "val: 1, store: bar"
+    
+    # Test with context
+    from langchain_core.runnables import RunnableConfig
+    config = RunnableConfig(configurable={"context": {"user_id": "test_user"}})
+    
+    tool_call = {
+        "name": "tool2",
+        "args": {"some_val": 2},
+        "id": "some 1",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+    result = node.invoke({"messages": [msg]}, config=config)
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "val: 2, context: test_user"
+    
+    # Test with both store and context
+    tool_call = {
+        "name": "tool3",
+        "args": {"x": 3, "y": "test"},
+        "id": "some 2",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+    result = node.invoke({"messages": [msg]}, store=store, config=config)
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "x: 3, y: test, store: yes, context: yes"
+
+
+def test_tool_node_mixed_injection_styles() -> None:
+    """Test that tools can mix reserved keywords and annotations."""
+    from langgraph.runtime import Runtime
+    
+    def tool1(some_val: int, state) -> str:
+        """Tool with reserved keyword 'state'."""
+        if isinstance(state, dict):
+            return f"reserved state: {state['foo']}"
+        else:
+            return f"reserved state: {getattr(state, 'foo')}"
+    
+    def tool2(some_val: int, state: Annotated[dict, InjectedState]) -> str:
+        """Tool with annotation-based state injection."""
+        return f"annotated state: {state['foo']}"
+    
+    def tool3(some_val: int, runtime) -> str:
+        """Tool with reserved keyword 'runtime'."""
+        assert isinstance(runtime, Runtime)
+        return f"reserved runtime: {runtime.context.get('user_id', 'none') if runtime.context else 'none'}"
+    
+    def tool4(some_val: int, store: Annotated[BaseStore, InjectedStore()]) -> str:
+        """Tool with annotation-based store injection."""
+        store_val = store.get(("test",), "test_key")
+        return f"annotated store: {store_val.value['foo'] if store_val else 'none'}"
+    
+    def tool5(x: int, state, runtime) -> str:
+        """Tool with both reserved keywords."""
+        assert isinstance(runtime, Runtime)
+        if isinstance(state, dict):
+            return f"both: state={state['foo']}, runtime={runtime.context.get('user_id', 'none') if runtime.context else 'none'}"
+        else:
+            return f"both: state={getattr(state, 'foo')}, runtime={runtime.context.get('user_id', 'none') if runtime.context else 'none'}"
+    
+    store = InMemoryStore()
+    store.put(("test",), "test_key", {"foo": "bar"})
+    
+    node = ToolNode([tool1, tool2, tool3, tool4, tool5])
+    
+    # Verify schemas exclude injected parameters
+    for tool_name, expected_excluded in [
+        ("tool1", ["state"]),
+        ("tool2", ["state"]),
+        ("tool3", ["runtime"]),
+        ("tool4", ["store"]),
+        ("tool5", ["state", "runtime"]),
+    ]:
+        schema = node.tools_by_name[tool_name].get_input_schema()
+        if hasattr(schema, 'model_fields'):
+            fields = schema.model_fields
+        else:
+            fields = schema.__fields__
+        for param in expected_excluded:
+            assert param not in fields, f"'{param}' should be excluded from {tool_name} schema"
+    
+    from langchain_core.runnables import RunnableConfig
+    config = RunnableConfig(configurable={"context": {"user_id": "test_user"}})
+    
+    # Test each tool
+    test_cases = [
+        ("tool1", {"some_val": 1}, "reserved state: baz"),
+        ("tool2", {"some_val": 2}, "annotated state: baz"),
+        ("tool3", {"some_val": 3}, "reserved runtime: test_user"),
+        ("tool4", {"some_val": 4}, "annotated store: bar"),
+        ("tool5", {"x": 5}, "both: state=baz, runtime=test_user"),
+    ]
+    
+    for tool_name, args, expected in test_cases:
+        tool_call = {
+            "name": tool_name,
+            "args": args,
+            "id": f"id_{tool_name}",
+            "type": "tool_call",
+        }
+        msg = AIMessage("test", tool_calls=[tool_call])
+        result = node.invoke({"messages": [msg], "foo": "baz"}, store=store, config=config)
+        tool_message = result["messages"][-1]
+        assert tool_message.content == expected, f"Failed for {tool_name}: got {tool_message.content}, expected {expected}"
+
+
+if __name__ == "__main__":
+    print("Testing runtime reserved keyword...")
+    test_tool_node_inject_runtime_reserved_keyword()
+    print("✓ Runtime reserved keyword test passed!")
+    
+    print("\nTesting mixed injection styles...")
+    test_tool_node_mixed_injection_styles()
+    print("✓ Mixed injection styles test passed!")
+    
+    print("\nAll tests passed!")
+
+

verify_tests.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python
+"""Verify that the reserved keyword tests work correctly."""
+
+import sys
+sys.path.insert(0, 'libs/prebuilt')
+
+from tests.test_react_agent import test_tool_node_inject_state_reserved_keyword
+
+print("Running test_tool_node_inject_state_reserved_keyword...")
+try:
+    test_tool_node_inject_state_reserved_keyword()
+    print("✓ State reserved keyword test passed!")
+except Exception as e:
+    print(f"✗ State reserved keyword test failed: {e}")
+    sys.exit(1)
+
+print("\nAll existing tests passed!")