0.4.7

- The default applied for subgraphs should be applied only when stream mode arg not passed in

docs/docs/agents/tools.md

@@ -280,7 +280,21 @@ LangGraph allows access to short-term and long-term memory from tools. See [Memo
 
 ## Prebuilt tools
 
-LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
+You can use prebuilt tools from model providers by passing a dictionary with tool specs to the `tools` parameter of `create_react_agent`. For example, to use the `web_search_preview` tool from OpenAI:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+agent = create_react_agent(
+    model="openai:gpt-4o-mini", 
+    tools=[{"type": "web_search_preview"}]
+)
+response = agent.invoke(
+    {"messages": ["What was a positive news story from today?"]}
+)
+```
+
+Additionally, LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
 
 You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/).
 

docs/docs/cloud/deployment/self_hosted_control_plane.md

@@ -2,8 +2,8 @@
 
 Before deploying, review the [conceptual guide for the Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployment option.
 
-!!! important "Beta"
-    The Self-Hosted Control Plane deployment option is currently in beta stage.
+!!! info "Important"
+    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
 
 ## Prerequisites
 

docs/docs/cloud/deployment/self_hosted_data_plane.md

@@ -2,8 +2,8 @@
 
 Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) deployment option.
 
-!!! important "Beta"
-    The Self-Hosted Data Plane deployment option is currently in beta stage.
+!!! info "Important"
+    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
 
 ## Prerequisites
 

docs/docs/concepts/deployment_options.md

@@ -40,8 +40,8 @@ For more information, please see:
 
 ## Self-Hosted Data Plane
 
-!!! important "Beta"
-    The Self-Hosted Data Plane deployment option is currently in beta stage.
+!!! info "Important"
+    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](../concepts/plans.md) plan.
 
 The [Self-Hosted Data Plane](./langgraph_self_hosted_data_plane.md) deployment option is a "hybrid" model for deployment where we manage the [control plane](./langgraph_control_plane.md) in our cloud and you manage the [data plane](./langgraph_data_plane.md) in your cloud. This option provides a way to securely manage your data plane infrastructure, while offloading control plane management to us.
 
@@ -56,8 +56,8 @@ For more information, please see:
 
 ## Self-Hosted Control Plane
 
-!!! important "Beta"
-    The Self-Hosted Control Plane deployment option is currently in beta stage.
+!!! info "Important"
+    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](../concepts/plans.md) plan.
 
 The [Self-Hosted Control Plane](./langgraph_self_hosted_control_plane.md) deployment option is a fully self-hosted model for deployment where you manage the [control plane](./langgraph_control_plane.md) and [data plane](./langgraph_data_plane.md) in your cloud. This option give you full control and responsibility of the control plane and data plane infrastructure.
 

docs/docs/concepts/langgraph_control_plane.md

@@ -47,17 +47,22 @@ This section describes various features of the control plane.
 
 For simplicity, the control plane offers two deployment types with different resource allocations: `Development` and `Production`.
 
-| **Deployment Type** | **CPU** | **Memory** | **Scaling**         |
-|---------------------|---------|------------|---------------------|
-| Development         | 1 CPU   | 1 GB       | Up to 1 container   |
-| Production          | 2 CPU   | 2 GB       | Up to 10 containers |
+| **Deployment Type** | **CPU/Memory**  | **Scaling**         | **Database**                                                                     |
+|---------------------|-----------------|---------------------|----------------------------------------------------------------------------------|
+| Development         | 1 CPU, 1 GB RAM | Up to 1 container   | 10 GB disk, no backups                                                           |
+| Production          | 2 CPU, 2 GB RAM | Up to 10 containers | Autoscaling disk, automatic backups, highly available (multi-zone configuration) |
 
 CPU and memory resources are per container.
 
-!!! info "For [Cloud SaaS](../concepts/langgraph_cloud.md)"
+!!! warning "Immutable Deployment Type"
+
+    Once a deployment is created, the deployment type cannot be changed.
+
+!!! info "Resource Customization"
     For `Production` type deployments, resources can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources.
 
-!!! info 
+    For `Development` types deployments, database disk size can be manually increased on a case-by-case basis depending on use case and capacity constraints. For most use cases, [TTLs](../how-tos/ttl/configure_ttl.md) should be configured to manage disk usage. Contact support@langchain.dev to request an increase in resources.
+
     Resources for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments can be fully customized.
 
 ### Database Provisioning

docs/docs/concepts/langgraph_self_hosted_control_plane.md

@@ -2,8 +2,8 @@
 
 There are two versions of the self-hosted deployment: [Self-Hosted Data Plane](./deployment_options.md#self-hosted-data-plane) and [Self-Hosted Control Plane](./deployment_options.md#self-hosted-control-plane).
 
-!!! important "Beta"
-    The Self-Hosted Control Plane deployment option is currently in beta stage.
+!!! info "Important"
+    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
 
 ## Requirements
 

docs/docs/concepts/langgraph_self_hosted_data_plane.md

@@ -7,8 +7,8 @@ search:
 
 There are two versions of the self-hosted deployment: [Self-Hosted Data Plane](./deployment_options.md#self-hosted-data-plane) and [Self-Hosted Control Plane](./deployment_options.md#self-hosted-control-plane).
 
-!!! important "Beta"
-    The Self-Hosted Data Plane deployment option is currently in beta stage.
+!!! info "Important"
+    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
 
 ## Requirements
 

docs/uv.lock

@@ -2891,7 +2891,7 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.2.0"
+version = "0.2.1"
 source = { editable = "../libs/prebuilt" }
 dependencies = [
     { name = "langchain-core" },

libs/langgraph/langgraph/pregel/__init__.py

@@ -2214,12 +2214,14 @@ class Pregel(PregelProtocol):
             validate_keys(output_keys, self.channels)
         interrupt_before = interrupt_before or self.interrupt_before_nodes
         interrupt_after = interrupt_after or self.interrupt_after_nodes
-        stream_mode = stream_mode if stream_mode is not None else self.stream_mode
+        if stream_mode is None and CONFIG_KEY_TASK_ID in config.get(CONF, {}):
+            # if being called as a node in another graph, default to values mode
+            # but don't overwrite stream_mode arg if provided
+            stream_mode = ["values"]
+        elif stream_mode is None:
+            stream_mode = self.stream_mode
         if not isinstance(stream_mode, list):
             stream_mode = [stream_mode]
-        if CONFIG_KEY_TASK_ID in config.get(CONF, {}):
-            # if being called as a node in another graph, always use values mode
-            stream_mode = ["values"]
         if self.checkpointer is False:
             checkpointer: BaseCheckpointSaver | None = None
         elif CONFIG_KEY_CHECKPOINTER in config.get(CONF, {}):

libs/langgraph/langgraph/pregel/loop.py

@@ -135,7 +135,6 @@ P = ParamSpec("P")
 INPUT_DONE = object()
 INPUT_RESUMING = object()
 INPUT_SHOULD_VALIDATE = object()
-SPECIAL_CHANNELS = (ERROR, INTERRUPT, SCHEDULED)
 WritesT = Sequence[tuple[str, Any]]
 
 

libs/langgraph/langgraph/pregel/runner.py

@@ -56,6 +56,10 @@ EXCLUDED_FRAME_FNAMES = (
     "concurrent/futures/_base.py",
 )
 
+SKIP_RERAISE_SET: weakref.WeakSet[Union[concurrent.futures.Future, asyncio.Future]] = (
+    weakref.WeakSet()
+)
+
 
 class FuturesDict(Generic[F, E], dict[F, Optional[PregelExecutableTask]]):
     event: E
@@ -165,7 +169,6 @@ class PregelRunner:
                             futures=weakref.ref(futures),
                             schedule_task=schedule_task,
                             submit=self.submit,
-                            reraise=reraise,
                         ),
                     },
                 )
@@ -207,7 +210,6 @@ class PregelRunner:
                         futures=weakref.ref(futures),
                         schedule_task=schedule_task,
                         submit=self.submit,
-                        reraise=reraise,
                     ),
                 },
                 __reraise_on_exit__=reraise,
@@ -302,7 +304,6 @@ class PregelRunner:
                             futures=weakref.ref(futures),
                             schedule_task=schedule_task,
                             submit=self.submit,
-                            reraise=reraise,
                             loop=loop,
                         ),
                     },
@@ -349,7 +350,6 @@ class PregelRunner:
                             futures=weakref.ref(futures),
                             schedule_task=schedule_task,
                             submit=self.submit,
-                            reraise=reraise,
                             loop=loop,
                         ),
                     },
@@ -434,7 +434,8 @@ class PregelRunner:
                 raise exception
             else:
                 # save error to checkpointer
-                self.put_writes()(task.id, [(ERROR, exception)])  # type: ignore[misc]
+                task.writes.append((ERROR, exception))
+                self.put_writes()(task.id, task.writes)  # type: ignore[misc]
         else:
             if self.node_finished and (
                 task.config is None or TAG_HIDDEN not in task.config.get("tags", [])
@@ -456,7 +457,7 @@ def _should_stop_others(
         if fut.cancelled():
             continue
         elif exc := fut.exception():
-            if not isinstance(exc, GraphBubbleUp):
+            if not isinstance(exc, GraphBubbleUp) and fut not in SKIP_RERAISE_SET:
                 return True
 
     return False
@@ -494,7 +495,8 @@ def _panic_or_proceed(
     interrupts: list[GraphInterrupt] = []
     while done:
         # if any task failed
-        if exc := _exception(done.pop()):
+        fut = done.pop()
+        if exc := _exception(fut):
             # cancel all pending tasks
             while inflight:
                 inflight.pop().cancel()
@@ -503,7 +505,7 @@ def _panic_or_proceed(
                 if isinstance(exc, GraphInterrupt):
                     # collect interrupts
                     interrupts.append(exc)
-                else:
+                elif fut not in SKIP_RERAISE_SET:
                     raise exc
     # raise combined interrupts
     if interrupts:
@@ -530,7 +532,6 @@ def _call(
         [PregelExecutableTask, int, Optional[Call]], Optional[PregelExecutableTask]
     ],
     submit: weakref.ref[Submit],
-    reraise: bool,
 ) -> concurrent.futures.Future[Any]:
     if asyncio.iscoroutinefunction(func):
         raise RuntimeError("In an sync context async tasks cannot be called")
@@ -582,14 +583,16 @@ def _call(
                         callbacks=callbacks,
                         schedule_task=schedule_task,
                         submit=submit,
-                        reraise=reraise,
                     ),
                 },
-                __reraise_on_exit__=reraise,
+                __reraise_on_exit__=False,
                 # starting a new task in the next tick ensures
                 # updates from this tick are committed/streamed first
                 __next_tick__=True,
             )
+            # exceptions for call() tasks are raised into the parent task
+            # so we should not re-raise at the end of the tick
+            SKIP_RERAISE_SET.add(fut)
             futures()[fut] = next_task  # type: ignore[index]
     fut = cast(Union[asyncio.Future, concurrent.futures.Future], fut)
     # return a chained future to ensure commit() callback is called
@@ -613,7 +616,6 @@ def _acall(
     ],
     submit: weakref.ref[Submit],
     loop: asyncio.AbstractEventLoop,
-    reraise: bool = False,
     stream: bool = False,
 ) -> Union[asyncio.Future[Any], concurrent.futures.Future[Any]]:
     # return a chained future to ensure commit() callback is called
@@ -643,7 +645,6 @@ def _acall(
             schedule_task=schedule_task,
             submit=submit,
             loop=loop,
-            reraise=reraise,
             stream=stream,
         ),
         loop,
@@ -669,7 +670,6 @@ async def _acall_impl(
     ],
     submit: weakref.ref[Submit],
     loop: asyncio.AbstractEventLoop,
-    reraise: bool = False,
     stream: bool = False,
 ) -> None:
     try:
@@ -726,17 +726,19 @@ async def _acall_impl(
                                 schedule_task=schedule_task,
                                 submit=submit,
                                 loop=loop,
-                                reraise=reraise,
                             ),
                         },
-                        __name__=task().name,  # type: ignore[union-attr]
+                        __name__=next_task.name,
                         __cancel_on_exit__=True,
-                        __reraise_on_exit__=reraise,
+                        __reraise_on_exit__=False,
                         # starting a new task in the next tick ensures
                         # updates from this tick are committed/streamed first
                         __next_tick__=True,
                     ),
                 )
+                # exceptions for call() tasks are raised into the parent task
+                # so we should not re-raise at the end of the tick
+                SKIP_RERAISE_SET.add(fut)
                 futures()[fut] = next_task  # type: ignore[index]
         if fut is not None:
             chain_future(fut, destination)

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "0.4.5"
+version = "0.4.7"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.9"

libs/langgraph/tests/test_pregel.py

@@ -6873,7 +6873,7 @@ def test_sync_streaming_with_functional_api() -> None:
     should be greater than the time delay between the two tasks.
     """
 
-    time_delay = 0.01
+    time_delay = 0.05
 
     @task()
     def slow() -> dict:
@@ -8769,3 +8769,36 @@ def test_get_graph_root_channel(snapshot: SnapshotAssertion) -> None:
 
     assert json.dumps(graph.get_graph().to_json(), indent=2) == snapshot
     assert graph.get_graph().draw_mermaid(with_styles=False) == snapshot
+
+
+def test_imp_exception(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    @task()
+    def my_task(number: int):
+        time.sleep(0.1)
+        return number * 2
+
+    @task()
+    def task_with_exception(number: int):
+        time.sleep(0.1)
+        raise Exception("This is a test exception")
+
+    @entrypoint(checkpointer=sync_checkpointer)
+    def my_workflow(number: int):
+        my_task(number).result()
+        try:
+            task_with_exception(number).result()
+        except Exception as e:
+            print(f"Exception caught: {e}")
+        my_task(number).result()
+        return "done"
+
+    thread1 = {"configurable": {"thread_id": "1"}}
+    assert my_workflow.invoke(1, thread1) == "done"
+
+    assert [c for c in my_workflow.stream(1, thread1)] == [
+        {"my_task": 2},
+        {"my_task": 2},
+        {"my_workflow": "done"},
+    ]

libs/langgraph/tests/test_pregel_async.py

@@ -9148,3 +9148,301 @@ async def test_draw_invalid():
             {"source": "nothing", "target": "__end__"},
         ],
     }
+
+
+@NEEDS_CONTEXTVARS
+async def test_imp_exception(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    @task()
+    async def my_task(number: int):
+        await asyncio.sleep(0.1)
+        return number * 2
+
+    @task()
+    async def task_with_exception(number: int):
+        await asyncio.sleep(0.1)
+        raise Exception("This is a test exception")
+
+    @entrypoint(checkpointer=async_checkpointer)
+    async def my_workflow(number: int):
+        await my_task(number)
+        try:
+            await task_with_exception(number)
+        except Exception as e:
+            print(f"Exception caught: {e}")
+        await my_task(number)
+        return "done"
+
+    thread1 = {"configurable": {"thread_id": "1"}}
+    assert await my_workflow.ainvoke(1, thread1) == "done"
+
+    assert [c async for c in my_workflow.astream(1, thread1)] == [
+        {"my_task": 2},
+        {"my_task": 2},
+        {"my_workflow": "done"},
+    ]
+
+    assert [c async for c in my_workflow.astream_events(1, thread1)] == [
+        {
+            "event": "on_chain_start",
+            "data": {"input": 1},
+            "name": "LangGraph",
+            "tags": [],
+            "run_id": AnyStr(),
+            "metadata": {"thread_id": "1"},
+            "parent_ids": [],
+        },
+        {
+            "event": "on_chain_start",
+            "data": {"input": 1},
+            "name": "my_workflow",
+            "tags": ["graph:step:4"],
+            "run_id": AnyStr(),
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_workflow",
+                "langgraph_triggers": ("__start__",),
+                "langgraph_path": ("__pregel_pull", "my_workflow"),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [AnyStr()],
+        },
+        {
+            "event": "on_chain_start",
+            "data": {"input": {"number": 1}},
+            "name": "my_task",
+            "tags": ["seq:step:1"],
+            "run_id": AnyStr(),
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_stream",
+            "run_id": AnyStr(),
+            "name": "my_task",
+            "tags": ["seq:step:1"],
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "data": {"chunk": 2},
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_end",
+            "data": {"output": 2, "input": {"number": 1}},
+            "run_id": AnyStr(),
+            "name": "my_task",
+            "tags": ["seq:step:1"],
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_stream",
+            "run_id": AnyStr(),
+            "name": "LangGraph",
+            "tags": [],
+            "metadata": {"thread_id": "1"},
+            "data": {"chunk": {"my_task": 2}},
+            "parent_ids": [],
+        },
+        {
+            "event": "on_chain_start",
+            "data": {"input": {"number": 1}},
+            "name": "task_with_exception",
+            "tags": ["seq:step:1"],
+            "run_id": AnyStr(),
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_start",
+            "data": {"input": {"number": 1}},
+            "name": "my_task",
+            "tags": ["seq:step:1"],
+            "run_id": AnyStr(),
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_stream",
+            "run_id": AnyStr(),
+            "name": "my_task",
+            "tags": ["seq:step:1"],
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "data": {"chunk": 2},
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_end",
+            "data": {"output": 2, "input": {"number": 1}},
+            "run_id": AnyStr(),
+            "name": "my_task",
+            "tags": ["seq:step:1"],
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_stream",
+            "run_id": AnyStr(),
+            "name": "my_workflow",
+            "tags": ["graph:step:4"],
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_workflow",
+                "langgraph_triggers": ("__start__",),
+                "langgraph_path": ("__pregel_pull", "my_workflow"),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "data": {"chunk": "done"},
+            "parent_ids": [AnyStr()],
+        },
+        {
+            "event": "on_chain_stream",
+            "run_id": AnyStr(),
+            "name": "LangGraph",
+            "tags": [],
+            "metadata": {"thread_id": "1"},
+            "data": {"chunk": {"my_task": 2}},
+            "parent_ids": [],
+        },
+        {
+            "event": "on_chain_end",
+            "data": {"output": "done", "input": 1},
+            "run_id": AnyStr(),
+            "name": "my_workflow",
+            "tags": ["graph:step:4"],
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_workflow",
+                "langgraph_triggers": ("__start__",),
+                "langgraph_path": ("__pregel_pull", "my_workflow"),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [AnyStr()],
+        },
+        {
+            "event": "on_chain_stream",
+            "run_id": AnyStr(),
+            "name": "LangGraph",
+            "tags": [],
+            "metadata": {"thread_id": "1"},
+            "data": {"chunk": {"my_workflow": "done"}},
+            "parent_ids": [],
+        },
+        {
+            "event": "on_chain_end",
+            "data": {"output": "done"},
+            "run_id": AnyStr(),
+            "name": "LangGraph",
+            "tags": [],
+            "metadata": {"thread_id": "1"},
+            "parent_ids": [],
+        },
+    ]

libs/langgraph/uv.lock

@@ -1,5 +1,4 @@
 version = 1
-revision = 1
 requires-python = ">=3.9"
 resolution-markers = [
     "python_full_version >= '3.13' and python_full_version < '4.0'",
@@ -1197,7 +1196,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.4.5"
+version = "0.4.7"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -1416,7 +1415,7 @@ inmem = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.2.0"
+version = "0.2.1"
 source = { editable = "../prebuilt" }
 dependencies = [
     { name = "langchain-core" },
@@ -2123,6 +2122,8 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b8/2a/25e0be2b509c28375c7f75c7e8d8d060773f2cce4856a1654276e3202339/pycryptodome-3.22.0-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:d21c1eda2f42211f18a25db4eaf8056c94a8563cd39da3683f89fe0d881fb772", size = 2262255 },
     { url = "https://files.pythonhosted.org/packages/41/58/60917bc4bbd91712e53ce04daf237a74a0ad731383a01288130672994328/pycryptodome-3.22.0-cp37-abi3-win32.whl", hash = "sha256:f02baa9f5e35934c6e8dcec91fcde96612bdefef6e442813b8ea34e82c84bbfb", size = 1763403 },
     { url = "https://files.pythonhosted.org/packages/55/f4/244c621afcf7867e23f63cfd7a9630f14cfe946c9be7e566af6c3915bcde/pycryptodome-3.22.0-cp37-abi3-win_amd64.whl", hash = "sha256:d086aed307e96d40c23c42418cbbca22ecc0ab4a8a0e24f87932eeab26c08627", size = 1794568 },
+    { url = "https://files.pythonhosted.org/packages/cd/13/16d3a83b07f949a686f6cfd7cfc60e57a769ff502151ea140ad67b118e26/pycryptodome-3.22.0-pp27-pypy_73-manylinux2010_x86_64.whl", hash = "sha256:98fd9da809d5675f3a65dcd9ed384b9dc67edab6a4cda150c5870a8122ec961d", size = 1700779 },
+    { url = "https://files.pythonhosted.org/packages/13/af/16d26f7dfc5fd7696ea2c91448f937b51b55312b5bed44f777563e32a4fe/pycryptodome-3.22.0-pp27-pypy_73-win32.whl", hash = "sha256:37ddcd18284e6b36b0a71ea495a4c4dca35bb09ccc9bfd5b91bfaf2321f131c1", size = 1775230 },
     { url = "https://files.pythonhosted.org/packages/37/c3/e3423e72669ca09f141aae493e1feaa8b8475859898b04f57078280a61c4/pycryptodome-3.22.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:b4bdce34af16c1dcc7f8c66185684be15f5818afd2a82b75a4ce6b55f9783e13", size = 1618698 },
     { url = "https://files.pythonhosted.org/packages/f9/b7/35eec0b3919cafea362dcb68bb0654d9cb3cde6da6b7a9d8480ce0bf203a/pycryptodome-3.22.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2988ffcd5137dc2d27eb51cd18c0f0f68e5b009d5fec56fbccb638f90934f333", size = 1666957 },
     { url = "https://files.pythonhosted.org/packages/b0/1f/f49bccdd8d61f1da4278eb0d6aee7f988f1a6ec4056b0c2dc51eda45ae27/pycryptodome-3.22.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e653519dedcd1532788547f00eeb6108cc7ce9efdf5cc9996abce0d53f95d5a9", size = 1659242 },

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -240,7 +240,7 @@ def _validate_chat_history(
 
 def create_react_agent(
     model: Union[str, LanguageModelLike],
-    tools: Union[Sequence[Union[BaseTool, Callable]], ToolNode],
+    tools: Union[Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode],
     *,
     prompt: Optional[Prompt] = None,
     response_format: Optional[
@@ -420,12 +420,13 @@ def create_react_agent(
             else AgentState
         )
 
+    llm_builtin_tools: list[dict] = []
     if isinstance(tools, ToolNode):
         tool_classes = list(tools.tools_by_name.values())
         tool_node = tools
     else:
-        tool_node = ToolNode(tools)
-        # get the tool functions wrapped in a tool class from the ToolNode
+        llm_builtin_tools = [t for t in tools if isinstance(t, dict)]
+        tool_node = ToolNode([t for t in tools if not isinstance(t, dict)])
         tool_classes = list(tool_node.tools_by_name.values())
 
     if isinstance(model, str):
@@ -442,8 +443,12 @@ def create_react_agent(
 
     tool_calling_enabled = len(tool_classes) > 0
 
-    if _should_bind_tools(model, tool_classes) and tool_calling_enabled:
-        model = cast(BaseChatModel, model).bind_tools(tool_classes)
+    if (
+        _should_bind_tools(model, tool_classes)
+        and len(tool_classes) > 0
+        or (len(llm_builtin_tools) > 0)
+    ):
+        model = cast(BaseChatModel, model).bind_tools(tool_classes + llm_builtin_tools)  # type: ignore[operator]
 
     model_runnable = _get_prompt_runnable(prompt) | model
 

libs/prebuilt/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-prebuilt"
-version = "0.2.0"
+version = "0.2.1"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.9"

libs/prebuilt/uv.lock

@@ -1,5 +1,4 @@
 version = 1
-revision = 1
 requires-python = ">=3.9"
 resolution-markers = [
     "python_full_version >= '3.12.4'",
@@ -320,7 +319,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.4.5"
+version = "0.4.7"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -461,7 +460,7 @@ dev = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.2.0"
+version = "0.2.1"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },

libs/scheduler-kafka/uv.lock

@@ -1,5 +1,4 @@
 version = 1
-revision = 1
 requires-python = ">=3.9"
 resolution-markers = [
     "python_full_version >= '3.12.4'",
@@ -447,7 +446,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.4.5"
+version = "0.4.7"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -558,7 +557,7 @@ dev = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.2.0"
+version = "0.2.1"
 source = { editable = "../prebuilt" }
 dependencies = [
     { name = "langchain-core" },