Update changelog via LangGraph Server Changelog Bot

2026-10-17 05:35:31 +02:00 · 2025-07-30 16:01:03 -07:00
33 changed files with 514 additions and 525 deletions
@@ -3,8 +3,7 @@ name: CI

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

 permissions:
@@ -4,48 +4,52 @@ 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.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",
+    "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",
    "CompiledStateGraph.astream": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.astream",
-    "Pregel.astream": "reference/pregel/#langgraph.pregel.Pregel.astream",
+    "Pregel.astream": "reference/graphs/#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/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",
+    "BaseStore": "reference/stores/#langgraph.store.base.BaseStore",
+    "BaseStore.put": "reference/stores/#langgraph.store.base.BaseStore.put",
+    "BinaryOperatorAggregate": "reference/channels/#langgraph.channels.BinaryOperatorAggregate",
    "CipherProtocol": "reference/checkpoints/#langgraph.checkpoint.serde.base.CipherProtocol",
-    "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",
+    "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",
    "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/func/#langgraph.func.entrypoint.final",
-    "entrypoint": "reference/func/#langgraph.func.entrypoint",
+    "entrypoint.final": "reference/functions/#langgraph.func.entrypoint.final",
+    "entrypoint": "reference/functions/#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/agents/#langgraph.prebuilt.tool_node.InjectedState",
+    "InjectedState": "reference/prebuilt/#langgraph.prebuilt.InjectedState",
    "InMemorySaver": "reference/checkpoints/#langgraph.checkpoint.memory.InMemorySaver",
-    "interrupt": "reference/types/#langgraph.types.Interrupt",
+    "interrupt": "reference/graphs/#langgraph.graph.interrupt",
    "CompiledStateGraph.invoke": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.invoke",
    "JsonPlusSerializer": "reference/checkpoints/#langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer",
-    "langgraph.json": "cloud/reference/cli/#configuration-file",
+    "langgraph.json": "reference/configuration/#configuration-file",
    "LastValue": "reference/channels/#langgraph.channels.LastValue",
+    # "MemorySaver": "<insert-ref>",
+    # "messagesStateReducer": "<insert-ref>",
    "PostgresSaver": "reference/checkpoints/#langgraph.checkpoint.postgres.PostgresSaver",
-    "Pregel": "reference/pregel/",
-    "Pregel.stream": "reference/pregel/#langgraph.pregel.Pregel.stream",
+    "Pregel": "reference/graphs/#langgraph.pregel.Pregel",
+    "Pregel.stream": "reference/graphs/#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",
@@ -53,56 +57,68 @@ 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/func/#langgraph.func.task",
+    "task": "reference/functions/#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/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",
+    "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",
    "ToolNode": "reference/classes/langgraph_prebuilt.ToolNode.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",
+    "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",
    "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",
-    "entrypoint.final": "reference/functions/langgraph.entrypoint.html#final",
-    "entrypoint": "reference/functions/langgraph.entrypoint.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",
    "getContextVariable": "https://v03.api.js.langchain.com/functions/_langchain_core.context.getContextVariable.html",
-    "get_state_history": "reference/classes/langgraph.CompiledStateGraph.html#getStateHistory",
-    "HumanInterrupt": "reference/interfaces/langgraph_prebuilt.HumanInterrupt.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",
    "interrupt": "reference/functions/langgraph.interrupt-2.html",
-    "CompiledStateGraph.invoke": "reference/classes/langgraph.CompiledStateGraph.html#invoke",
-    "langgraph.json": "cloud/reference/cli/#configuration-file",
+    "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",
    "MemorySaver": "reference/classes/checkpoint.MemorySaver.html",
    "messagesStateReducer": "reference/functions/langgraph.messagesStateReducer.html",
-    "PostgresSaver": "reference/classes/checkpoint_postgres.PostgresSaver.html",
+    "PostgresSaver": "reference/classes/langgraph_checkpoint_postgres.PostgresSaver.html",
    "Pregel": "reference/classes/langgraph.Pregel.html",
-    "Pregel.stream": "reference/classes/langgraph.Pregel.html#stream",
+    "Pregel.stream": "reference/functions/langgraph_Pregel.stream.html",
    "pre_model_hook": "reference/functions/langgraph_prebuilt.createReactAgent.html",
-    "protocol": "reference/interfaces/checkpoint.SerializerProtocol.html",
+    "protocol": "reference/classes/langgraph_checkpoint_serde_base.SerializerProtocol.html",
    "Send": "reference/classes/langgraph.Send.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",
+    "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",
 }

 # 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": "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/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/concepts/streaming.md": "concepts/streaming.md",
    "agents/streaming.md": "how-tos/streaming.md",
    # prebuilt redirects
@@ -128,84 +128,78 @@ 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",

-    # 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",
+    # 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/",
 }


@@ -145,76 +145,6 @@ 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(")", "", "# 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()");
+  lines.push(")", "", "agent.get_graph().draw_mermaid_png()");

  return lines.join("\n");
 }
@@ -21,6 +21,7 @@ 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.
@@ -1,11 +1,16 @@
 # 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.

 ---

+## v0.2.113 (2025-07-30)
+- Improved thread search pagination by refining response headers for better navigation and accuracy.
+
+## v0.2.112 (2025-07-30)
+- Ensured asynchronous handling for sync logging methods and added a linter to prevent future issues.
+- Fixed an issue where JavaScript tasks were not populating correctly in graphs.
+
 ## v0.2.111 (2025-07-29)
 - Started the heartbeat immediately upon connection to prevent JS graph streaming errors during long startups.

@@ -43,6 +43,11 @@ 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:
@@ -7,7 +7,10 @@ search:

 ## Free deployment

-[Local](../tutorials/langgraph-platform/local-server.md): Deploy for local testing and development.
+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.

 ## Production deployment

@@ -30,7 +33,8 @@ 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 |
-| **[Pricing](https://www.langchain.com/pricing-langgraph-platform)** | Plus | Enterprise | Enterprise | Enterprise |
+| **[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 |

 ## Cloud SaaS

@@ -13,6 +13,21 @@ 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.
@@ -34,3 +34,12 @@ 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).
@@ -10,3 +10,48 @@ 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 [local deployment](./deployment_options.md#free-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 [Standalone Container (Lite)](./deployment_options.md) 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                                               | 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                                              |
+| 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                                              |
 | APIs for retrieving and updating state and conversational history | ✅                                           | ✅                                                     | ✅                                                   |
 | APIs for retrieving and updating long-term memory                | ✅                                           | ✅                                                     | ✅                                                   |
 | Horizontally scalable task queues and servers                    | ✅                                           | ✅                                                     | ✅                                                   |
@@ -9,4 +9,15 @@ 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.
+- [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)
@@ -31,3 +31,15 @@ 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.
+    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.

 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.
+    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.

 ## Add custom authentication to your deployment

@@ -11,8 +11,7 @@ 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

@@ -58,8 +57,7 @@ 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.

@@ -343,12 +341,7 @@ 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
@@ -571,8 +564,7 @@ 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
@@ -619,8 +611,7 @@ print(graph.invoke({}, context={"my_runtime_value": "b"}))
    ```

 ??? 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
@@ -705,8 +696,7 @@ 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
@@ -769,8 +759,7 @@ 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:

@@ -846,8 +835,7 @@ 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.

@@ -874,8 +862,7 @@ 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)
@@ -921,8 +908,7 @@ 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
@@ -1004,14 +990,12 @@ 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.

@@ -1165,8 +1149,7 @@ 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]:
@@ -1251,8 +1234,7 @@ 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:

@@ -1569,8 +1551,7 @@ 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`

@@ -1640,8 +1621,7 @@ 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
@@ -1649,7 +1629,7 @@ from IPython.display import display, Image
 display(Image(graph.get_graph().draw_mermaid_png()))
 ```

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

@@ -1678,8 +1658,7 @@ 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
@@ -1762,8 +1741,7 @@ 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.

@@ -1436,7 +1436,6 @@ 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

@@ -2798,6 +2797,4 @@ 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.
-
 :::
-
@@ -172,82 +172,6 @@ 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:
@@ -1571,7 +1495,6 @@ 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, 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 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.
 - [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.
+- [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.
 - [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 Enterprise server version features. 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 differences between Lite and Enterprise server versions. 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.
@@ -49,8 +49,9 @@ 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.
-
-See the [LangGraph Platform reference](https://docs.langchain.com/langgraph-platform/reference-overview) for more reference documentation.
+- [Environment variables](../cloud/reference/env_var.md): Supported configuration variables when deploying with the LangGraph Platform.
@@ -21,9 +21,15 @@ 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
+#### For Standalone Container (Lite)

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

@@ -32,7 +38,12 @@ 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
+#### 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)

 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
@@ -11,4 +11,11 @@ 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)
+- [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)
@@ -85,80 +85,124 @@ 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
+    ```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


-  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

@@ -765,4 +809,4 @@ 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 ```
-``` 
+``` 
@@ -52,103 +52,6 @@ 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:
@@ -220,6 +123,7 @@ 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:
@@ -237,9 +141,11 @@ 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
@@ -250,10 +156,88 @@ 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
@@ -276,9 +260,14 @@ 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
@@ -288,6 +277,16 @@ 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
@@ -306,6 +305,7 @@ 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 %}
-<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.
+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!
 {% endblock %}
@@ -39,7 +39,6 @@ docs = [
    "markdown-callouts",
    "markdown-include",
    "mkdocs-exclude",
-    "mkdocs-exclude-search",
    "psycopg[binary]",
    "psycopg-pool",
    "pygments-ansi-color",
@@ -335,10 +335,7 @@ class entrypoint(Generic[ContextT]):
        of the previous invocation on the same thread id.

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

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

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

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

        @entrypoint(checkpointer=InMemorySaver())
@@ -128,7 +128,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
            Use this to expose immutable context data to your nodes, like user_id, db_conn, etc.
        input_schema: The schema class that defines the input to the graph.
        output_schema: The schema class that defines the output from the graph.
-        name: The default name to use when compiling the graph.

    !!! warning "`config_schema` Deprecated"
        The `config_schema` parameter is deprecated in v0.6.0 and support will be removed in v2.0.0.
@@ -178,7 +177,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
    managed: dict[str, ManagedValueSpec]
    schemas: dict[type[Any], dict[str, BaseChannel | ManagedValueSpec]]
    waiting_edges: set[tuple[tuple[str, ...], str]]
-    name: str = "LangGraph"
+
    compiled: bool
    state_schema: type[StateT]
    context_schema: type[ContextT] | None
@@ -875,7 +874,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
            debug=debug,
            store=store,
            cache=cache,
-            name=name or self.name,
+            name=name or "LangGraph",
        )

        compiled.attach_node(START, None)
@@ -1305,7 +1305,7 @@ class Pregel(
    ) -> Iterator[StateSnapshot]:
        """Get the history of the state of the graph."""
        config = ensure_config(config)
-        checkpointer: BaseCheckpointSaver | None = config[CONF].get(
+        checkpointer: BaseCheckpointSaver | None = ensure_config(config)[CONF].get(
            CONFIG_KEY_CHECKPOINTER, self.checkpointer
        )
        if not checkpointer:
@@ -254,13 +254,6 @@ 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],
    *,
@@ -294,9 +287,6 @@ 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`