fix(langgraph): Tidy up AgentState (#5801 )

Fixes https://github.com/langchain-ai/langgraph/issues/5784 * Removes usage of `is_last_step`, no longer needed with `remaining_steps` * Make `remaining_steps` `NotRequired` so that json schema doesn't suggest need for user input * Move `PregelScratchpad` to shared utils file to prevent circular import issue (it's used from `channels/managed` and other pregel files). * Ensures that managed values wrapped in `NotRequired` or `Required` are still recognized!
fix(docs): update agents.md (#5800 )
2026-08-25 09:02:25 +02:00 · 2025-08-03 07:12:53 -04:00 · 2025-08-02 06:09:09 -04:00 · 2025-08-01 15:41:21 -04:00 · 2025-08-01 14:52:38 -04:00 · 2025-08-01 17:25:14 +00:00
56 changed files with 865 additions and 599 deletions
@@ -3,7 +3,8 @@ name: CI

 on:
  push:
-    branches: [main, v1]
+    branches:
+      - main
  pull_request:

 permissions:
@@ -18,6 +18,9 @@ build-prebuilt:
 build-docs: build-prebuilt
 	TARGET_LANGUAGE=python uv run python -m mkdocs build --clean -f mkdocs.yml --strict

+build-docs-js: build-prebuilt
+	TARGET_LANGUAGE=js uv run python -m mkdocs build --clean -f mkdocs.yml --strict
+
 llms-text:
 	uv run python -m _scripts.generate_llms_text docs/llms-full.txt

@@ -4,52 +4,48 @@ This module provides link mappings for different language/framework scopes
 to resolve @[link_name] references to actual URLs.
 """

-# Python-specific link mappings
 # Python-specific link mappings
 PYTHON_LINK_MAP = {
    "StateGraph": "reference/graphs/#langgraph.graph.StateGraph",
-    "add_conditional_edges": "reference/graphs/#langgraph.graph.StateGraph.add_conditional_edges",
-    "add_edge": "reference/graphs/#langgraph.graph.StateGraph.add_edge",
-    "add_node": "reference/graphs/#langgraph.graph.StateGraph.add_node",
-    "add_messages": "reference/messages/#langgraph.graph.message.add_messages",
-    "ToolNode": "reference/prebuilt/#langgraph.prebuilt.tool_node.ToolNode",
+    "add_conditional_edges": "reference/graphs/#langgraph.graph.state.StateGraph.add_conditional_edges",
+    "add_edge": "reference/graphs/#langgraph.graph.state.StateGraph.add_edge",
+    "add_node": "reference/graphs/#langgraph.graph.state.StateGraph.add_node",
+    "add_messages": "reference/graphs/#langgraph.graph.message.add_messages",
+    "ToolNode": "reference/agents/#langgraph.prebuilt.tool_node.ToolNode",
    "CompiledStateGraph.astream": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.astream",
-    "Pregel.astream": "reference/graphs/#langgraph.pregel.Pregel.astream",
+    "Pregel.astream": "reference/pregel/#langgraph.pregel.Pregel.astream",
    "AsyncPostgresSaver": "reference/checkpoints/#langgraph.checkpoint.postgres.aio.AsyncPostgresSaver",
    "AsyncSqliteSaver": "reference/checkpoints/#langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver",
    "BaseCheckpointSaver": "reference/checkpoints/#langgraph.checkpoint.base.BaseCheckpointSaver",
-    "BaseStore": "reference/stores/#langgraph.store.base.BaseStore",
-    "BaseStore.put": "reference/stores/#langgraph.store.base.BaseStore.put",
-    "BinaryOperatorAggregate": "reference/channels/#langgraph.channels.BinaryOperatorAggregate",
+    "BaseStore": "reference/store/#langgraph.store.base.BaseStore",
+    "BaseStore.put": "reference/store/#langgraph.store.base.BaseStore.put",
+    "BinaryOperatorAggregate": "reference/pregel/#langgraph.pregel.Pregel--advanced-channels-context-and-binaryoperatoraggregate",
    "CipherProtocol": "reference/checkpoints/#langgraph.checkpoint.serde.base.CipherProtocol",
-    "client.runs.stream": "reference/client/#langgraph_sdk.client.RunsClient.stream",
-    "client.runs.wait": "reference/client/#langgraph_sdk.client.RunsClient.wait",
-    "client.threads.get_history": "reference/client/#langgraph_sdk.client.ThreadsClient.get_history",
-    "client.threads.update_state": "reference/client/#langgraph_sdk.client.ThreadsClient.update_state",
+    "client.runs.stream": "cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.RunsClient.stream",
+    "client.runs.wait": "cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.RunsClient.wait",
+    "client.threads.get_history": "cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.ThreadsClient.get_history",
+    "client.threads.update_state": "cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.ThreadsClient.update_state",
    "Command": "reference/types/#langgraph.types.Command",
    "CompiledStateGraph": "reference/graphs/#langgraph.graph.state.CompiledStateGraph",
    "create_react_agent": "reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent",
    "create_supervisor": "reference/supervisor/#langgraph_supervisor.supervisor.create_supervisor",
    "EncryptedSerializer": "reference/checkpoints/#langgraph.checkpoint.serde.encrypted.EncryptedSerializer",
-    "entrypoint.final": "reference/functions/#langgraph.func.entrypoint.final",
-    "entrypoint": "reference/functions/#langgraph.func.entrypoint",
+    "entrypoint.final": "reference/func/#langgraph.func.entrypoint.final",
+    "entrypoint": "reference/func/#langgraph.func.entrypoint",
    "from_pycryptodome_aes": "reference/checkpoints/#langgraph.checkpoint.serde.encrypted.EncryptedSerializer.from_pycryptodome_aes",
-    # "getContextVariable": "<insert-ref>",
    "get_state_history": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.get_state_history",
    "get_stream_writer": "reference/config/#langgraph.config.get_stream_writer",
    "HumanInterrupt": "reference/prebuilt/#langgraph.prebuilt.interrupt.HumanInterrupt",
-    "InjectedState": "reference/prebuilt/#langgraph.prebuilt.InjectedState",
+    "InjectedState": "reference/agents/#langgraph.prebuilt.tool_node.InjectedState",
    "InMemorySaver": "reference/checkpoints/#langgraph.checkpoint.memory.InMemorySaver",
-    "interrupt": "reference/graphs/#langgraph.graph.interrupt",
+    "interrupt": "reference/types/#langgraph.types.Interrupt",
    "CompiledStateGraph.invoke": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.invoke",
    "JsonPlusSerializer": "reference/checkpoints/#langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer",
-    "langgraph.json": "reference/configuration/#configuration-file",
+    "langgraph.json": "cloud/reference/cli/#configuration-file",
    "LastValue": "reference/channels/#langgraph.channels.LastValue",
-    # "MemorySaver": "<insert-ref>",
-    # "messagesStateReducer": "<insert-ref>",
    "PostgresSaver": "reference/checkpoints/#langgraph.checkpoint.postgres.PostgresSaver",
-    "Pregel": "reference/graphs/#langgraph.pregel.Pregel",
-    "Pregel.stream": "reference/graphs/#langgraph.pregel.Pregel.stream",
+    "Pregel": "reference/pregel/",
+    "Pregel.stream": "reference/pregel/#langgraph.pregel.Pregel.stream",
    "pre_model_hook": "reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent",
    "protocol": "reference/checkpoints/#langgraph.checkpoint.serde.base.SerializerProtocol",
    "Send": "reference/types/#langgraph.types.Send",
@@ -57,68 +53,56 @@ PYTHON_LINK_MAP = {
    "SqliteSaver": "reference/checkpoints/#langgraph.checkpoint.sqlite.SqliteSaver",
    "START": "reference/constants/#langgraph.constants.START",
    "CompiledStateGraph.stream": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.stream",
-    "task": "reference/functions/#langgraph.func.task",
+    "task": "reference/func/#langgraph.func.task",
    "Topic": "reference/channels/#langgraph.channels.Topic",
    "update_state": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.update_state",
 }

-
 # JavaScript-specific link mappings
 JS_LINK_MAP = {
    "Auth": "reference/classes/sdk_auth.Auth.html",
    "StateGraph": "reference/classes/langgraph.StateGraph.html",
-    "add_conditional_edges": "reference/functions/langgraph_StateGraph.addConditionalEdges.html",
-    "add_edge": "reference/functions/langgraph_StateGraph.addEdge.html",
-    "add_node": "reference/functions/langgraph_StateGraph.addNode.html",
-    "add_messages": "reference/functions/langgraph_message.addMessages.html",
+    "add_conditional_edges": "/reference/classes/langgraph.StateGraph.html#addConditionalEdges",
+    "add_edge": "reference/classes/langgraph.StateGraph.html#addEdge",
+    "add_node": "reference/classes/langgraph.StateGraph.html#addNode",
+    "add_messages": "reference/modules/langgraph.html#addMessages",
    "ToolNode": "reference/classes/langgraph_prebuilt.ToolNode.html",
-    "CompiledStateGraph.astream()": "reference/functions/langgraph_CompiledStateGraph.astream.html",
-    "Pregel.astream": "reference/functions/langgraph_Pregel.astream.html",
-    "AsyncPostgresSaver": "reference/classes/langgraph_checkpoint_postgres_aio.AsyncPostgresSaver.html",
-    "AsyncSqliteSaver": "reference/classes/langgraph_checkpoint_sqlite_aio.AsyncSqliteSaver.html",
-    "BaseCheckpointSaver": "reference/classes/langgraph_checkpoint_base.BaseCheckpointSaver.html",
-    "BaseStore": "reference/classes/langgraph_store_base.BaseStore.html",
-    "BaseStore.put": "reference/functions/langgraph_store_base.BaseStore.put.html",
-    "BinaryOperatorAggregate": "reference/classes/langgraph_channels.BinaryOperatorAggregate.html",
-    "CipherProtocol": "reference/classes/langgraph_checkpoint_serde_base.CipherProtocol.html",
-    "client.runs.stream": "reference/functions/langgraph_sdk_client.RunsClient.stream.html",
-    "client.runs.wait": "reference/functions/langgraph_sdk_client.RunsClient.wait.html",
-    "client.threads.get_history": "reference/functions/langgraph_sdk_client.ThreadsClient.getHistory.html",
-    "client.threads.update_state": "reference/functions/langgraph_sdk_client.ThreadsClient.updateState.html",
+    "BaseCheckpointSaver": "reference/classes/checkpoint.BaseCheckpointSaver.html",
+    "BaseStore": "reference/classes/checkpoint.BaseStore.html",
+    "BaseStore.put": "reference/classes/checkpoint.BaseStore.html#put",
+    "BinaryOperatorAggregate": "reference/classes/langgraph.BinaryOperatorAggregate.html",
+    "client.runs.stream": "reference/classes/sdk_client.RunsClient.html#stream",
+    "client.runs.wait": "reference/classes/sdk_client.RunsClient.html#wait",
+    "client.threads.get_history": "reference/classes/sdk_client.ThreadsClient.html#getHistory",
+    "client.threads.update_state": "reference/classes/sdk_client.ThreadsClient.html#updateState",
    "Command": "reference/classes/langgraph.Command.html",
    "CompiledStateGraph": "reference/classes/langgraph.CompiledStateGraph.html",
    "create_react_agent": "reference/functions/langgraph_prebuilt.createReactAgent.html",
    "create_supervisor": "reference/functions/langgraph_supervisor.createSupervisor.html",
-    "EncryptedSerializer": "reference/classes/langgraph_checkpoint_serde_encrypted.EncryptedSerializer.html",
-    "entrypoint.final": "reference/functions/langgraph_func.entrypoint.final.html",
-    "entrypoint": "reference/functions/langgraph_func.entrypoint.html",
-    "from_pycryptodome_aes": "reference/functions/langgraph_checkpoint_serde_encrypted.EncryptedSerializer.fromPycryptodomeAes.html",
+    "entrypoint.final": "reference/functions/langgraph.entrypoint.html#final",
+    "entrypoint": "reference/functions/langgraph.entrypoint.html",
    "getContextVariable": "https://v03.api.js.langchain.com/functions/_langchain_core.context.getContextVariable.html",
-    "get_state_history": "reference/functions/langgraph_CompiledStateGraph.getStateHistory.html",
-    "get_stream_writer": "reference/functions/langgraph_config.getStreamWriter.html",
-    "HumanInterrupt": "reference/classes/langgraph_prebuilt.HumanInterrupt.html",
-    "InjectedState": "reference/classes/langgraph_prebuilt.InjectedState.html",
-    "InMemorySaver": "reference/classes/langgraph_checkpoint_memory.InMemorySaver.html",
+    "get_state_history": "reference/classes/langgraph.CompiledStateGraph.html#getStateHistory",
+    "HumanInterrupt": "reference/interfaces/langgraph_prebuilt.HumanInterrupt.html",
    "interrupt": "reference/functions/langgraph.interrupt-2.html",
-    "CompiledStateGraph.invoke": "reference/functions/langgraph_CompiledStateGraph.invoke.html",
-    "JsonPlusSerializer": "reference/classes/langgraph_checkpoint_serde_jsonplus.JsonPlusSerializer.html",
-    "langgraph.json": "reference/configuration.html",
-    "LastValue": "reference/classes/langgraph_channels.LastValue.html",
+    "CompiledStateGraph.invoke": "reference/classes/langgraph.CompiledStateGraph.html#invoke",
+    "langgraph.json": "cloud/reference/cli/#configuration-file",
    "MemorySaver": "reference/classes/checkpoint.MemorySaver.html",
    "messagesStateReducer": "reference/functions/langgraph.messagesStateReducer.html",
-    "PostgresSaver": "reference/classes/langgraph_checkpoint_postgres.PostgresSaver.html",
+    "PostgresSaver": "reference/classes/checkpoint_postgres.PostgresSaver.html",
    "Pregel": "reference/classes/langgraph.Pregel.html",
-    "Pregel.stream": "reference/functions/langgraph_Pregel.stream.html",
+    "Pregel.stream": "reference/classes/langgraph.Pregel.html#stream",
    "pre_model_hook": "reference/functions/langgraph_prebuilt.createReactAgent.html",
-    "protocol": "reference/classes/langgraph_checkpoint_serde_base.SerializerProtocol.html",
+    "protocol": "reference/interfaces/checkpoint.SerializerProtocol.html",
    "Send": "reference/classes/langgraph.Send.html",
-    "SerializerProtocol": "reference/classes/langgraph_checkpoint_serde_base.SerializerProtocol.html",
-    "SqliteSaver": "reference/classes/langgraph_checkpoint_sqlite.SqliteSaver.html",
-    "START": "reference/constants.html#START",
-    "CompiledStateGraph.stream": "reference/functions/langgraph_CompiledStateGraph.stream.html",
-    "task": "reference/functions/langgraph_func.task.html",
-    "Topic": "reference/classes/langgraph_channels.Topic.html",
-    "update_state": "reference/functions/langgraph_CompiledStateGraph.updateState.html",
+    "SerializerProtocol": "reference/interfaces/checkpoint.SerializerProtocol.html",
+    "SqliteSaver": "reference/classes/checkpoint_sqlite.SqliteSaver.html",
+    "START": "reference/variables/langgraph.START.html",
+    "CompiledStateGraph.stream": "reference/classes/langgraph.CompiledStateGraph.html#stream",
+    "task": "reference/functions/langgraph.task.html",
+    ## TODO (hntrl): export Topic from langgraphjs
+    # "Topic": "reference/classes/langgraph_channels.Topic.html",
+    "update_state": "reference/classes/langgraph.CompiledStateGraph.html#updateState",
 }

 # TODO: Allow updating these to localhost for local development
@@ -88,12 +88,12 @@ REDIRECT_MAP = {
    "cloud/how-tos/human_in_the_loop_user_input.md": "cloud/how-tos/add-human-in-the-loop.md",
    "concepts/platform_architecture.md": "concepts/langgraph_cloud#architecture",
    # cloud streaming redirects
-    "cloud/how-tos/stream_values.md": "cloud/how-tos/streaming.md#stream-graph-state",
-    "cloud/how-tos/stream_updates.md": "cloud/how-tos/streaming.md#stream-graph-state",
-    "cloud/how-tos/stream_messages.md": "cloud/how-tos/streaming.md#messages",
-    "cloud/how-tos/stream_events.md": "cloud/how-tos/streaming.md#stream-events",
-    "cloud/how-tos/stream_debug.md": "cloud/how-tos/streaming.md#debug",
-    "cloud/how-tos/stream_multiple.md": "cloud/how-tos/streaming.md#stream-multiple-modes",
+    "cloud/how-tos/stream_values.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/how-tos/stream_updates.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/how-tos/stream_messages.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/how-tos/stream_events.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/how-tos/stream_debug.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/how-tos/stream_multiple.md": "https://docs.langchain.com/langgraph-platform/streaming",
    "cloud/concepts/streaming.md": "concepts/streaming.md",
    "agents/streaming.md": "how-tos/streaming.md",
    # prebuilt redirects
@@ -128,78 +128,84 @@ REDIRECT_MAP = {
    "cloud/how-tos/human_in_the_loop_breakpoint.md": "cloud/how-tos/add-human-in-the-loop.md",
    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.md",

-    # mintlify 
-    # "tutorials/auth/getting_started.md": "https://docs.langchain.com/langgraph-platform/",
-    # "tutorials/auth/resource_auth.md": "https://docs.langchain.com/langgraph-platform/",
-    # "tutorials/auth/add_auth_server.md": "https://docs.langchain.com/langgraph-platform/",
-    # "how-tos/use-remote-graph.md": "https://docs.langchain.com/langgraph-platform/",
-    # "how-tos/autogen-integration.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/use_stream_react.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/generative_ui_react.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/langgraph_platform.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/langgraph_components.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/langgraph_server.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/langgraph_data_plane.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/langgraph_control_plane.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/langgraph_cli.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/langgraph_studio.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/studio/quick_start.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/invoke_studio.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/studio/manage_assistants.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/threads_studio.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/iterate_graph_studio.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/studio/run_evals.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/clone_traces_studio.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/datasets_studio.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/sdk.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/plans.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/application_structure.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/scalability_and_resilience.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/auth.md": "https://docs.langchain.com/langgraph-platform/",
-    # "how-tos/auth/custom_auth.md": "https://docs.langchain.com/langgraph-platform/",
-    # "how-tos/auth/openapi_security.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/assistants.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/configuration_cloud.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/use_threads.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/background_run.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/same-thread.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/cron_jobs.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/stateless_runs.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/configurable_headers.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/double_texting.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/interrupt_concurrent.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/rollback_concurrent.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/reject_concurrent.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/enqueue_concurrent.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/concepts/webhooks.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/webhooks.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/concepts/cron_jobs.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/cron_jobs.md": "https://docs.langchain.com/langgraph-platform/",
-    # "how-tos/http/custom_lifespan.md": "https://docs.langchain.com/langgraph-platform/",
-    # "how-tos/http/custom_middleware.md": "https://docs.langchain.com/langgraph-platform/",
-    # "how-tos/http/custom_routes.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/concepts/data_storage_and_privacy.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/deployment/semantic_search.md": "https://docs.langchain.com/langgraph-platform/",
-    # "how-tos/ttl/configure_ttl.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/deployment_options.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/quick_start.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/deployment/setup.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/deployment/setup_pyproject.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/deployment/setup_javascript.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/deployment/custom_docker.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/deployment/graph_rebuild.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/langgraph_cloud.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/langgraph_self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/langgraph_self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/langgraph_standalone_container.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/deployment/cloud.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/deployment/self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/deployment/self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/deployment/standalone_container.md": "https://docs.langchain.com/langgraph-platform/",
-    # "concepts/server-mcp.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/human_in_the_loop_time_travel.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/add-human-in-the-loop.md": "https://docs.langchain.com/langgraph-platform/",
-    # "cloud/how-tos/streaming.md": "https://docs.langchain.com/langgraph-platform/",
+    # LGP mintlify migration redirects
+    "tutorials/auth/getting_started.md": "https://docs.langchain.com/langgraph-platform/auth",
+    "tutorials/auth/resource_auth.md": "https://docs.langchain.com/langgraph-platform/resource-auth",
+    "tutorials/auth/add_auth_server.md": "https://docs.langchain.com/langgraph-platform/add-auth-server",
+    "how-tos/use-remote-graph.md": "https://docs.langchain.com/langgraph-platform/use-remote-graph",
+    "how-tos/autogen-integration.md": "https://docs.langchain.com/langgraph-platform/autogen-integration",
+    "cloud/how-tos/use_stream_react.md": "https://docs.langchain.com/langgraph-platform/use-stream-react",
+    "cloud/how-tos/generative_ui_react.md": "https://docs.langchain.com/langgraph-platform/generative-ui-react",
+    "concepts/langgraph_platform.md": "https://docs.langchain.com/langgraph-platform/index",
+    "concepts/langgraph_components.md": "https://docs.langchain.com/langgraph-platform/components",
+    "concepts/langgraph_server.md": "https://docs.langchain.com/langgraph-platform/langgraph-server",
+    "concepts/langgraph_data_plane.md": "https://docs.langchain.com/langgraph-platform/data-plane",
+    "concepts/langgraph_control_plane.md": "https://docs.langchain.com/langgraph-platform/control-plane",
+    "concepts/langgraph_cli.md": "https://docs.langchain.com/langgraph-platform/langgraph-cli",
+    "concepts/langgraph_studio.md": "https://docs.langchain.com/langgraph-platform/langgraph-studio",
+    "cloud/how-tos/studio/quick_start.md": "https://docs.langchain.com/langgraph-platform/quick-start-studio",
+    "cloud/how-tos/invoke_studio.md": "https://docs.langchain.com/langgraph-platform/invoke-studio",
+    "cloud/how-tos/studio/manage_assistants.md": "https://docs.langchain.com/langgraph-platform/manage-assistants-studio",
+    "cloud/how-tos/threads_studio.md": "https://docs.langchain.com/langgraph-platform/threads-studio",
+    "cloud/how-tos/iterate_graph_studio.md": "https://docs.langchain.com/langgraph-platform/iterate-graph-studio",
+    "cloud/how-tos/studio/run_evals.md": "https://docs.langchain.com/langgraph-platform/run-evals-studio",
+    "cloud/how-tos/clone_traces_studio.md": "https://docs.langchain.com/langgraph-platform/clone-traces-studio",
+    "cloud/how-tos/datasets_studio.md": "https://docs.langchain.com/langgraph-platform/datasets-studio",
+    "concepts/sdk.md": "https://docs.langchain.com/langgraph-platform/sdk",
+    "concepts/plans.md": "https://docs.langchain.com/langgraph-platform/plans",
+    "concepts/application_structure.md": "https://docs.langchain.com/langgraph-platform/application-structure",
+    "concepts/scalability_and_resilience.md": "https://docs.langchain.com/langgraph-platform/scalability-and-resilience",
+    "concepts/auth.md": "https://docs.langchain.com/langgraph-platform/auth",
+    "how-tos/auth/custom_auth.md": "https://docs.langchain.com/langgraph-platform/custom-auth",
+    "how-tos/auth/openapi_security.md": "https://docs.langchain.com/langgraph-platform/openapi-security",
+    "concepts/assistants.md": "https://docs.langchain.com/langgraph-platform/assistants",
+    "cloud/how-tos/configuration_cloud.md": "https://docs.langchain.com/langgraph-platform/configuration-cloud",
+    "cloud/how-tos/use_threads.md": "https://docs.langchain.com/langgraph-platform/use-threads",
+    "cloud/how-tos/background_run.md": "https://docs.langchain.com/langgraph-platform/background-run",
+    "cloud/how-tos/same-thread.md": "https://docs.langchain.com/langgraph-platform/same-thread",
+    "cloud/how-tos/stateless_runs.md": "https://docs.langchain.com/langgraph-platform/stateless-runs",
+    "cloud/how-tos/configurable_headers.md": "https://docs.langchain.com/langgraph-platform/configurable-headers",
+    "concepts/double_texting.md": "https://docs.langchain.com/langgraph-platform/double-texting",
+    "cloud/how-tos/interrupt_concurrent.md": "https://docs.langchain.com/langgraph-platform/interrupt-concurrent",
+    "cloud/how-tos/rollback_concurrent.md": "https://docs.langchain.com/langgraph-platform/rollback-concurrent",
+    "cloud/how-tos/reject_concurrent.md": "https://docs.langchain.com/langgraph-platform/reject-concurrent",
+    "cloud/how-tos/enqueue_concurrent.md": "https://docs.langchain.com/langgraph-platform/enqueue-concurrent",
+    "cloud/concepts/webhooks.md": "https://docs.langchain.com/langgraph-platform/use-webhooks",
+    "cloud/how-tos/webhooks.md": "https://docs.langchain.com/langgraph-platform/use-webhooks",
+    "cloud/concepts/cron_jobs.md": "https://docs.langchain.com/langgraph-platform/cron-jobs",
+    "cloud/how-tos/cron_jobs.md": "https://docs.langchain.com/langgraph-platform/cron-jobs",
+    "how-tos/http/custom_lifespan.md": "https://docs.langchain.com/langgraph-platform/custom-lifespan",
+    "how-tos/http/custom_middleware.md": "https://docs.langchain.com/langgraph-platform/custom-middleware",
+    "how-tos/http/custom_routes.md": "https://docs.langchain.com/langgraph-platform/custom-routes",
+    "cloud/concepts/data_storage_and_privacy.md": "https://docs.langchain.com/langgraph-platform/data-storage-and-privacy",
+    "cloud/deployment/semantic_search.md": "https://docs.langchain.com/langgraph-platform/semantic-search",
+    "how-tos/ttl/configure_ttl.md": "https://docs.langchain.com/langgraph-platform/configure-ttl",
+    "concepts/deployment_options.md": "https://docs.langchain.com/langgraph-platform/deployment-options",
+    "cloud/quick_start.md": "https://docs.langchain.com/langgraph-platform/deployment-quickstart",
+    "cloud/deployment/setup.md": "https://docs.langchain.com/langgraph-platform/setup-app-requirements-txt",
+    "cloud/deployment/setup_pyproject.md": "https://docs.langchain.com/langgraph-platform/setup-pyproject",
+    "cloud/deployment/setup_javascript.md": "https://docs.langchain.com/langgraph-platform/setup-javascript",
+    "cloud/deployment/custom_docker.md": "https://docs.langchain.com/langgraph-platform/custom-docker",
+    "cloud/deployment/graph_rebuild.md": "https://docs.langchain.com/langgraph-platform/graph-rebuild",
+    "concepts/langgraph_cloud.md": "https://docs.langchain.com/langgraph-platform/cloud",
+    "concepts/langgraph_self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/self-hosted-data-plane",
+    "concepts/langgraph_self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/self-hosted-control-plane",
+    "concepts/langgraph_standalone_container.md": "https://docs.langchain.com/langgraph-platform/standalone-container",
+    "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-self-hosted-data-plane",
+    "cloud/deployment/self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-self-hosted-control-plane",
+    "cloud/deployment/standalone_container.md": "https://docs.langchain.com/langgraph-platform/deploy-standalone-container",
+    "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",
+    "cloud/deployment/egress.md": "https://docs.langchain.com/langgraph-platform/env-var",
+    "cloud/how-tos/streaming.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/reference/api/api_ref.md": "https://docs.langchain.com/langgraph-platform/server-api-ref",
+    "cloud/reference/langgraph_server_changelog.md": "https://docs.langchain.com/langgraph-platform/langgraph-server-changelog",
+    "cloud/reference/api/api_ref_control_plane.md": "https://docs.langchain.com/langgraph-platform/api-ref-control-plane",
+    "cloud/reference/cli.md": "https://docs.langchain.com/langgraph-platform/cli",
+    "cloud/reference/env_var.md": "https://docs.langchain.com/langgraph-platform/env-var",
+    "troubleshooting/studio.md": "https://docs.langchain.com/langgraph-platform/troubleshooting-studio",
 }


@@ -29,7 +29,7 @@ pip install -U langgraph "langchain[anthropic]"

 !!! info

-    LangChain is installed so the agent can call the [model](https://python.langchain.com/docs/integrations/chat/).
+    `langchain[anthropic]` is installed so the agent can call the [model](https://python.langchain.com/docs/integrations/chat/).

 :::

@@ -41,7 +41,7 @@ npm install @langchain/langgraph @langchain/core @langchain/anthropic

 !!! info

-    LangChain is installed so the agent can call the [model](https://js.langchain.com/docs/integrations/chat/).
+    `@langchain/core` `@langchain/anthropic` are installed so the agent can call the [model](https://js.langchain.com/docs/integrations/chat/).

 :::

@@ -145,6 +145,76 @@ const agent = createReactAgent({

 :::

+:::python
+
+### Dynamic model selection
+
+Pass a callable function to `create_react_agent` to dynamically select the model at runtime. This is useful for scenarios where you want to choose a model based on user input, configuration settings, or other runtime conditions.
+
+The selector function must return a chat model. If you're using tools, you must bind the tools to the model within the selector function.
+
+  ```python
+from dataclasses import dataclass
+from typing import Literal
+from langchain.chat_models import init_chat_model
+from langchain_core.language_models import BaseChatModel
+from langchain_core.tools import tool
+from langgraph.prebuilt import create_react_agent
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langgraph.runtime import Runtime
+
+@tool
+def weather() -> str:
+    """Returns the current weather conditions."""
+    return "It's nice and sunny."
+
+
+# Define the runtime context
+@dataclass
+class CustomContext:
+    provider: Literal["anthropic", "openai"]
+
+# Initialize models
+openai_model = init_chat_model("openai:gpt-4o")
+anthropic_model = init_chat_model("anthropic:claude-sonnet-4-20250514")
+
+
+# Selector function for model choice
+def select_model(state: AgentState, runtime: Runtime[CustomContext]) -> BaseChatModel:
+    if runtime.context.provider == "anthropic":
+        model = anthropic_model
+    elif runtime.context.provider == "openai":
+        model = openai_model
+    else:
+        raise ValueError(f"Unsupported provider: {runtime.context.provider}")
+
+    # With dynamic model selection, you must bind tools explicitly
+    return model.bind_tools([weather])
+
+
+# Create agent with dynamic model selection
+agent = create_react_agent(select_model, tools=[weather])
+
+# Invoke with context to select model
+output = agent.invoke(
+    {
+        "messages": [
+            {
+                "role": "user",
+                "content": "Which model is handling this?",
+            }
+        ]
+    },
+    context=CustomContext(provider="openai"),
+)
+
+print(output["messages"][-1].text())
+```
+
+!!! version-added "New in LangGraph v0.6"
+
+:::
+
 ## Advanced model configuration

 ### Disable streaming
@@ -159,7 +159,7 @@ function generateCodeSnippet({ tools, pre, post, response }) {
  if (post) lines.push("    post_model_hook=post_model_hook,");
  if (response) lines.push("    response_format=ResponseFormat,");

-  lines.push(")", "", "agent.get_graph().draw_mermaid_png()");
+  lines.push(")", "", "# Visualize the graph", "# For Jupyter or GUI environments:", "agent.get_graph().draw_mermaid_png()", "", "# To save PNG to file:", "png_data = agent.get_graph().draw_mermaid_png()", "with open(\"graph.png\", \"wb\") as f:", "    f.write(png_data)", "", "# For terminal/ASCII output:", "agent.get_graph().draw_ascii()");

  return lines.join("\n");
 }
@@ -21,7 +21,6 @@ Before deploying, review the [conceptual guide for the Standalone Container](../

            `<database_name_1>` and `database_name_2` are different databases within the same instance, but `<hostname_1>` is shared. **The same database cannot be used for separate deployments**.

-    1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_server.md#server-versions)) LangSmith API key. This will be used to authenticate ONCE at server start up.
    1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#licensing)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
    1. `LANGSMITH_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGSMITH_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
 1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
@@ -1,5 +1,7 @@
 # LangGraph Server Changelog

+> **Note:** This changelog is no longer actively maintained. For the most up-to-date LangGraph Server changelog, please visit our new documentation site: [LangGraph Server Changelog](https://docs.langchain.com/langgraph-platform/langgraph-server-changelog#langgraph-server-changelog)
+
 [LangGraph Server](../../concepts/langgraph_server.md) is an API platform for creating and managing agent-based applications. It provides built-in persistence, a task queue, and supports deploying, configuring, and running assistants (agentic workflows) at scale. This changelog documents all notable updates, features, and fixes to LangGraph Server releases.

 ---
@@ -35,7 +35,8 @@ LangGraph Platform provides different security defaults:
 - Can be customized with your auth handler

 !!! note "Custom auth"
-Custom auth **is supported** for all plans in LangGraph Platform.
+
+   Custom auth **is supported** for all plans in LangGraph Platform.

 ### Self-Hosted

@@ -43,11 +44,6 @@ Custom auth **is supported** for all plans in LangGraph Platform.
 - Complete flexibility to implement your security model
 - You control all aspects of authentication and authorization

-!!! note "Custom auth"
-
-    Custom auth is supported for **Enterprise** self-hosted deployments.
-    Standalone Container (Lite) deployments do not support custom auth natively.
-
 ## System Architecture

 A typical authentication setup involves three main components:
@@ -7,10 +7,7 @@ search:

 ## Free deployment

-There are two free options for deploying LangGraph applications via the LangGraph Server:
-
-1. [Local](../tutorials/langgraph-platform/local-server.md): Deploy for local testing and development. 
-1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more than 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.
+[Local](../tutorials/langgraph-platform/local-server.md): Deploy for local testing and development.

 ## Production deployment

@@ -33,8 +30,7 @@ A quick comparison:
 | **CI/CD** | Managed internally by platform | Managed externally by you | Managed externally by you | Managed externally by you |
 | **Data/compute residency** | LangChain's cloud | Your cloud | Your cloud | Your cloud |
 | **LangSmith compatibility** | Trace to LangSmith SaaS | Trace to LangSmith SaaS | Trace to Self-Hosted LangSmith | Optional tracing |
-| **[Server version compatibility](../concepts/langgraph_server.md#server-versions)** | Enterprise | Enterprise | Enterprise | Lite, Enterprise |
-| **[Pricing](https://www.langchain.com/pricing-langgraph-platform)** | Plus | Enterprise | Enterprise | Developer |
+| **[Pricing](https://www.langchain.com/pricing-langgraph-platform)** | Plus | Enterprise | Enterprise | Enterprise |

 ## Cloud SaaS

@@ -51,6 +51,51 @@ For some examples of pitfalls to avoid, see the [Common Pitfalls](./functional_a
 how to structure your code using **tasks** to avoid these issues. The same principles apply to the @[StateGraph (Graph API)][StateGraph].
 :::

+## Durability modes
+
+LangGraph supports three durability modes that allow you to balance performance and data consistency based on your application's requirements. The durability modes, from least to most durable, are as follows:
+
+- [`"exit"`](#exit)
+- [`"async"`](#async)
+- [`"sync"`](#sync)
+
+A higher durability mode add more overhead to the workflow execution.
+
+!!! version-added "Added in v0.6.0"
+
+    Use the `durability` parameter instead of `checkpoint_during` (deprecated in v0.6.0) for persistence policy management:
+    
+    * `durability="async"` replaces `checkpoint_during=True`
+    * `durability="exit"` replaces `checkpoint_during=False`
+    
+    for persistence policy management, with the following mapping:
+
+    * `checkpoint_during=True` -> `durability="async"`
+    * `checkpoint_during=False` -> `durability="exit"`
+
+
+### `"exit"`
+Changes are persisted only when graph execution completes (either successfully or with an error). This provides the best performance for long-running graphs but means intermediate state is not saved, so you cannot recover from mid-execution failures or interrupt the graph execution.
+
+### `"async"`
+Changes are persisted asynchronously while the next step executes. This provides good performance and durability, but there's a small risk that checkpoints might not be written if the process crashes during execution.
+
+### `"sync"`
+Changes are persisted synchronously before the next step starts. This ensures that every checkpoint is written before continuing execution, providing high durability at the cost of some performance overhead.
+
+You can specify the durability mode when calling any graph execution method:
+
+:::python
+
+```python
+graph.stream(
+    {"input": "test"}, 
+    durability="sync"
+)
+```
+
+:::
+
 ## Using tasks in nodes

 If a [node](./low_level.md#nodes) contains multiple operations, you may find it easier to convert each operation into a **task** rather than refactor the operations into individual nodes.
@@ -13,21 +13,6 @@ Use LangGraph Server to create and manage [assistants](assistants.md), [threads]
  
    For detailed information on the API endpoints and data models, see [LangGraph Platform API reference docs](../cloud/reference/api/api_ref.html).

-## Server versions
-
-There are two versions of LangGraph Server:
-
- `Lite` is a limited version of the LangGraph Server that you can run locally or in a self-hosted manner (up to 1 million [nodes executed](../concepts/faq.md#what-does-nodes-executed-mean-for-langgraph-platform-usage) per year).
- `Enterprise` is the full version of the LangGraph Server. To use the `Enterprise` version, you must acquire a license key that you will need to specify when running the Docker image. To acquire a license key, please email sales@langchain.dev.
-
-Feature Differences:
-
-|       | Lite       | Enterprise |
-|-------|------------|------------|
-| [Cron Jobs](../cloud/concepts/cron_jobs.md) |❌|✅|
-| [Custom Authentication](../concepts/auth.md) |❌|✅|
-| [Deployment options](../concepts/deployment_options.md) | Standalone container | Cloud SaaS, Self-Hosted Data Plane, Self-Hosted Control Plane, Standalone container
-
 ## Application structure

 To deploy a LangGraph Server application, you need to specify the graph(s) you want to deploy, as well as any relevant configuration settings, such as dependencies and environment variables.
@@ -34,12 +34,3 @@ The Standalone Container deployment option supports deploying data plane infrast
 ### Docker

 The Standalone Container deployment option supports deploying data plane infrastructure to any Docker-supported compute platform.
-
-## Lite vs. Enterprise
-
-The Standalone Container deployment option supports both of the [server versions](../concepts/langgraph_server.md#langgraph-server):
-
- The `Lite` version is free, but has limited features.
- The `Enterprise` version has custom pricing and is fully featured.
-
-For more details on feature difference, see [LangGraph Server](../concepts/langgraph_server.md#server-versions).
@@ -88,8 +88,6 @@ Typically, all graph nodes communicate with a single schema. This means that the

 It is possible to have nodes write to private state channels inside the graph for internal node communication. We can simply define a private schema, `PrivateState`.

-See [this guide](../how-tos/graph-api.ipynb#pass-private-state-between-nodes) for more detail.
-
 It is also possible to define explicit input and output schemas for a graph. In these cases, we define an "internal" schema that contains _all_ keys relevant to graph operations. But, we also define `input` and `output` schemas that are sub-sets of the "internal" schema to constrain the input and output of the graph. See [this guide](../how-tos/graph-api.md#define-input-and-output-schemas) for more detail.

 Let's look at an example:
@@ -473,7 +471,7 @@ const builder = new StateGraph(State);

 :::

-Behind the scenes, functions are converted to [RunnableLambda](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableLambda.html#langchain_core.runnables.base.RunnableLambda)s, which add batch and async support to your function, along with native tracing and debugging.
+Behind the scenes, functions are converted to [RunnableLambda](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html)s, which add batch and async support to your function, along with native tracing and debugging.

 If you add a node to a graph without specifying a name, it will be given a default name equivalent to the function name.

@@ -701,7 +699,8 @@ graph.addConditionalEdges("nodeA", routingFunction, {
 :::

 !!! tip
-Use [`Command`](#command) instead of conditional edges if you want to combine state updates and routing in a single function.
+
+    Use [`Command`](#command) instead of conditional edges if you want to combine state updates and routing in a single function.

 ### Entry Point

@@ -820,7 +819,6 @@ def my_node(state: State) -> Command[Literal["my_other_node"]]:
        return Command(update={"foo": "baz"}, goto="my_other_node")
 ```

-Check out this [how-to guide](../how-tos/graph-api.ipynb#combine-control-flow-and-state-updates-with-command) for an end-to-end example of how to use `Command`.
 :::

 :::js
@@ -860,7 +858,6 @@ builder.addNode("myNode", myNode, {
 });
 ```

-Check out this [how-to guide](../how-tos/graph-api.ipynb#combine-control-flow-and-state-updates-with-command) for an end-to-end example of how to use `Command`.
 :::

 !!! important
@@ -10,48 +10,3 @@ Install the `langchain-mcp-adapters` library to use MCP tools in LangGraph:
 pip install langchain-mcp-adapters
 ```

-## Authenticate to an MCP server
-
-You can set up [custom authentication middleware](../how-tos/auth/custom_auth.md) to authenticate a user with an MCP server to get access to user-scoped tools within your LangGraph Platform deployment.
-
-!!! note
-
-    Custom authentication is a LangGraph Platform feature.
-
-An example architecture for this flow:
-
-```mermaid
-sequenceDiagram
-  %% Actors
-  participant ClientApp as Client
-  participant AuthProv  as Auth Provider
-  participant LangGraph as LangGraph Backend
-  participant SecretStore as Secret Store
-  participant MCPServer as MCP Server
-
-  %% Platform login / AuthN
-  ClientApp  ->> AuthProv: 1. Login (username / password)
-  AuthProv   -->> ClientApp: 2. Return token
-  ClientApp  ->> LangGraph: 3. Request with token
-
-  Note over LangGraph: 4. Validate token (@auth.authenticate)
-  LangGraph  -->> AuthProv: 5. Fetch user info
-  AuthProv   -->> LangGraph: 6. Confirm validity
-
-  %% Fetch user tokens from secret store
-  LangGraph  ->> SecretStore: 6a. Fetch user tokens
-  SecretStore -->> LangGraph: 6b. Return tokens
-
-  Note over LangGraph: 7. Apply access control (@auth.on.*)
-
-  %% MCP round-trip
-  Note over LangGraph: 8. Build MCP client with user token
-  LangGraph  ->> MCPServer: 9. Call MCP tool (with header)
-  Note over MCPServer: 10. MCP validates header and runs tool
-  MCPServer  -->> LangGraph: 11. Tool response
-
-  %% Return to caller
-  LangGraph  -->> ClientApp: 12. Return resources / tool output
-```
-
-For more information, see [MCP endpoint in LangGraph Server](../concepts/server-mcp.md).
@@ -10,7 +10,7 @@ search:
 LangGraph Platform is a solution for deploying agentic applications in production.
 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 [Standalone Container (Lite)](./deployment_options.md) deployment option.
+- **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).

@@ -19,8 +19,8 @@ There are three different plans for using it.

 |                                                                  | Developer                                   | Plus                                                  | Enterprise                                          |
 |------------------------------------------------------------------|---------------------------------------------|-------------------------------------------------------|-----------------------------------------------------|
-| Deployment Options                                               | Standalone Container (Lite)                          | Cloud SaaS                                         | <ul><li>Cloud SaaS</li><li>Self-Hosted Data Plane</li><li>Self-Hosted Control Plane</li><li>Standalone Container (Enterprise)</li></ul> |
-| Usage                                                            | Free, limited to 1M [nodes executed](../concepts/faq.md#what-does-nodes-executed-mean-for-langgraph-platform-usage) per year | See [Pricing](https://www.langchain.com/langgraph-platform-pricing) | Custom                                              |
+| Deployment Options                                               | Local                          | Cloud SaaS                                         | <ul><li>Cloud SaaS</li><li>Self-Hosted Data Plane</li><li>Self-Hosted Control Plane</li><li>Standalone Container</li></ul> |
+| Usage                                                            | Free | See [Pricing](https://www.langchain.com/langgraph-platform-pricing) | Custom                                              |
 | APIs for retrieving and updating state and conversational history | ✅                                           | ✅                                                     | ✅                                                   |
 | APIs for retrieving and updating long-term memory                | ✅                                           | ✅                                                     | ✅                                                   |
 | Horizontally scalable task queues and servers                    | ✅                                           | ✅                                                     | ✅                                                   |
@@ -9,15 +9,4 @@ The pages in this section provide end-to-end examples for the following topics:
 - [Agent Supervisor](../tutorials/multi_agent/agent_supervisor.md): Build a supervisor agent that can manage a team of agents.
 - [SQL agent](../tutorials/sql/sql-agent.md): Build a SQL agent that can execute SQL queries and return the results.
 - [Prebuilt chat UI](../agents/ui.md): Use a prebuilt chat UI to interact with any LangGraph agent.
- [Graph runs in LangSmith](../how-tos/run-id-langsmith.md): Use LangSmith to track and analyze graph runs.
-
-## LangGraph Platform
-
- [Set up custom authentication](../tutorials/auth/getting_started.md): Set up custom authentication for your LangGraph application.
- [Make conversations private](../tutorials/auth/resource_auth.md): Make conversations private by using resource-based authentication.
- [Connect an authentication provider](../tutorials/auth/add_auth_server.md): Connect an authentication provider to your LangGraph application.
- [Rebuild graph at runtime](../cloud/deployment/graph_rebuild.md): Rebuild a graph at runtime.
- [Use RemoteGraph](../how-tos/use-remote-graph.md): Use RemoteGraph to deploy your LangGraph application to a remote server.
- [Deploy CrewAI, AutoGen, and other frameworks](../how-tos/autogen-integration.md): Deploy CrewAI, AutoGen, and other frameworks with LangGraph.
- [Integrate LangGraph into a React app](../cloud/how-tos/use_stream_react.md)
- [Implement Generative User Interfaces with LangGraph](../cloud/how-tos/generative_ui_react.md)
+- [Graph runs in LangSmith](../how-tos/run-id-langsmith.md): Use LangSmith to track and analyze graph runs.
@@ -31,15 +31,3 @@ These capabilities are available in both LangGraph OSS and the LangGraph Platfor
 - [MCP](../concepts/mcp.md): Use MCP servers in a LangGraph graph.
 - [Evaluation](../agents/evals.md): Use LangSmith to evaluate your graph's performance.

-## Platform-only capabilities
-
-These capabilities are only available in [LangGraph Platform](../concepts/langgraph_platform.md).
-
- [Authentication and access control](../concepts/auth.md): Authenticate and authorize users to access a LangGraph graph.
- [Assistants](../concepts/assistants.md): Build assistants that can be used to interact with a LangGraph graph.
- [Double-texting](../concepts/double_texting.md): Handle double-texting (consecutive messages before a first response is returned) in a LangGraph graph.
- [Webhooks](../cloud/concepts/webhooks.md): Send webhooks to a LangGraph graph.
- [Cron jobs](../cloud/concepts/cron_jobs.md): Schedule jobs to run at a specific time.
- [Server customization](../how-tos/http/custom_lifespan.md): Customize the server that runs a LangGraph graph.
- [Data management](../cloud/concepts/data_storage_and_privacy.md): Manage data in a LangGraph graph.
- [Deployment](../concepts/deployment_options.md): Deploy a LangGraph graph to a server.
@@ -11,13 +11,13 @@

 ???+ note "Support by deployment type"

-    Custom auth is supported for all deployments in the **managed LangGraph Platform**, as well as **Enterprise** self-hosted plans. It is not supported for **Lite** self-hosted plans.
+    Custom auth is supported for all deployments in the **managed LangGraph Platform**, as well as **Enterprise** self-hosted plans.

 This guide shows how to add custom authentication to your LangGraph Platform application. This guide applies to both LangGraph Platform and self-hosted deployments. It does not apply to isolated usage of the LangGraph open source library in your own custom server.

 !!! note

-    Custom auth is supported for all **managed LangGraph Platform** deployments, as well as **Enterprise** self-hosted plans. It is not supported for **Lite** self-hosted plans.
+    Custom auth is supported for all **managed LangGraph Platform** deployments, as well as **Enterprise** self-hosted plans.

 ## Add custom authentication to your deployment

@@ -145,7 +145,7 @@ def my_node(state, config):
 By default, if you add custom authorization on your resources, this will also apply to interactions made from the Studio. If you want, you can handle logged-in Studio users differently by checking [is_studio_user()](../../reference/functions/sdk_auth.isStudioUser.html).

 !!! note
-`is_studio_user` was added in version 0.1.73 of the langgraph-sdk. If you're on an older version, you can still check whether `isinstance(ctx.user, StudioUser)`.
+    `is_studio_user` was added in version 0.1.73 of the langgraph-sdk. If you're on an older version, you can still check whether `isinstance(ctx.user, StudioUser)`.

 ```python
 from langgraph_sdk.auth import is_studio_user, Auth
@@ -11,7 +11,8 @@ pip install -U langgraph
 ```

 !!! tip "Set up LangSmith for better debugging"
-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 in the [docs](https://docs.smith.langchain.com).
+
+    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 in the [docs](https://docs.smith.langchain.com).

 ## Define and update state

@@ -57,7 +58,8 @@ def node(state: State):
 This node simply appends a message to our message list, and populates an extra field.

 !!! important
-Nodes should return updates to the state directly, instead of mutating the state.
+
+    Nodes should return updates to the state directly, instead of mutating the state.

 Let's next define a simple graph containing this node. We use [StateGraph](../concepts/low_level.md#stategraph) to define a graph that operates on this state. We then use [add_node](../concepts/low_level.md#nodes) populate our graph.

@@ -341,7 +343,12 @@ In our examples, we typically use a python-native `TypedDict` or [`dataclass`](h

 Here, we'll see how a [Pydantic BaseModel](https://docs.pydantic.dev/latest/api/base_model/) can be used for `state_schema` to add run-time validation on **inputs**.

-!!! note "Known Limitations" - Currently, the output of the graph will **NOT** be an instance of a pydantic model. - Run-time validation only occurs on inputs into nodes, not on the outputs. - The validation error trace from pydantic does not show which node the error arises in. - Pydantic's recursive validation can be slow. For performance-sensitive applications, you may want to consider using a `dataclass` instead.
+!!! note "Known Limitations" 
+
+    - Currently, the output of the graph will **NOT** be an instance of a pydantic model. 
+    - Run-time validation only occurs on inputs into nodes, not on the outputs. 
+    - The validation error trace from pydantic does not show which node the error arises in. 
+    - Pydantic's recursive validation can be slow. For performance-sensitive applications, you may want to consider using a `dataclass` instead.

 ```python
 from langgraph.graph import StateGraph, START, END
@@ -564,7 +571,8 @@ print(graph.invoke({}, context={"my_runtime_value": "b"}))
 ```

 ??? example "Extended example: specifying LLM at runtime"
-Below we demonstrate a practical example in which we configure what LLM to use at runtime. We will use both OpenAI and Anthropic models.
+
+    Below we demonstrate a practical example in which we configure what LLM to use at runtime. We will use both OpenAI and Anthropic models.

    ```python
    from dataclasses import dataclass
@@ -611,7 +619,8 @@ Below we demonstrate a practical example in which we configure what LLM to use a
    ```

 ??? example "Extended example: specifying model and system message at runtime"
-Below we demonstrate a practical example in which we configure two parameters: the LLM and system message to use at runtime.
+
+    Below we demonstrate a practical example in which we configure two parameters: the LLM and system message to use at runtime.

    ```python
    from dataclasses import dataclass
@@ -696,7 +705,8 @@ By default, the `retry_on` parameter uses the `default_retry_on` function, which
 In addition, for exceptions from popular http request libraries such as `requests` and `httpx` it only retries on 5xx status codes.

 ??? example "Extended example: customizing retry policies"
-Consider an example in which we are reading from a SQL database. Below we pass two different retry policies to nodes:
+
+    Consider an example in which we are reading from a SQL database. Below we pass two different retry policies to nodes:

    ```python
    import sqlite3
@@ -759,7 +769,8 @@ graph = builder.compile(cache=InMemoryCache())
 ## Create a sequence of steps

 !!! info "Prerequisites"
-This guide assumes familiarity with the above section on [state](#define-and-update-state).
+
+    This guide assumes familiarity with the above section on [state](#define-and-update-state).

 Here we demonstrate how to construct a simple sequence of steps. We will show:

@@ -835,7 +846,8 @@ def step_3(state: State):
 ```

 !!! note
-Note that when issuing updates to the state, each node can just specify the value of the key it wishes to update.
+
+    Note that when issuing updates to the state, each node can just specify the value of the key it wishes to update.

    By default, this will **overwrite** the value of the corresponding key. You can also use [reducers](../concepts/low_level.md#reducers) to control how updates are processed— for example, you can append successive updates to a key instead. See [this section](#process-state-updates-with-reducers) for more detail.

@@ -862,7 +874,8 @@ builder.add_edge("step_2", "step_3")
 ```

 !!! tip "Specifying custom names"
-You can specify custom names for nodes using `.add_node`:
+
+    You can specify custom names for nodes using `.add_node`:

    ```python
    builder.add_node("my_node", step_1)
@@ -908,7 +921,8 @@ Note that:
 - The third node populated a different value.

 !!! tip "Built-in shorthand"
-`langgraph>=0.2.46` includes a built-in short-hand `add_sequence` for adding node sequences. You can compile the same graph as follows:
+
+    `langgraph>=0.2.46` includes a built-in short-hand `add_sequence` for adding node sequences. You can compile the same graph as follows:

    ```python
    # highlight-next-line
@@ -990,12 +1004,14 @@ Adding "D" to ['A', 'B', 'C']
 ```

 !!! note
-In the above example, nodes `"b"` and `"c"` are executed concurrently in the same [superstep](../concepts/low_level.md#graphs). Because they are in the same step, node `"d"` executes after both `"b"` and `"c"` are finished.
+
+    In the above example, nodes `"b"` and `"c"` are executed concurrently in the same [superstep](../concepts/low_level.md#graphs). Because they are in the same step, node `"d"` executes after both `"b"` and `"c"` are finished.

    Importantly, updates from a parallel superstep may not be ordered consistently. If you need a consistent, predetermined ordering of updates from a parallel superstep, you should write the outputs to a separate field in the state together with a value with which to order them.

 ??? note "Exception handling?"
-LangGraph executes nodes within [supersteps](../concepts/low_level.md#graphs), meaning that while parallel branches are executed in parallel, the entire superstep is **transactional**. If any of these branches raises an exception, **none** of the updates are applied to the state (the entire superstep errors).
+
+    LangGraph executes nodes within [supersteps](../concepts/low_level.md#graphs), meaning that while parallel branches are executed in parallel, the entire superstep is **transactional**. If any of these branches raises an exception, **none** of the updates are applied to the state (the entire superstep errors).

    Importantly, when using a [checkpointer](../concepts/persistence.md), results from successful nodes within a superstep are saved, and don't repeat when resumed.

@@ -1149,7 +1165,8 @@ Adding "C" to ['A']
 ```

 !!! tip
-Your conditional edges can route to multiple destination nodes. For example:
+
+    Your conditional edges can route to multiple destination nodes. For example:

    ```python
    def route_bc_or_cd(state: State) -> Sequence[str]:
@@ -1234,7 +1251,8 @@ You can also set the graph recursion limit when invoking or streaming the graph.
 Let's consider a simple graph with a loop to better understand how these mechanisms work.

 !!! tip
-To return the last value of your state instead of receiving a recursion limit error, see the [next section](#impose-a-recursion-limit).
+
+    To return the last value of your state instead of receiving a recursion limit error, see the [next section](#impose-a-recursion-limit).

 When creating a loop, you can include a conditional edge that specifies a termination condition:

@@ -1551,7 +1569,8 @@ result = await graph.ainvoke({"messages": [input_message]}) # (3)!
 3. Use async invocations on the graph object itself.

 !!! tip "Async streaming"
-See the [streaming guide](./streaming.md) for examples of streaming with async.
+
+    See the [streaming guide](./streaming.md) for examples of streaming with async.

 ## Combine control flow and state updates with `Command`

@@ -1621,7 +1640,8 @@ graph = builder.compile()
 ```

 !!! important
-You might have noticed that we used `Command` as a return type annotation, e.g. `Command[Literal["node_b", "node_c"]]`. This is necessary for the graph rendering and tells LangGraph that `node_a` can navigate to `node_b` and `node_c`.
+
+    You might have noticed that we used `Command` as a return type annotation, e.g. `Command[Literal["node_b", "node_c"]]`. This is necessary for the graph rendering and tells LangGraph that `node_a` can navigate to `node_b` and `node_c`.

 ```python
 from IPython.display import display, Image
@@ -1629,7 +1649,7 @@ from IPython.display import display, Image
 display(Image(graph.get_graph().draw_mermaid_png()))
 ```

-![Command-based graph navigation](assets/graph_api_image_6.png)
+![Command-based graph navigation](assets/graph_api_image_11.png)

 If we run the graph multiple times, we'd see it take different paths (A -> B or A -> C) based on the random choice in node A.

@@ -1658,7 +1678,8 @@ def my_node(state: State) -> Command[Literal["my_other_node"]]:
 Let's demonstrate this using the above example. We'll do so by changing `node_a` in the above example into a single-node graph that we'll add as a subgraph to our parent graph.

 !!! important "State updates with `Command.PARENT`"
-When you send updates from a subgraph node to a parent graph node for a key that's shared by both parent and subgraph [state schemas](../concepts/low_level.md#schema), you **must** define a [reducer](../concepts/low_level.md#reducers) for the key you're updating in the parent graph state. See the example below.
+
+    When you send updates from a subgraph node to a parent graph node for a key that's shared by both parent and subgraph [state schemas](../concepts/low_level.md#schema), you **must** define a [reducer](../concepts/low_level.md#reducers) for the key you're updating in the parent graph state. See the example below.

 ```python
 import operator
@@ -1741,7 +1762,8 @@ def lookup_user_info(tool_call_id: Annotated[str, InjectedToolCallId], config: R
 ```

 !!! important
-You MUST include `messages` (or any state key used for the message history) in `Command.update` when returning `Command` from a tool and the list of messages in `messages` MUST contain a `ToolMessage`. This is necessary for the resulting message history to be valid (LLM providers require AI messages with tool calls to be followed by the tool result messages).
+
+    You MUST include `messages` (or any state key used for the message history) in `Command.update` when returning `Command` from a tool and the list of messages in `messages` MUST contain a `ToolMessage`. This is necessary for the resulting message history to be valid (LLM providers require AI messages with tool calls to be followed by the tool result messages).

 If you are using tools that update state via `Command`, we recommend using prebuilt [`ToolNode`](../reference/agents.md#langgraph.prebuilt.tool_node.ToolNode) which automatically handles tools returning `Command` objects and propagates them to the graph state. If you're writing a custom node that calls tools, you would need to manually propagate `Command` objects returned by the tools as the update from the node.

@@ -1436,6 +1436,7 @@ await agent.invoke(
 from typing_extensions import TypedDict

 from langgraph.config import get_store
+from langchain_core.runnables import RunnableConfig
 from langgraph.prebuilt import create_react_agent
 from langgraph.store.memory import InMemoryStore

@@ -2797,4 +2798,6 @@ await checkpointer.deleteThread(threadId);
 ## Prebuilt memory tools

 **LangMem** is a LangChain-maintained library that offers tools for managing long-term memories in your agent. See the [LangMem documentation](https://langchain-ai.github.io/langmem/) for usage examples.
+
 :::
+
@@ -57,7 +57,7 @@ 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][InjectedState] annotation. 
+1. Access the [state](../concepts/low_level.md#state) of the agent that is calling the handoff tool using the @[InjectedState] annotation. 
 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.
@@ -172,6 +172,82 @@ await agent.invoke({

 :::

+:::python
+
+### Dynamically select tools
+
+Configure tool availability at runtime based on context:
+
+```python
+from dataclasses import dataclass
+from typing import Literal
+
+from langchain.chat_models import init_chat_model
+from langchain_core.tools import tool
+
+from langgraph.prebuilt import create_react_agent
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langgraph.runtime import Runtime
+
+
+@dataclass
+class CustomContext:
+    tools: list[Literal["weather", "compass"]]
+
+
+@tool
+def weather() -> str:
+    """Returns the current weather conditions."""
+    return "It's nice and sunny."
+
+
+@tool
+def compass() -> str:
+    """Returns the direction the user is facing."""
+    return "North"
+
+model = init_chat_model("anthropic:claude-sonnet-4-20250514")
+
+# highlight-next-line
+def configure_model(state: AgentState, runtime: Runtime[CustomContext]):
+    """Configure the model with tools based on runtime context."""
+    selected_tools = [
+        tool
+        for tool in [weather, compass]
+        if tool.name in runtime.context.tools
+    ]
+    return model.bind_tools(selected_tools)
+
+
+agent = create_react_agent(
+    # Dynamically configure the model with tools based on runtime context
+    # highlight-next-line
+    configure_model,
+    # Initialize with all tools available
+    # highlight-next-line
+    tools=[weather, compass]
+)
+
+output = agent.invoke(
+    {
+        "messages": [
+            {
+                "role": "user",
+                "content": "Who are you and what tools do you have access to?",
+            }
+        ]
+    },
+    # highlight-next-line
+    context=CustomContext(tools=["weather"]),  # Only enable the weather tool
+)
+
+print(output["messages"][-1].text())
+```
+
+!!! version-added "New in langgraph>=0.6"
+
+:::
+
 ## Use in a workflow

 If you are writing a custom workflow, you will need to:
@@ -1495,6 +1571,7 @@ const saveUserInfo = tool(

    from langchain_core.tools import tool
    from langgraph.config import get_store
+    from langchain_core.runnables import RunnableConfig
    from langgraph.prebuilt import create_react_agent
    from langgraph.store.memory import InMemoryStore

@@ -10,7 +10,7 @@
 - [Implementing Human-in-the-Loop Controls in LangGraph](https://langchain-ai.github.io/langgraph/tutorials/get-started/4-human-in-the-loop/): This page provides a comprehensive guide on adding human-in-the-loop controls to LangGraph workflows, enabling agents to pause execution for human input. It details the use of the `interrupt` function to facilitate user feedback and outlines the steps to integrate a `human_assistance` tool into a chatbot. Additionally, the tutorial covers graph compilation, visualization, and resuming execution with human input.
 - [Customizing State in LangGraph for Enhanced Chatbot Functionality](https://langchain-ai.github.io/langgraph/tutorials/get-started/5-customize-state/): This tutorial guides you through the process of adding custom fields to the state in LangGraph, enabling complex behaviors in your chatbot without relying solely on message lists. You will learn how to implement human-in-the-loop controls to verify information before it is stored in the state. By the end of this tutorial, you will have a deeper understanding of state management and how to enhance your chatbot's capabilities.
 - [Implementing Time Travel in LangGraph Chatbots](https://langchain-ai.github.io/langgraph/tutorials/get-started/6-time-travel/): This page provides a comprehensive guide on utilizing the time travel functionality in LangGraph to enhance chatbot interactions. It covers how to rewind, add steps, and replay the state history of a chatbot, allowing users to explore different outcomes and fix mistakes. Additionally, it includes code snippets and practical examples to help developers implement these features effectively.
- [LangGraph Deployment Options](https://langchain-ai.github.io/langgraph/tutorials/deployment/): This page outlines the various options available for deploying LangGraph applications, including local testing and different cloud-based solutions. It details free deployment methods such as Local and Standalone Container (Lite), as well as production options like Cloud SaaS and self-hosted solutions. Each deployment method is linked to further documentation for in-depth guidance.
+- [LangGraph Deployment Options](https://langchain-ai.github.io/langgraph/tutorials/deployment/): This page outlines the various options available for deploying LangGraph applications, including local testing and different cloud-based solutions. It details free deployment methods such as Local, as well as production options like Cloud SaaS and self-hosted solutions. Each deployment method is linked to further documentation for in-depth guidance.
 - [Agent Development with LangGraph](https://langchain-ai.github.io/langgraph/agents/overview/): This page provides an overview of agent development using LangGraph, highlighting its prebuilt components and capabilities for building agent-based applications. It explains the structure of an agent, key features such as memory integration and human-in-the-loop control, and outlines the package ecosystem available for developers. With LangGraph, users can focus on application logic while leveraging robust infrastructure for state management and feedback.
 - [Guide to Running Agents in LangGraph](https://langchain-ai.github.io/langgraph/agents/run_agents/): This page provides a comprehensive overview of how to execute agents in LangGraph, detailing both synchronous and asynchronous methods. It covers input and output formats, streaming capabilities, and how to manage execution limits to prevent infinite loops. Additionally, it includes code examples and links to further resources for deeper understanding.
 - [Streaming Data in LangGraph](https://langchain-ai.github.io/langgraph/agents/streaming/): This page provides an overview of streaming data types in LangGraph, including agent progress, LLM tokens, and custom updates. It includes code examples for both synchronous and asynchronous streaming methods. Additionally, it covers how to stream multiple modes and disable streaming when necessary.
@@ -73,7 +73,7 @@
 - [Integrating Semantic Search in LangGraph](https://langchain-ai.github.io/langgraph/cloud/deployment/semantic_search/): This guide provides step-by-step instructions on how to implement semantic search in your LangGraph deployment. It covers prerequisites, configuration of the store, and usage examples for searching memories and documents by semantic similarity. Additionally, it includes information on using custom embeddings and querying via the LangGraph SDK.
 - [Configuring Time-to-Live (TTL) in LangGraph Applications](https://langchain-ai.github.io/langgraph/how-tos/ttl/configure_ttl/): This guide provides detailed instructions on how to configure Time-to-Live (TTL) settings for checkpoints and store items in LangGraph applications. It covers the necessary configurations in the `langgraph.json` file, including strategies for managing data lifecycle and memory. Additionally, it explains how to combine TTL configurations and override them at runtime.
 - [LangGraph Authentication & Access Control Overview](https://langchain-ai.github.io/langgraph/concepts/auth/): This page provides a comprehensive guide to the authentication and authorization mechanisms within the LangGraph Platform. It explains the core concepts of authentication versus authorization, outlines default security models, and details the system architecture involved in user identity management. Additionally, it covers implementation examples for authentication and authorization handlers, along with common access patterns and supported resources.
- [Custom Authentication Setup for LangGraph Platform](https://langchain-ai.github.io/langgraph/how-tos/auth/custom_auth/): This guide provides step-by-step instructions on how to implement custom authentication in your LangGraph Platform application. It covers the necessary prerequisites, implementation details, configuration updates, and client connection methods. The guide is applicable to both managed and Enterprise self-hosted deployments, but not to Lite self-hosted plans.
+- [Custom Authentication Setup for LangGraph Platform](https://langchain-ai.github.io/langgraph/how-tos/auth/custom_auth/): This guide provides step-by-step instructions on how to implement custom authentication in your LangGraph Platform application. It covers the necessary prerequisites, implementation details, configuration updates, and client connection methods. The guide is applicable to both managed and Enterprise self-hosted deployments.
 - [Documenting API Authentication in OpenAPI for LangGraph](https://langchain-ai.github.io/langgraph/how-tos/auth/openapi_security/): This guide provides instructions on how to customize the security schema for your LangGraph Platform API documentation using OpenAPI. It covers default security schemes for both LangGraph Platform and self-hosted deployments, as well as how to implement custom authentication. Additionally, it includes examples for OAuth2 and API key authentication, along with testing procedures.
 - [Managing Assistants in LangGraph](https://langchain-ai.github.io/langgraph/concepts/assistants/): This page provides an overview of how to create and manage assistants within the LangGraph Platform, which allows for separate configuration of agents without altering the core graph logic. It covers the prerequisites, configuration options, and versioning of assistants, highlighting their role in optimizing agent performance for different tasks. Additionally, it includes links to relevant API references and how-to guides for further assistance.
 - [Managing Assistants in LangGraph](https://langchain-ai.github.io/langgraph/cloud/how-tos/configuration_cloud/): This documentation page provides a comprehensive guide on how to create, configure, and manage assistants using the LangGraph SDK and Platform UI. It includes code examples in Python and JavaScript, as well as instructions for creating new versions and using previous versions of assistants. Additionally, it covers the process of utilizing assistants in various environments.
@@ -112,7 +112,7 @@
 - [Deploying a Self-Hosted Data Plane](https://langchain-ai.github.io/langgraph/cloud/deployment/self_hosted_data_plane/): This page provides a comprehensive guide on deploying a Self-Hosted Data Plane using Kubernetes and Amazon ECS. It outlines the prerequisites, setup steps, and configuration details necessary for a successful deployment. Additionally, it highlights the current beta status of this deployment option.
 - [Self-Hosted Control Plane Deployment Guide](https://langchain-ai.github.io/langgraph/concepts/langgraph_self_hosted_control_plane/): This page provides an overview of the Self-Hosted Control Plane deployment option, currently in beta. It outlines the requirements, architecture, and compute platforms supported for deploying the control and data planes in your cloud environment. Additionally, it includes important links and resources for managing your self-hosted infrastructure.
 - [Deploying a Self-Hosted Control Plane](https://langchain-ai.github.io/langgraph/cloud/deployment/self_hosted_control_plane/): This page provides a comprehensive guide on deploying a Self-Hosted Control Plane using Kubernetes. It outlines the prerequisites, setup steps, and configuration details necessary for a successful deployment. Additionally, it highlights the beta status of this deployment option and includes links to relevant resources for further assistance.
- [Deploying LangGraph Server with Standalone Container](https://langchain-ai.github.io/langgraph/concepts/langgraph_standalone_container/): This page provides a comprehensive guide on deploying a LangGraph Server using the Standalone Container option. It outlines the architecture, supported compute platforms, and differences between Lite and Enterprise server versions. Users will find essential information on managing the data plane infrastructure without a control plane.
+- [Deploying LangGraph Server with Standalone Container](https://langchain-ai.github.io/langgraph/concepts/langgraph_standalone_container/): This page provides a comprehensive guide on deploying a LangGraph Server using the Standalone Container option. It outlines the architecture, supported compute platforms, and Enterprise server version features. Users will find essential information on managing the data plane infrastructure without a control plane.
 - [Deploying a Standalone Container with LangGraph](https://langchain-ai.github.io/langgraph/cloud/deployment/standalone_container/): This documentation provides a comprehensive guide on deploying a standalone container for the LangGraph application. It covers prerequisites, environment variable configurations, and deployment methods using Docker and Docker Compose. Additionally, it includes instructions for deploying on Kubernetes using Helm.
 - [Scalability and Resilience of LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/scalability_and_resilience/): This page provides an overview of the scalability and resilience features of the LangGraph Platform. It details how the platform handles server and queue scalability, as well as the mechanisms in place for ensuring resilience during both graceful and hard shutdowns. Additionally, it covers the resilience strategies employed for Postgres and Redis to maintain service availability.
 - [LangGraph Platform Plans Overview](https://langchain-ai.github.io/langgraph/concepts/plans/): This page provides an overview of the different plans available for the LangGraph Platform, including Developer, Plus, and Enterprise options. Each plan offers varying deployment options, usage limits, and features tailored to different user needs. For detailed pricing and related resources, links to additional documentation are also included.
@@ -49,9 +49,8 @@ Higher-level abstractions for common workflows, agents, and other patterns.

 Tools for deploying and connecting to the LangGraph Platform.

- [CLI](../cloud/reference/cli.md): Command-line interface for building and deploying LangGraph Platform applications.
- [Server API](../cloud/reference/api/api_ref.md): REST API for the LangGraph Server.
 - [SDK (Python)](../cloud/reference/sdk/python_sdk_ref.md): Python SDK for interacting with instances of the LangGraph Server.
 - [SDK (JS/TS)](../cloud/reference/sdk/js_ts_sdk_ref.md): JavaScript/TypeScript SDK for interacting with instances of the LangGraph Server.
 - [RemoteGraph](remote_graph.md): `Pregel` abstraction for connecting to LangGraph Server instances.
- [Environment variables](../cloud/reference/env_var.md): Supported configuration variables when deploying with the LangGraph Platform.
+
+See the [LangGraph Platform reference](https://docs.langchain.com/langgraph-platform/reference-overview) for more reference documentation.
@@ -21,15 +21,9 @@ See the [local server](../../tutorials/langgraph-platform/local-server.md) docs

 If you would like a fast managed environment, consider the [Cloud SaaS](../../concepts/langgraph_cloud.md) deployment option. This requires no additional license key.

-#### For Standalone Container (Lite)
+#### For Standalone Container

-If your deployment is unlikely to see more than 1 million node executions per year and don't need Crons and other enterprise features, consider the [Standalone Container](../../concepts/deployment_options.md) deployment option.
-
-You can deploy with Standalone Container by setting a valid `LANGSMITH_API_KEY` in your environment (e.g., in the `.env` file referenced by `langgraph.json`) and building a Docker image. The API key must be associated with an account on a **Plus** plan or greater.
-
-#### For Standalone Container (Enterprise)
-
-For full self-hosting, set the `LANGGRAPH_CLOUD_LICENSE_KEY` environment variable. If you are interested in an enterprise license key, please contact the LangChain support team.
+For self-hosting, set the `LANGGRAPH_CLOUD_LICENSE_KEY` environment variable. If you are interested in an enterprise license key, please contact the LangChain support team.

 For more information on deployment options and their features, see the [Deployment Options](../../concepts/deployment_options.md) documentation.

@@ -38,12 +32,7 @@ For more information on deployment options and their features, see the [Deployme

 If you have confirmed that you would like to self-host LangGraph Platform, please verify your credentials.

-#### For Standalone Container (Lite)
-
-1. Confirm that you have provided a working `LANGSMITH_API_KEY` environment variable in your deployment environment or `.env` file
-2. Confirm the provided API key is associated with an account on a **Plus** or **Enterprise** plan (or equivalent)
-
-#### For Standalone Container (Enterprise)
+#### For Standalone Container

 1. Confirm that you have provided a working `LANGGRAPH_CLOUD_LICENSE_KEY` environment variable in your deployment environment or `.env` file
 2. Confirm the key is still valid and has not surpassed its expiration date
@@ -11,11 +11,4 @@ Errors referenced below will have an `lc_error_code` property corresponding to o
 - [INVALID_CONCURRENT_GRAPH_UPDATE](./INVALID_CONCURRENT_GRAPH_UPDATE.md)
 - [INVALID_GRAPH_NODE_RETURN_VALUE](./INVALID_GRAPH_NODE_RETURN_VALUE.md)
 - [MULTIPLE_SUBGRAPHS](./MULTIPLE_SUBGRAPHS.md)
- [INVALID_CHAT_HISTORY](./INVALID_CHAT_HISTORY.md)
-
-## LangGraph Platform
-
-These guides provide troubleshooting information for errors that are specific to the LangGraph Platform.
-
- [INVALID_LICENSE](./INVALID_LICENSE.md)
- [Studio Errors](../studio.md)
+- [INVALID_CHAT_HISTORY](./INVALID_CHAT_HISTORY.md)
@@ -17,7 +17,7 @@ Create a `MemorySaver` checkpointer:
 :::python

 ```python
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver

 memory = InMemorySaver()
 ```
@@ -447,3 +447,4 @@ const graph = new StateGraph(State)
 ## Next steps

 In the next tutorial, you will [add human-in-the-loop to the chatbot](./4-human-in-the-loop.md) to handle situations where it may need guidance or verification before proceeding.
+
@@ -85,124 +85,80 @@ Let's [run the agent](../../agents/run_agents.md) to verify that it behaves as e

 !!! note "We'll use `pretty_print_messages` helper to render the streamed agent outputs nicely"

-    ```python
-    from langchain_core.messages import convert_to_messages
-    
-    
-    def pretty_print_message(message, indent=False):
-        pretty_message = message.pretty_repr(html=True)
-        if not indent:
-            print(pretty_message)
-            return
-    
-        indented = "\n".join("\t" + c for c in pretty_message.split("\n"))
-        print(indented)
-    
-    
-    def pretty_print_messages(update, last_message=False):
-        is_subgraph = False
-        if isinstance(update, tuple):
-            ns, update = update
-            # skip parent graph updates in the printouts
-            if len(ns) == 0:
-                return
-    
-            graph_id = ns[-1].split(":")[0]
-            print(f"Update from subgraph {graph_id}:")
-            print("\n")
-            is_subgraph = True
-    
-        for node_name, node_update in update.items():
-            update_label = f"Update from node {node_name}:"
-            if is_subgraph:
-                update_label = "\t" + update_label
-    
-            print(update_label)
-            print("\n")
-    
-            messages = convert_to_messages(node_update["messages"])
-            if last_message:
-                messages = messages[-1:]
-    
-            for m in messages:
-                pretty_print_message(m, indent=is_subgraph)
-            print("\n")
-    ```
-
-```python
-from langchain_core.messages import convert_to_messages
+  ```python
+  from langchain_core.messages import convert_to_messages


-def pretty_print_message(message, indent=False):
-    pretty_message = message.pretty_repr(html=True)
-    if not indent:
-        print(pretty_message)
-        return
+  def pretty_print_message(message, indent=False):
+      pretty_message = message.pretty_repr(html=True)
+      if not indent:
+          print(pretty_message)
+          return

-    indented = "\n".join("\t" + c for c in pretty_message.split("\n"))
-    print(indented)
+      indented = "\n".join("\t" + c for c in pretty_message.split("\n"))
+      print(indented)


-def pretty_print_messages(update, last_message=False):
-    is_subgraph = False
-    if isinstance(update, tuple):
-        ns, update = update
-        # skip parent graph updates in the printouts
-        if len(ns) == 0:
-            return
+  def pretty_print_messages(update, last_message=False):
+      is_subgraph = False
+      if isinstance(update, tuple):
+          ns, update = update
+          # skip parent graph updates in the printouts
+          if len(ns) == 0:
+              return

-        graph_id = ns[-1].split(":")[0]
-        print(f"Update from subgraph {graph_id}:")
-        print("\n")
-        is_subgraph = True
+          graph_id = ns[-1].split(":")[0]
+          print(f"Update from subgraph {graph_id}:")
+          print("\n")
+          is_subgraph = True

-    for node_name, node_update in update.items():
-        update_label = f"Update from node {node_name}:"
-        if is_subgraph:
-            update_label = "\t" + update_label
+      for node_name, node_update in update.items():
+          update_label = f"Update from node {node_name}:"
+          if is_subgraph:
+              update_label = "\t" + update_label

-        print(update_label)
-        print("\n")
+          print(update_label)
+          print("\n")

-        messages = convert_to_messages(node_update["messages"])
-        if last_message:
-            messages = messages[-1:]
+          messages = convert_to_messages(node_update["messages"])
+          if last_message:
+              messages = messages[-1:]

-        for m in messages:
-            pretty_print_message(m, indent=is_subgraph)
-        print("\n")
-```
+          for m in messages:
+              pretty_print_message(m, indent=is_subgraph)
+          print("\n")
+  ```

-```python
-for chunk in research_agent.stream(
-    {"messages": [{"role": "user", "content": "who is the mayor of NYC?"}]}
-):
-    pretty_print_messages(chunk)
-```
+  ```python
+  for chunk in research_agent.stream(
+      {"messages": [{"role": "user", "content": "who is the mayor of NYC?"}]}
+  ):
+      pretty_print_messages(chunk)
+  ```

-**Output:**
-```
-Update from node agent:
+  **Output:**
+  ```
+  Update from node agent:


-================================== Ai Message ==================================
-Name: research_agent
-Tool Calls:
-  tavily_search (call_U748rQhQXT36sjhbkYLSXQtJ)
- Call ID: call_U748rQhQXT36sjhbkYLSXQtJ
-  Args:
-    query: current mayor of New York City
-    search_depth: basic
+  ================================== Ai Message ==================================
+  Name: research_agent
+  Tool Calls:
+    tavily_search (call_U748rQhQXT36sjhbkYLSXQtJ)
+   Call ID: call_U748rQhQXT36sjhbkYLSXQtJ
+    Args:
+      query: current mayor of New York City
+      search_depth: basic


-Update from node tools:
+  Update from node tools:


-================================= Tool Message ==================================
-Name: tavily_search
+  ================================= Tool Message ==================================
+  Name: tavily_search

-{"query": "current mayor of New York City", "follow_up_questions": null, "answer": null, "images": [], "results": [{"title": "List of mayors of New York City - Wikipedia", "url": "https://en.wikipedia.org/wiki/List_of_mayors_of_New_York_City", "content": "The mayor of New York City is the chief executive of the Government of New York City, as stipulated by New York City's charter.The current officeholder, the 110th in the sequence of regular mayors, is Eric Adams, a member of the Democratic Party.. During the Dutch colonial period from 1624 to 1664, New Amsterdam was governed by the Director of Netherland.", "score": 0.9039154, "raw_content": null}, {"title": "Office of the Mayor | Mayor's Bio | City of New York - NYC.gov", "url": "https://www.nyc.gov/office-of-the-mayor/bio.page", "content": "Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. He gave voice to a diverse coalition of working families in all five boroughs and is leading the fight to bring back New York City's economy, reduce inequality, improve", "score": 0.8405867, "raw_content": null}, {"title": "Eric Adams - Wikipedia", "url": "https://en.wikipedia.org/wiki/Eric_Adams", "content": "Eric Leroy Adams (born September 1, 1960) is an American politician and former police officer who has served as the 110th mayor of New York City since 2022. Adams was an officer in the New York City Transit Police and then the New York City Police Department (```
-```
+  {"query": "current mayor of New York City", "follow_up_questions": null, "answer": null, "images": [], "results": [{"title": "List of mayors of New York City - Wikipedia", "url": "https://en.wikipedia.org/wiki/List_of_mayors_of_New_York_City", "content": "The mayor of New York City is the chief executive of the Government of New York City, as stipulated by New York City's charter.The current officeholder, the 110th in the sequence of regular mayors, is Eric Adams, a member of the Democratic Party.. During the Dutch colonial period from 1624 to 1664, New Amsterdam was governed by the Director of Netherland.", "score": 0.9039154, "raw_content": null}, {"title": "Office of the Mayor | Mayor's Bio | City of New York - NYC.gov", "url": "https://www.nyc.gov/office-of-the-mayor/bio.page", "content": "Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. He gave voice to a diverse coalition of working families in all five boroughs and is leading the fight to bring back New York City's economy, reduce inequality, improve", "score": 0.8405867, "raw_content": null}, {"title": "Eric Adams - Wikipedia", "url": "https://en.wikipedia.org/wiki/Eric_Adams", "content": "Eric Leroy Adams (born September 1, 1960) is an American politician and former police officer who has served as the 110th mayor of New York City since 2022. Adams was an officer in the New York City Transit Police and then the New York City Police Department (```
+  ```

 ### Math agent

@@ -809,4 +765,4 @@ Update from subgraph research_agent:
 	Name: tavily_search
 	
 	{"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 ```
-``` 
+``` 
@@ -52,6 +52,103 @@ theme:
 plugins:
  - search:
      separator: '[\s\u200b\-,:!=\[\]()"`/]+|\.(?!\d)|&[lg]t;'
+  - exclude-search:
+      exclude:
+        - additional-resources/index.md
+        - agents/prebuilt.md
+        - cloud/concepts/cron_jobs.md
+        - cloud/concepts/data_storage_and_privacy.md
+        - cloud/concepts/webhooks.md
+        - cloud/deployment/cloud.md
+        - cloud/deployment/custom_docker.md
+        - cloud/deployment/egress.md
+        - cloud/deployment/graph_rebuild.md
+        - cloud/deployment/self_hosted_control_plane.md
+        - cloud/deployment/self_hosted_data_plane.md
+        - cloud/deployment/semantic_search.md
+        - cloud/deployment/setup_javascript.md
+        - cloud/deployment/setup_pyproject.md
+        - cloud/deployment/setup.md
+        - cloud/deployment/standalone_container.md
+        - cloud/how-tos/add-human-in-the-loop.md
+        - cloud/how-tos/background_run.md
+        - cloud/how-tos/clone_traces_studio.md
+        - cloud/how-tos/configurable_headers.md
+        - cloud/how-tos/configuration_cloud.md
+        - cloud/how-tos/cron_jobs.md
+        - cloud/how-tos/datasets_studio.md
+        - cloud/how-tos/enqueue_concurrent.md
+        - cloud/how-tos/generative_ui_react.md
+        - cloud/how-tos/human_in_the_loop_time_travel.md
+        - cloud/how-tos/interrupt_concurrent.md
+        - cloud/how-tos/invoke_studio.md
+        - cloud/how-tos/iterate_graph_studio.md
+        - cloud/how-tos/reject_concurrent.md
+        - cloud/how-tos/rollback_concurrent.md
+        - cloud/how-tos/same-thread.md
+        - cloud/how-tos/stateless_runs.md
+        - cloud/how-tos/streaming.md
+        - cloud/how-tos/studio/manage_assistants.md
+        - cloud/how-tos/studio/quick_start.md
+        - cloud/how-tos/studio/run_evals.md
+        - cloud/how-tos/threads_studio.md
+        - cloud/how-tos/use_stream_react.md
+        - cloud/how-tos/use_threads.md
+        - cloud/how-tos/webhooks.md
+        - cloud/quick_start.md
+        - cloud/reference/api/api_ref_control_plane.md
+        - cloud/reference/api/api_ref.md
+        - cloud/reference/cli.md
+        - cloud/reference/env_var.md
+        - cloud/reference/langgraph_server_changelog.md
+        - cloud/reference/sdk/js_ts_sdk_ref.md
+        - concepts/application_structure.md
+        - concepts/assistants.md
+        - concepts/auth.md
+        - concepts/deployment_options.md
+        - concepts/double_texting.md
+        - concepts/faq.md
+        - concepts/langgraph_cli.md
+        - concepts/langgraph_cloud.md
+        - concepts/langgraph_components.md
+        - concepts/langgraph_control_plane.md
+        - concepts/langgraph_data_plane.md
+        - concepts/langgraph_platform.md
+        - concepts/langgraph_self_hosted_control_plane.md
+        - concepts/langgraph_self_hosted_data_plane.md
+        - concepts/langgraph_server.md
+        - concepts/langgraph_standalone_container.md
+        - concepts/langgraph_studio.md
+        - concepts/plans.md
+        - concepts/scalability_and_resilience.md
+        - concepts/sdk.md
+        - concepts/server-mcp.md
+        - concepts/template_applications.md
+        - concepts/why-langgraph.md
+        - examples/index.md
+        - guides/index.md
+        - how-tos/auth/custom_auth.md
+        - how-tos/auth/openapi_security.md
+        - how-tos/autogen-integration.md
+        - how-tos/http/custom_lifespan.md
+        - how-tos/http/custom_middleware.md
+        - how-tos/http/custom_routes.md
+        - how-tos/ttl/configure_ttl.md
+        - how-tos/use-remote-graph.md
+        - index.md
+        - reference/index.md
+        - snippets/chat_model_tabs.md
+        - troubleshooting/errors/GRAPH_RECURSION_LIMIT.md
+        - troubleshooting/errors/index.md
+        - troubleshooting/errors/INVALID_CHAT_HISTORY.md
+        - troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE.md
+        - troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE.md
+        - troubleshooting/errors/INVALID_LICENSE.md
+        - troubleshooting/errors/MULTIPLE_SUBGRAPHS.md
+        - troubleshooting/studio.md
+        - tutorials/auth/add_auth_server.md
+        - tutorials/auth/getting_started.md
+        - tutorials/auth/resource_auth.md
  - tags
  - include-markdown
  - mkdocstrings:
@@ -123,7 +220,6 @@ nav:
      - Streaming:
          - Overview: concepts/streaming.md
          - Stream outputs: how-tos/streaming.md
-          - Use Server API: cloud/how-tos/streaming.md
      - Persistence:
          - Overview: concepts/persistence.md
      - Durable execution:
@@ -141,11 +237,9 @@ nav:
      - Human-in-the-loop:
          - Overview: concepts/human_in_the_loop.md
          - Add human intervention: how-tos/human_in_the_loop/add-human-in-the-loop.md
-          - Use Server API: cloud/how-tos/add-human-in-the-loop.md
      - Time travel:
          - Overview: concepts/time-travel.md
          - Use time travel: how-tos/human_in_the_loop/time-travel.md
-          - Use Server API: cloud/how-tos/human_in_the_loop_time_travel.md
      - Subgraphs:
          - Overview: concepts/subgraphs.md
          - Use subgraphs: how-tos/subgraph.md
@@ -156,88 +250,10 @@ nav:
      - MCP:
          - Overview: concepts/mcp.md
          - Use MCP: agents/mcp.md
-          - Server API: concepts/server-mcp.md
      - Tracing:
          - Overview: concepts/tracing.md
          - Enable tracing: how-tos/enable-tracing.md
          - Evaluate performance: agents/evals.md
-    - Platform-only capabilities:
-      - LangGraph Platform:
-          - Overview: concepts/langgraph_platform.md
-          - Components:
-            - Overview: concepts/langgraph_components.md
-            - LangGraph Server:
-              - Overview: concepts/langgraph_server.md
-              - Data plane: concepts/langgraph_data_plane.md
-              - Control plane: concepts/langgraph_control_plane.md
-            - LangGraph CLI: concepts/langgraph_cli.md
-            - LangGraph Studio: 
-              - Overview: concepts/langgraph_studio.md
-              - Quickstart: cloud/how-tos/studio/quick_start.md
-              - cloud/how-tos/invoke_studio.md
-              - cloud/how-tos/studio/manage_assistants.md
-              - cloud/how-tos/threads_studio.md
-              - cloud/how-tos/iterate_graph_studio.md
-              - cloud/how-tos/studio/run_evals.md
-              - cloud/how-tos/clone_traces_studio.md
-              - cloud/how-tos/datasets_studio.md
-            - LangGraph SDK: concepts/sdk.md
-          - Plans & pricing: concepts/plans.md
-          - Application structure: concepts/application_structure.md
-          - Scalability & resilience: concepts/scalability_and_resilience.md
-      - Authentication & access control:
-          - Overview: concepts/auth.md
-          - how-tos/auth/custom_auth.md
-          - how-tos/auth/openapi_security.md
-      - Assistants:
-          - Overview: concepts/assistants.md
-          - cloud/how-tos/configuration_cloud.md
-          - Threads: cloud/how-tos/use_threads.md
-          - Runs:
-            - cloud/how-tos/background_run.md
-            - cloud/how-tos/same-thread.md
-            - cloud/how-tos/cron_jobs.md
-            - cloud/how-tos/stateless_runs.md
-            - cloud/how-tos/configurable_headers.md
-      - Double-texting: 
-          - Overview: concepts/double_texting.md
-          - cloud/how-tos/interrupt_concurrent.md
-          - cloud/how-tos/rollback_concurrent.md
-          - cloud/how-tos/reject_concurrent.md
-          - cloud/how-tos/enqueue_concurrent.md
-      - Webhooks:
-          - Overview: cloud/concepts/webhooks.md
-          - Use webhooks: cloud/how-tos/webhooks.md
-      - Cron jobs:
-          - Overview: cloud/concepts/cron_jobs.md
-          - cloud/how-tos/cron_jobs.md
-      - Server customization:
-          - how-tos/http/custom_lifespan.md
-          - how-tos/http/custom_middleware.md
-          - how-tos/http/custom_routes.md
-      - Data management:
-        - cloud/concepts/data_storage_and_privacy.md
-        - Add semantic search: cloud/deployment/semantic_search.md
-        - Add TTLs: how-tos/ttl/configure_ttl.md
-      - Deployment:
-          - Overview: concepts/deployment_options.md
-          - Quickstart: cloud/quick_start.md
-          - Set up your application:
-            - Use requirements.txt: cloud/deployment/setup.md
-            - Use pyproject.toml: cloud/deployment/setup_pyproject.md
-            - Use JavaScript: cloud/deployment/setup_javascript.md
-            - Use custom Docker: cloud/deployment/custom_docker.md
-            - Rebuild graph at runtime: cloud/deployment/graph_rebuild.md
-          - Deployment options:
-            - Cloud SaaS: concepts/langgraph_cloud.md
-            - Self-Hosted Data Plane: concepts/langgraph_self_hosted_data_plane.md
-            - Self-Hosted Control Plane: concepts/langgraph_self_hosted_control_plane.md
-            - Standalone Container: concepts/langgraph_standalone_container.md
-          - Deploy to production:
-            - Cloud SaaS: cloud/deployment/cloud.md
-            - Self-Hosted Data Plane: cloud/deployment/self_hosted_data_plane.md
-            - Self-Hosted Control Plane: cloud/deployment/self_hosted_control_plane.md
-            - Standalone Container: cloud/deployment/standalone_container.md

  - Reference:
    - reference/index.md
@@ -260,14 +276,9 @@ nav:
      - Swarm: reference/swarm.md
      - MCP Adapters: reference/mcp.md
    - LangGraph Platform:
-      - Server API: cloud/reference/api/api_ref.md
-      - Server changelog: cloud/reference/langgraph_server_changelog.md
-      - Control Plane API: cloud/reference/api/api_ref_control_plane.md
-      - CLI: cloud/reference/cli.md
      - SDK (Python): cloud/reference/sdk/python_sdk_ref.md
      - SDK (JS/TS): https://langchain-ai.github.io/langgraphjs/reference/modules/sdk.html
      - RemoteGraph: reference/remote_graph.md
-      - Environment variables: cloud/reference/env_var.md

  - Examples:
    - examples/index.md
@@ -277,16 +288,6 @@ nav:
    - SQL agent: tutorials/sql/sql-agent.md
    - Prebuilt chat UI: agents/ui.md
    - Graph runs in LangSmith: how-tos/run-id-langsmith.md
-    - LangGraph Platform:
-      - Authentication:
-        - tutorials/auth/getting_started.md
-        - tutorials/auth/resource_auth.md
-        - tutorials/auth/add_auth_server.md
-      - Use RemoteGraph: how-tos/use-remote-graph.md
-      - Deploy CrewAI, AutoGen, and other frameworks: how-tos/autogen-integration.md
-      - Front-end and generative UI:
-        - Integrate LangGraph into a React app: cloud/how-tos/use_stream_react.md
-        - Implement generative UI with LangGraph: cloud/how-tos/generative_ui_react.md

  - Additional resources:
    - additional-resources/index.md
@@ -305,7 +306,6 @@ nav:
          - troubleshooting/errors/MULTIPLE_SUBGRAPHS.md
          - troubleshooting/errors/INVALID_CHAT_HISTORY.md
          - troubleshooting/errors/INVALID_LICENSE.md
-        - LangGraph Studio: troubleshooting/studio.md
    

 markdown_extensions:
@@ -360,5 +360,5 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
 {% endblock %}

 {% block announce %}
-LangGraph Platform docs are moving! Find the LangGraph Platform docs at the new <a href="https://docs.langchain.com/langgraph-platform" target="_blank">LangChain Docs</a> site!
+<strong>LangGraph Platform docs have moved!</strong> Find the LangGraph Platform docs at the new <a href="https://docs.langchain.com/langgraph-platform" target="_blank">LangChain Docs</a> site.
 {% endblock %}
@@ -39,6 +39,7 @@ docs = [
    "markdown-callouts",
    "markdown-include",
    "mkdocs-exclude",
+    "mkdocs-exclude-search",
    "psycopg[binary]",
    "psycopg-pool",
    "pygments-ansi-color",
@@ -2337,7 +2337,7 @@ wheels = [

 [[package]]
 name = "langgraph"
-version = "0.6.1"
+version = "0.6.2"
 source = { editable = "../libs/langgraph" }
 dependencies = [
    { name = "langchain-core" },
@@ -2524,6 +2524,7 @@ docs = [
    { name = "markdown-include" },
    { name = "mkdocs" },
    { name = "mkdocs-exclude" },
+    { name = "mkdocs-exclude-search" },
    { name = "mkdocs-git-committers-plugin-2" },
    { name = "mkdocs-include-markdown-plugin" },
    { name = "mkdocs-material", extra = ["imaging"] },
@@ -2595,6 +2596,7 @@ docs = [
    { name = "markdown-include" },
    { name = "mkdocs" },
    { name = "mkdocs-exclude" },
+    { name = "mkdocs-exclude-search" },
    { name = "mkdocs-git-committers-plugin-2" },
    { name = "mkdocs-include-markdown-plugin", specifier = ">=7.1.6" },
    { name = "mkdocs-material", extras = ["imaging"] },
@@ -2641,7 +2643,7 @@ test = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "0.6.1"
+version = "0.6.2"
 source = { editable = "../libs/prebuilt" }
 dependencies = [
    { name = "langchain-core" },
@@ -3030,6 +3032,18 @@ dependencies = [
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/54/b5/3a8e289282c9e8d7003f8a2f53d673d4fdaa81d493dc6966092d9985b6fc/mkdocs-exclude-1.0.2.tar.gz", hash = "sha256:ba6fab3c80ddbe3fd31d3e579861fd3124513708271180a5f81846da8c7e2a51", size = 6751, upload-time = "2019-02-20T23:34:12.81Z" }

+[[package]]
+name = "mkdocs-exclude-search"
+version = "0.6.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mkdocs" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/1d/52/8243589d294cf6091c1145896915fe50feea0e91d64d843942d0175770c2/mkdocs-exclude-search-0.6.6.tar.gz", hash = "sha256:3cdff1b9afdc1b227019cd1e124f401453235b92153d60c0e5e651a76be4f044", size = 9501, upload-time = "2023-12-03T22:58:21.259Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3b/ef/9af45ffb1bdba684a0694922abae0bb771e9777aba005933f838b7f1bcea/mkdocs_exclude_search-0.6.6-py3-none-any.whl", hash = "sha256:2b4b941d1689808db533fe4a6afba75ce76c9bab8b21d4e31efc05fd8c4e0a4f", size = 7821, upload-time = "2023-12-03T22:58:19.355Z" },
+]
+
 [[package]]
 name = "mkdocs-get-deps"
 version = "0.2.0"
@@ -4,6 +4,7 @@ import asyncio
 import enum
 import inspect
 import sys
+import warnings
 from collections.abc import (
    AsyncIterator,
    Awaitable,
@@ -303,6 +304,16 @@ class RunnableCallable(Runnable):
            if typ != (ANY_TYPE,) and p.annotation not in typ:
                # A specific type is required, but the function annotation does
                # not match the expected type.
+
+                # If this is a config parameter with incorrect typing, emit a warning
+                # because we used to support any type but are moving towards more correct typing
+                if kw == "config" and p.annotation != inspect.Parameter.empty:
+                    warnings.warn(
+                        f"The 'config' parameter should be typed as 'RunnableConfig' or "
+                        f"'RunnableConfig | None', not '{p.annotation}'. ",
+                        UserWarning,
+                        stacklevel=4,
+                    )
                continue

            # If the kwarg is accepted by the function, store the key / runtime attribute to inject
@@ -91,7 +91,7 @@ class GraphInterrupt(GraphBubbleUp):

@deprecated(
    "NodeInterrupt is deprecated. Please use `langgraph.types.interrupt` instead.",
-    stacklevel=2,
+    category=None,
 )
 class NodeInterrupt(GraphInterrupt):
    """Raised by a node to interrupt execution.
@@ -335,7 +335,10 @@ class entrypoint(Generic[ContextT]):
        of the previous invocation on the same thread id.

        ```python
-        from langgraph.checkpoint.memory import InMemorySaver
+        from typing import Optional
+
+        from langgraph.checkpoint.memory import MemorySaver
+
        from langgraph.func import entrypoint

        @entrypoint(checkpointer=InMemorySaver())
@@ -347,7 +350,7 @@ class entrypoint(Generic[ContextT]):
                "thread_id": "some_thread"
            }
        }
-        my_workflow.invoke("hello")
+        my_workflow.invoke("hello", config)
        ```

    Example: Using entrypoint.final to save a value
@@ -357,7 +360,10 @@ class entrypoint(Generic[ContextT]):
        long as the same thread id is used.

        ```python
-        from langgraph.checkpoint.memory import InMemorySaver
+        from typing import Any
+
+        from langgraph.checkpoint.memory import MemorySaver
+
        from langgraph.func import entrypoint

        @entrypoint(checkpointer=InMemorySaver())
@@ -1390,6 +1390,14 @@ def _is_field_managed_value(name: str, typ: type[Any]) -> ManagedValueSpec | Non
            if is_managed_value(decoration):
                return decoration

+    # Handle Required, NotRequired, etc wrapped types by extracting the inner type
+    if (
+        get_origin(typ) is not None
+        and (args := get_args(typ))
+        and (inner_type := args[0])
+    ):
+        return _is_field_managed_value(name, inner_type)
+
    return None


@@ -8,7 +8,7 @@ from typing import (

 from typing_extensions import TypeGuard

-from langgraph.pregel._scratchpad import PregelScratchpad
+from langgraph._internal._scratchpad import PregelScratchpad

 V = TypeVar("V")
 U = TypeVar("U")
@@ -1,7 +1,7 @@
 from typing import Annotated

+from langgraph._internal._scratchpad import PregelScratchpad
 from langgraph.managed.base import ManagedValue
-from langgraph.pregel._scratchpad import PregelScratchpad

 __all__ = ("IsLastStep", "RemainingStepsManager")

@@ -53,6 +53,7 @@ from langgraph._internal._constants import (
    RETURN,
    TASKS,
 )
+from langgraph._internal._scratchpad import PregelScratchpad
 from langgraph._internal._typing import EMPTY_SEQ, MISSING
 from langgraph.channels.base import BaseChannel
 from langgraph.channels.topic import Topic
@@ -69,7 +70,6 @@ from langgraph.pregel._call import get_runnable_for_task, identifier
 from langgraph.pregel._io import read_channels
 from langgraph.pregel._log import logger
 from langgraph.pregel._read import INPUT_CACHE_KEY_TYPE, PregelNode
-from langgraph.pregel._scratchpad import PregelScratchpad
 from langgraph.runtime import DEFAULT_RUNTIME, Runtime
 from langgraph.store.base import BaseStore
 from langgraph.types import (
@@ -48,6 +48,7 @@ from langgraph._internal._constants import (
    PUSH,
    RESUME,
 )
+from langgraph._internal._scratchpad import PregelScratchpad
 from langgraph._internal._typing import EMPTY_SEQ, MISSING
 from langgraph.cache.base import BaseCache
 from langgraph.channels.base import BaseChannel
@@ -100,7 +101,6 @@ from langgraph.pregel._io import (
    read_channels,
 )
 from langgraph.pregel._read import PregelNode
-from langgraph.pregel._scratchpad import PregelScratchpad
 from langgraph.pregel._utils import get_new_channel_versions, is_xxh3_128_hexdigest
 from langgraph.pregel.debug import (
    map_debug_checkpoint,
@@ -30,13 +30,13 @@ from langgraph._internal._constants import (
    RETURN,
 )
 from langgraph._internal._future import chain_future, run_coroutine_threadsafe
+from langgraph._internal._scratchpad import PregelScratchpad
 from langgraph._internal._typing import MISSING
 from langgraph.constants import TAG_HIDDEN
 from langgraph.errors import GraphBubbleUp, GraphInterrupt
 from langgraph.pregel._algo import Call
 from langgraph.pregel._executor import Submit
 from langgraph.pregel._retry import arun_with_retry, run_with_retry
-from langgraph.pregel._scratchpad import PregelScratchpad
 from langgraph.types import (
    CachePolicy,
    PregelExecutableTask,
@@ -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, Optional, Union, cast, get_type_hints
+from typing import Any, Callable, Generic, Union, cast, get_type_hints
 from uuid import UUID, uuid5

 from langchain_core.globals import get_debug
@@ -637,8 +637,7 @@ class Pregel(
        **deprecated_kwargs: Unpack[DeprecatedKwargs],
    ) -> None:
        if (
-            config_type := deprecated_kwargs.get("config_type"),
-            MISSING,
+            config_type := deprecated_kwargs.get("config_type", MISSING)
        ) is not MISSING:
            warnings.warn(
                "`config_type` is deprecated and will be removed. Please use `context_schema` instead.",
@@ -785,7 +784,8 @@ class Pregel(
        return self

    @deprecated(
-        "`config_schema` is deprecated. Use `get_context_jsonschema` for the relevant schema instead."
+        "`config_schema` is deprecated. Use `get_context_jsonschema` for the relevant schema instead.",
+        category=None,
    )
    def config_schema(self, *, include: Sequence[str] | None = None) -> type[BaseModel]:
        warnings.warn(
@@ -810,7 +810,8 @@ class Pregel(
        return create_model(self.get_name("Config"), field_definitions=fields)

    @deprecated(
-        "`get_config_jsonschema` is deprecated. Use `get_context_jsonschema` instead."
+        "`get_config_jsonschema` is deprecated. Use `get_context_jsonschema` instead.",
+        category=None,
    )
    def get_config_jsonschema(
        self, *, include: Sequence[str] | None = None
@@ -1305,7 +1306,7 @@ class Pregel(
    ) -> Iterator[StateSnapshot]:
        """Get the history of the state of the graph."""
        config = ensure_config(config)
-        checkpointer: BaseCheckpointSaver | None = ensure_config(config)[CONF].get(
+        checkpointer: BaseCheckpointSaver | None = config[CONF].get(
            CONFIG_KEY_CHECKPOINTER, self.checkpointer
        )
        if not checkpointer:
@@ -2351,7 +2352,6 @@ class Pregel(
        interrupt_before: All | Sequence[str] | None,
        interrupt_after: All | Sequence[str] | None,
        durability: Durability | None = None,
-        checkpoint_during: bool | None = None,
    ) -> tuple[
        set[StreamMode],
        str | Sequence[str],
@@ -2399,15 +2399,6 @@ class Pregel(
            cache: BaseCache | None = config[CONF][CONFIG_KEY_CACHE]
        else:
            cache = self.cache
-        if checkpoint_during is not None:
-            if durability is not None:
-                raise ValueError(
-                    "Cannot use both `checkpoint_during` and `durability` parameters."
-                )
-            elif checkpoint_during:
-                durability = "async"
-            else:
-                durability = "exit"
        if durability is None:
            durability = config.get(CONF, {}).get(CONFIG_KEY_DURABILITY, "async")
        return (
@@ -2480,6 +2471,17 @@ class Pregel(
        Yields:
            The output of each step in the graph. The output shape depends on the stream_mode.
        """
+        if (checkpoint_during := kwargs.get("checkpoint_during")) is not None:
+            warnings.warn(
+                "`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
+                category=LangGraphDeprecatedSinceV10,
+                stacklevel=2,
+            )
+            if durability is not None:
+                raise ValueError(
+                    "Cannot use both `checkpoint_during` and `durability` parameters. Please use `durability` instead."
+                )
+            durability = "async" if checkpoint_during else "exit"

        if stream_mode is None:
            # if being called as a node in another graph, default to values mode
@@ -2503,14 +2505,6 @@ class Pregel(
            run_id=config.get("run_id"),
        )
        try:
-            deprecated_checkpoint_during = cast(
-                Optional[bool], kwargs.get("checkpoint_during")
-            )
-            if deprecated_checkpoint_during is not None:
-                warnings.warn(
-                    "`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
-                    category=LangGraphDeprecatedSinceV10,
-                )
            # assign defaults
            (
                stream_modes,
@@ -2529,11 +2523,8 @@ class Pregel(
                interrupt_before=interrupt_before,
                interrupt_after=interrupt_after,
                durability=durability,
-                checkpoint_during=deprecated_checkpoint_during,
            )
-            if checkpointer is None and (
-                durability is not None or deprecated_checkpoint_during is not None
-            ):
+            if checkpointer is None and durability is not None:
                warnings.warn(
                    "`durability` has no effect when no checkpointer is present.",
                )
@@ -2570,7 +2561,7 @@ class Pregel(
                    pass

            # set durability mode for subgraphs
-            if durability is not None or deprecated_checkpoint_during is not None:
+            if durability is not None:
                config[CONF][CONFIG_KEY_DURABILITY] = durability_

            runtime = Runtime(
@@ -2741,6 +2732,17 @@ class Pregel(
        Yields:
            The output of each step in the graph. The output shape depends on the stream_mode.
        """
+        if (checkpoint_during := kwargs.get("checkpoint_during")) is not None:
+            warnings.warn(
+                "`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
+                category=LangGraphDeprecatedSinceV10,
+                stacklevel=2,
+            )
+            if durability is not None:
+                raise ValueError(
+                    "Cannot use both `checkpoint_during` and `durability` parameters. Please use `durability` instead."
+                )
+            durability = "async" if checkpoint_during else "exit"

        if stream_mode is None:
            # if being called as a node in another graph, default to values mode
@@ -2783,14 +2785,6 @@ class Pregel(
            else False
        )
        try:
-            deprecated_checkpoint_during = cast(
-                Optional[bool], kwargs.get("checkpoint_during")
-            )
-            if deprecated_checkpoint_during is not None:
-                warnings.warn(
-                    "`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
-                    category=LangGraphDeprecatedSinceV10,
-                )
            # assign defaults
            (
                stream_modes,
@@ -2809,11 +2803,8 @@ class Pregel(
                interrupt_before=interrupt_before,
                interrupt_after=interrupt_after,
                durability=durability,
-                checkpoint_during=deprecated_checkpoint_during,
            )
-            if checkpointer is None and (
-                durability is not None or deprecated_checkpoint_during is not None
-            ):
+            if checkpointer is None and durability is not None:
                warnings.warn(
                    "`durability` has no effect when no checkpointer is present.",
                )
@@ -2865,7 +2856,7 @@ class Pregel(
                    pass

            # set durability mode for subgraphs
-            if durability is not None or deprecated_checkpoint_during is not None:
+            if durability is not None:
                config[CONF][CONFIG_KEY_DURABILITY] = durability_

            runtime = Runtime(
@@ -2990,6 +2981,7 @@ class Pregel(
        output_keys: str | Sequence[str] | None = None,
        interrupt_before: All | Sequence[str] | None = None,
        interrupt_after: All | Sequence[str] | None = None,
+        durability: Durability | None = None,
        **kwargs: Any,
    ) -> dict[str, Any] | Any:
        """Run the graph with a single input and config.
@@ -3004,6 +2996,10 @@ class Pregel(
            output_keys: Optional. The output keys to retrieve from the graph run.
            interrupt_before: Optional. The nodes to interrupt the graph run before.
            interrupt_after: Optional. The nodes to interrupt the graph run after.
+            durability: The durability mode for the graph execution, defaults to "async". Options are:
+                - `"sync"`: Changes are persisted synchronously before the next step starts.
+                - `"async"`: Changes are persisted asynchronously while the next step executes.
+                - `"exit"`: Changes are persisted only when the graph exits.
            **kwargs: Additional keyword arguments to pass to the graph run.

        Returns:
@@ -3027,6 +3023,7 @@ class Pregel(
            output_keys=output_keys,
            interrupt_before=interrupt_before,
            interrupt_after=interrupt_after,
+            durability=durability,
            **kwargs,
        ):
            if stream_mode == "values":
@@ -3069,6 +3066,7 @@ class Pregel(
        output_keys: str | Sequence[str] | None = None,
        interrupt_before: All | Sequence[str] | None = None,
        interrupt_after: All | Sequence[str] | None = None,
+        durability: Durability | None = None,
        **kwargs: Any,
    ) -> dict[str, Any] | Any:
        """Asynchronously invoke the graph on a single input.
@@ -3083,6 +3081,10 @@ class Pregel(
            output_keys: Optional. The output keys to include in the result. Default is None.
            interrupt_before: Optional. The nodes to interrupt before. Default is None.
            interrupt_after: Optional. The nodes to interrupt after. Default is None.
+            durability: The durability mode for the graph execution, defaults to "async". Options are:
+                - `"sync"`: Changes are persisted synchronously before the next step starts.
+                - `"async"`: Changes are persisted asynchronously while the next step executes.
+                - `"exit"`: Changes are persisted only when the graph exits.
            **kwargs: Additional keyword arguments.

        Returns:
@@ -3107,6 +3109,7 @@ class Pregel(
            output_keys=output_keys,
            interrupt_before=interrupt_before,
            interrupt_after=interrupt_after,
+            durability=durability,
            **kwargs,
        ):
            if stream_mode == "values":
@@ -191,10 +191,7 @@ class Interrupt:
        return cls(value=value, id=xxh3_128_hexdigest(ns.encode()))

    @property
-    @deprecated(
-        "`interrupt_id` is deprecated. Use `id` instead.",
-        stacklevel=2,
-    )
+    @deprecated("`interrupt_id` is deprecated. Use `id` instead.", category=None)
    def interrupt_id(self) -> str:
        warn(
            "`interrupt_id` is deprecated. Use `id` instead.",
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph"
-version = "0.6.2"
+version = "0.6.3"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.9"
@@ -175,10 +175,10 @@
  '''
 # ---
 # name: test_prebuilt_tool_chat
-  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "is_last_step": {"title": "Is Last Step", "type": "boolean"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}}, "required": ["messages", "is_last_step", "remaining_steps"], "title": "AgentState", "type": "object"}'
+  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}}, "required": ["messages"], "title": "AgentState", "type": "object"}'
 # ---
 # name: test_prebuilt_tool_chat.1
-  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "is_last_step": {"title": "Is Last Step", "type": "boolean"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}}, "required": ["messages", "is_last_step", "remaining_steps"], "title": "AgentState", "type": "object"}'
+  '{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}}, "required": ["messages"], "title": "AgentState", "type": "object"}'
 # ---
 # name: test_prebuilt_tool_chat.2
  '''
@@ -1,6 +1,12 @@
+from __future__ import annotations
+
+import warnings
+from typing import Any, Optional
+
 import pytest
+from langchain_core.runnables import RunnableConfig
 from pytest_mock import MockerFixture
-from typing_extensions import TypedDict
+from typing_extensions import NotRequired, TypedDict

 from langgraph.channels.last_value import LastValue
 from langgraph.errors import NodeInterrupt
@@ -94,8 +100,6 @@ def test_pregel_types_deprecation() -> None:
        from langgraph.pregel.types import StateSnapshot  # noqa: F401


-@pytest.mark.filterwarnings("ignore:`config_schema` is deprecated")
-@pytest.mark.filterwarnings("ignore:`get_config_jsonschema` is deprecated")
 def test_config_schema_deprecation() -> None:
    with pytest.warns(
        LangGraphDeprecatedSinceV10,
@@ -121,7 +125,6 @@ def test_config_schema_deprecation() -> None:
        graph.get_config_jsonschema()


-@pytest.mark.filterwarnings("ignore:`config_schema` is deprecated")
 def test_config_schema_deprecation_on_entrypoint() -> None:
    with pytest.warns(
        LangGraphDeprecatedSinceV10,
@@ -132,10 +135,15 @@ def test_config_schema_deprecation_on_entrypoint() -> None:
        def my_entrypoint(state: PlainState) -> PlainState:
            return state

+    with pytest.warns(
+        LangGraphDeprecatedSinceV10,
+        match="`config_schema` is deprecated. Use `get_context_jsonschema` for the relevant schema instead.",
+    ):
        assert my_entrypoint.context_schema == PlainState
        assert my_entrypoint.config_schema() is not None


+@pytest.mark.filterwarnings("ignore:`config_type` is deprecated")
 def test_config_type_deprecation_pregel(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    chain = NodeBuilder().subscribe_only("input").do(add_one).write_to("output")
@@ -159,7 +167,6 @@ def test_config_type_deprecation_pregel(mocker: MockerFixture) -> None:
        assert instance.context_schema == PlainState


-@pytest.mark.filterwarnings("ignore:`interrupt_id` is deprecated. Use `id` instead.")
 def test_interrupt_attributes_deprecation() -> None:
    interrupt = Interrupt(value="question", id="abc")

@@ -170,7 +177,6 @@ def test_interrupt_attributes_deprecation() -> None:
        interrupt.interrupt_id


-@pytest.mark.filterwarnings("ignore:NodeInterrupt is deprecated.")
 def test_node_interrupt_deprecation() -> None:
    with pytest.warns(
        LangGraphDeprecatedSinceV10,
@@ -185,3 +191,144 @@ def test_deprecated_import() -> None:
        match="Importing PREVIOUS from langgraph.constants is deprecated. This constant is now private and should not be used directly.",
    ):
        from langgraph.constants import PREVIOUS  # noqa: F401
+
+
+@pytest.mark.filterwarnings(
+    "ignore:`durability` has no effect when no checkpointer is present"
+)
+def test_checkpoint_during_deprecation_state_graph() -> None:
+    class CheckDurability(TypedDict):
+        durability: NotRequired[str]
+
+    def plain_node(state: CheckDurability, config: RunnableConfig) -> CheckDurability:
+        return {"durability": config["configurable"]["__pregel_durability"]}
+
+    builder = StateGraph(CheckDurability)
+    builder.add_node("plain_node", plain_node)
+    builder.set_entry_point("plain_node")
+    graph = builder.compile()
+
+    with pytest.warns(
+        LangGraphDeprecatedSinceV10,
+        match="`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
+    ):
+        result = graph.invoke({}, checkpoint_during=True)
+        assert result["durability"] == "async"
+
+    with pytest.warns(
+        LangGraphDeprecatedSinceV10,
+        match="`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
+    ):
+        result = graph.invoke({}, checkpoint_during=False)
+        assert result["durability"] == "exit"
+
+    with pytest.warns(
+        LangGraphDeprecatedSinceV10,
+        match="`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
+    ):
+        for chunk in graph.stream({}, checkpoint_during=True):  # type: ignore[arg-type]
+            assert chunk["plain_node"]["durability"] == "async"
+
+    with pytest.warns(
+        LangGraphDeprecatedSinceV10,
+        match="`checkpoint_during` is deprecated and will be removed. Please use `durability` instead.",
+    ):
+        for chunk in graph.stream({}, checkpoint_during=False):  # type: ignore[arg-type]
+            assert chunk["plain_node"]["durability"] == "exit"
+
+
+def test_config_parameter_incorrect_typing() -> None:
+    """Test that a warning is raised when config parameter is typed incorrectly."""
+    builder = StateGraph(PlainState)
+
+    # Test sync function with config: dict
+    with pytest.warns(
+        UserWarning,
+        match="The 'config' parameter should be typed as 'RunnableConfig' or 'RunnableConfig | None', not '.*dict.*'. ",
+    ):
+
+        def sync_node_with_dict_config(state: PlainState, config: dict) -> PlainState:
+            return state
+
+        builder.add_node(sync_node_with_dict_config)
+
+    # Test async function with config: dict
+    with pytest.warns(
+        UserWarning,
+        match="The 'config' parameter should be typed as 'RunnableConfig' or 'RunnableConfig | None', not '.*dict.*'. ",
+    ):
+
+        async def async_node_with_dict_config(
+            state: PlainState, config: dict
+        ) -> PlainState:
+            return state
+
+        builder.add_node(async_node_with_dict_config)
+
+    # Test with other incorrect types
+    with pytest.warns(
+        UserWarning,
+        match="The 'config' parameter should be typed as 'RunnableConfig' or 'RunnableConfig | None', not '.*Any.*'. ",
+    ):
+
+        def sync_node_with_any_config(state: PlainState, config: Any) -> PlainState:
+            return state
+
+        builder.add_node(sync_node_with_any_config)
+
+    with pytest.warns(
+        UserWarning,
+        match="The 'config' parameter should be typed as 'RunnableConfig' or 'RunnableConfig | None', not '.*Any.*'. ",
+    ):
+
+        async def async_node_with_any_config(
+            state: PlainState, config: Any
+        ) -> PlainState:
+            return state
+
+        builder.add_node(async_node_with_any_config)
+
+    with warnings.catch_warnings(record=True) as w:
+
+        def node_with_correct_config(
+            state: PlainState, config: RunnableConfig
+        ) -> PlainState:
+            return state
+
+        builder.add_node(node_with_correct_config)
+
+        def node_with_optional_config(
+            state: PlainState,
+            config: Optional[RunnableConfig],  # noqa: UP045
+        ) -> PlainState:
+            return state
+
+        builder.add_node(node_with_optional_config)
+
+        def node_with_untyped_config(state: PlainState, config) -> PlainState:
+            return state
+
+        builder.add_node(node_with_untyped_config)
+
+        async def async_node_with_correct_config(
+            state: PlainState, config: RunnableConfig
+        ) -> PlainState:
+            return state
+
+        builder.add_node(async_node_with_correct_config)
+
+        async def async_node_with_optional_config(
+            state: PlainState,
+            config: Optional[RunnableConfig],  # noqa: UP045
+        ) -> PlainState:
+            return state
+
+        builder.add_node(async_node_with_optional_config)
+
+        async def async_node_with_untyped_config(
+            state: PlainState, config
+        ) -> PlainState:
+            return state
+
+        builder.add_node(async_node_with_untyped_config)
+        assert len(w) == 0
@@ -0,0 +1,27 @@
+from typing_extensions import NotRequired, Required, TypedDict
+
+from langgraph.graph import StateGraph
+from langgraph.managed import RemainingSteps
+
+
+class StatePlain(TypedDict):
+    remaining_steps: RemainingSteps
+
+
+class StateNotRequired(TypedDict):
+    remaining_steps: NotRequired[RemainingSteps]
+
+
+class StateRequired(TypedDict):
+    remaining_steps: Required[RemainingSteps]
+
+
+def test_managed_values_recognized() -> None:
+    graph = StateGraph(StatePlain)
+    assert "remaining_steps" in graph.managed
+
+    graph = StateGraph(StateNotRequired)
+    assert "remaining_steps" in graph.managed
+
+    graph = StateGraph(StateRequired)
+    assert "remaining_steps" in graph.managed
@@ -1192,7 +1192,7 @@ wheels = [

 [[package]]
 name = "langgraph"
-version = "0.6.2"
+version = "0.6.3"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
@@ -1433,7 +1433,7 @@ dev = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "0.6.2"
+version = "0.6.3"
 source = { editable = "../prebuilt" }
 dependencies = [
    { name = "langchain-core" },
@@ -34,7 +34,7 @@ from langchain_core.runnables import (
 )
 from langchain_core.tools import BaseTool
 from pydantic import BaseModel
-from typing_extensions import Annotated, TypedDict
+from typing_extensions import Annotated, NotRequired, TypedDict

 from langgraph._internal._runnable import RunnableCallable, RunnableLike
 from langgraph._internal._typing import MISSING
@@ -42,7 +42,7 @@ from langgraph.errors import ErrorCode, create_error_message
 from langgraph.graph import END, StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.graph.state import CompiledStateGraph
-from langgraph.managed import IsLastStep, RemainingSteps
+from langgraph.managed import RemainingSteps
 from langgraph.prebuilt._internal import ToolCallWithContext
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.runtime import Runtime
@@ -65,9 +65,7 @@ class AgentState(TypedDict):

    messages: Annotated[Sequence[BaseMessage], add_messages]

-    is_last_step: IsLastStep
-
-    remaining_steps: RemainingSteps
+    remaining_steps: NotRequired[RemainingSteps]


 class AgentStatePydantic(BaseModel):
@@ -254,6 +252,13 @@ def create_react_agent(
        LanguageModelLike,
        Callable[[StateSchema, Runtime[ContextT]], BaseChatModel],
        Callable[[StateSchema, Runtime[ContextT]], Awaitable[BaseChatModel]],
+        Callable[
+            [StateSchema, Runtime[ContextT]], Runnable[LanguageModelInput, BaseMessage]
+        ],
+        Callable[
+            [StateSchema, Runtime[ContextT]],
+            Awaitable[Runnable[LanguageModelInput, BaseMessage]],
+        ],
    ],
    tools: Union[Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode],
    *,
@@ -287,6 +292,9 @@ def create_react_agent(
            - **Dynamic model**: A callable with signature
              `(state, runtime) -> BaseChatModel` that returns different models
              based on runtime context
+              If the model has tools bound via `.bind_tools()` or other configurations,
+              the return type should be a Runnable[LanguageModelInput, BaseMessage]
+              Coroutines are also supported, allowing for asynchronous model selection.

            Dynamic functions receive graph state and runtime, enabling
            context-dependent model selection. Must return a `BaseChatModel`
@@ -561,16 +569,13 @@ def create_react_agent(
            else False
        )
        remaining_steps = _get_state_value(state, "remaining_steps", None)
-        is_last_step = _get_state_value(state, "is_last_step", False)
-        return (
-            (remaining_steps is None and is_last_step and has_tool_calls)
-            or (
-                remaining_steps is not None
-                and remaining_steps < 1
-                and all_tools_return_direct
-            )
-            or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)
-        )
+        if remaining_steps is not None:
+            if remaining_steps < 1 and all_tools_return_direct:
+                return True
+            elif remaining_steps < 2 and has_tool_calls:
+                return True
+
+        return False

    def _get_model_input_state(state: StateSchema) -> StateSchema:
        if pre_model_hook is not None:
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-prebuilt"
-version = "0.6.2"
+version = "0.6.3"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.9"
@@ -316,7 +316,7 @@ wheels = [

 [[package]]
 name = "langgraph"
-version = "0.6.2"
+version = "0.6.3"
 source = { editable = "../langgraph" }
 dependencies = [
    { name = "langchain-core" },
@@ -460,7 +460,7 @@ dev = [

 [[package]]
 name = "langgraph-prebuilt"
-version = "0.6.2"
+version = "0.6.3"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
Author	SHA1	Message	Date
Sydney RunkleandGitHub	2920a9dd19	fix(langgraph): Tidy up `AgentState` (#5801 ) Fixes https://github.com/langchain-ai/langgraph/issues/5784 * Removes usage of `is_last_step`, no longer needed with `remaining_steps` * Make `remaining_steps` `NotRequired` so that json schema doesn't suggest need for user input * Move `PregelScratchpad` to shared utils file to prevent circular import issue (it's used from `channels/managed` and other pregel files). * Ensures that managed values wrapped in `NotRequired` or `Required` are still recognized!	2025-08-03 07:12:53 -04:00
Eugene YurtsevandGitHub	db8ed4e9e4	fix(docs): update agents.md (#5800 ) fix comment in tip	2025-08-02 06:09:09 -04:00
Lauren Hirata SinghandGitHub	b16fcc8468	docs: remove broken links (#5803 )	2025-08-01 15:41:21 -04:00
Sydney RunkleandGitHub	a2fe4df89b	release: langgraph + prebuilt 0.6.3 (#5799 )	2025-08-01 14:52:38 -04:00
open-swe[bot]GitHubopen-swe[bot] <open-swe@users.noreply.github.com>Sydney Runkle	69dd20e523	fix(langgraph): Add warning for incorrect node signature with mistyped `config` param (#5798 ) Fixes: #5787 Ensures that if `config` is not typed as one of `RunanbleConfig` or `Optional[RunnableConfig]` a warning is raised to help developers avoid unexpected results at invocation time. --------- Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com> Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>	2025-08-01 17:25:14 +00:00
open-swe[bot]GitHubopen-swe[bot] <open-swe@users.noreply.github.com>	5152a96fce	fix(docs): Correct import statement for `InMemorySaver` in conceptual docs (#5797 ) Fixes #5781 Fixes the incorrect import statement in the Python documentation tutorial. - Changed import from `MemorySaver` to `InMemorySaver` - Ensures consistency between import statement and class instantiation - Verified through formatting and linting checks The documentation now correctly reflects the proper import for the InMemorySaver class. --------- Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>	2025-08-01 14:54:30 +00:00
Sydney RunkleandGitHub	220314b53a	fix(langgraph): fix up deprecation warnings (#5796 ) Fixes https://github.com/langchain-ai/langgraph/issues/5795 * Must use `category=None` on decorator so that we get type checking support but no dupe warning * Fixed tuple on `confix_type` warning causing false warning	2025-08-01 14:33:46 +00:00
Sydney Runkle GitHub Eugene Yurtsev Lauren Hirata Singh	38bbd92e01	feat(langgraph): add durability mode for `invoke` and `ainvoke` (#5771 ) Fixes https://github.com/langchain-ai/langgraph/issues/5741 Follow up to https://github.com/langchain-ai/langgraph/pull/5432 Plus clean up deprecation logic for `checkpoint_during` and add tests. --------- Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>	2025-08-01 10:30:24 -04:00
Eugene YurtsevandGitHub	e3cb2dd23b	chore(docs): fix more admonitions (#5792 ) Fix more admonitions	2025-07-31 22:57:44 -04:00
Eugene YurtsevandGitHub	2f23d1a30d	chore(docs): fix js build (#5793 ) Fix js build	2025-07-31 22:57:32 -04:00
Eugene YurtsevandGitHub	88e195bb78	chore(docs): fix admonitions in graph api page (#5791 ) fix many admonitions in the graph API page	2025-07-31 21:50:27 -04:00
Lauren Hirata Singh GitHub Hunter Lovell	41a4f993b1	docs: Update link maps for reference docs (#5745 ) Update link maps --------- Co-authored-by: Hunter Lovell <hunter@hntrl.io>	2025-07-31 18:06:35 -04:00
Kai-Wendel GitHub Eugene Yurtsev Lauren Hirata Singh	b8f3f48da9	fix(docs): Add missing imports to make examples runnable (#5477 ) I suppose that the code snippets are intended to run each on its own. To guarantee this the snippet for the example: "Write long-temr memory from tools" needs to include `RunnableConfig` Same also for the second commit of this pull request. The other commits are about similar issues, where imports are missing to make a snippet executable on its own. --------- Signed-off-by: Kai Wendel <kai.wendel@iws.uni-stuttgart.de> Co-authored-by: Eugene Yurtsev <eugene@langchain.dev> Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>	2025-07-31 20:04:52 +00:00
Michael GitHub Lauren Hirata Singh	94f7f0632d	docs(docs): fix image in "Run graph nodes in parallel" section of N Graph API how-to (#5527 ) Description: Replaced the outdated image in the "Run graph nodes in parallel" section of the N Graph API how-to guide to correctly show parallel node execution. Twitter handle: @MichaelLoukeris Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>	2025-07-31 20:02:33 +00:00
Xin JinandGitHub	b3e0582255	docs: clarify draw_mermaid_png only works in jupyter (#5609 ) Nit, without comments I thought image would somehow show in terminal, but that's not true you'd only get image to show if in jupyter notebook. Don't have to merge, i'm just seeing this particular as not a pleasure DevX. <img width="848" height="440" alt="Screenshot 2025-07-21 at 12 26 04 PM" src="https://github.com/user-attachments/assets/50261b35-f09d-4516-86f4-14e8bf53f8e2" />	2025-07-31 15:54:45 -04:00
Srikanto Rajbongshi GitHub Lauren Hirata Singh	24c7a8db3f	Remove duplicated pretty_print_messages helper in Multi‑agent supervisor tutorial (#5617 ) Description: Closes #___ Removed the redundant `pretty_print_message`/`pretty_print_messages` helper snippet from `docs/docs/tutorials/multi_agent/agent_supervisor.md`. Now there is a single, authoritative definition of these functions, which: - Simplifies the tutorial - Avoids reader confusion over which helper to use - Prevents future drift between duplicate code blocks Issue: Closes #___ Dependencies: None Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>	2025-07-31 19:54:22 +00:00
Sakshi Gupta GitHub Eugene Yurtsev	f8eb4244e0	docs(graphapi): update the graph image for the "Combine control flow and state updates with Command" example (#5626 ) The example had the node names as - node_a, node_b & node_c. But the graph shows the image of generate_topics. This change includes the addition of complete & accurate graph. --------- Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>	2025-07-31 19:52:55 +00:00
Syed Baqar AbbasandGitHub	62c3bbc9fe	docs: Removed repetition of block pretty_print_messages (#5636 ) ## docs: Removed repetition of block pretty_print_messages - Description: The documentation had the same cell duplicated, I fixed it by deleting one example - Issue: #5616	2025-07-31 19:49:48 +00:00
Kathryn MayandGitHub	76bbb761b4	docs: Add studio troubleshooting to redirects (#5783 ) Add a redirect from https://langchain-ai.github.io/langgraph/troubleshooting/studio/ to https://docs.langchain.com/langgraph-platform/troubleshooting-studio	2025-07-31 15:31:35 -04:00
Sydney RunkleandGitHub	80cd91344f	chore: no ci on v1 branch (#5782 )	2025-07-31 19:08:10 +00:00
Lauren Hirata SinghandGitHub	9e4c41cbed	docs: remove LGP mentions (#5780 )	2025-07-31 14:13:56 -04:00
ShehabandGitHub	1fda568df9	fix(docs): extended examples in graph API docs (#5774 ) Fixes #5770	2025-07-31 18:00:54 +00:00
Mohammad Mohtashim GitHub Eugene Yurtsev	08295ecadb	chore(prebuilt): add supported input types for model in create_react_agent (#5748 ) - Description: Update the type annotations in create_react_agent to allow one to provide a callable for the model that uses bind_tools and returns a Runnable[LanguageModelInput, BaseMessage] - Issue: #5739 --------- Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>	2025-07-31 14:00:12 -04:00
Lauren Hirata SinghandGitHub	4ecbabafe8	docs: redirects for LGP mintlify (#5767 ) - Added redirects for all of the LGP docs we're moving to Mintlify - Exclude files not listed in nav from search - Update banner	2025-07-31 13:10:45 -04:00
Sam CrowderandGitHub	246efe71f4	fix: change from developer to enterprise (#5778 )	2025-07-31 09:42:19 -07:00
Eugene YurtsevandGitHub	116121eb3a	feat(docs): dynamic model and tool selection in create react agent (#5777 ) Document dynamic models and dynamic tools	2025-07-31 11:22:30 -04:00
William FHandGitHub	18887e9f86	fix(langgraph): Remove duplicate call to ensure_config (#5768 )	2025-07-31 09:15:03 -04:00
Sam CrowderandGitHub	bec28226d0	fix: removing standalone container lite from old docs (#5759 )	2025-07-30 18:21:37 -07:00
Sam CrowderandGitHub	967e368e14	fix: add link that points to where changelog now lives (#5758 )	2025-07-30 17:49:51 -07:00