Python SDK Reference

The LangGraph client implementations connect to the LangGraph API.

This module provides both asynchronous (LangGraphClient) and synchronous (SyncLanggraphClient) clients to interacting with the LangGraph API's core resources such as Assistants, Threads, Runs, and Cron jobs, as well as its persistent document Store.

LangGraphClient

Top-level client for LangGraph API.

Attributes:

• assistants –

  Manages versioned configuration for your graphs.

• threads –

  Handles (potentially) multi-turn interactions, such as conversational threads.

• runs –

  Controls individual invocations of the graph.

• crons –

  Manages scheduled operations.

• store –

  Interfaces with persistent, shared data storage.

HttpClient

Handle async requests to the LangGraph API.

Adds additional error messaging & content handling above the provided httpx client.

Attributes:

• client (AsyncClient) –

  Underlying HTTPX async client.

get(path: str, *, params: Optional[QueryParamTypes] = None) -> Any async

Send a GET request.

post(path: str, *, json: Optional[dict]) -> Any async

Send a POST request.

put(path: str, *, json: dict) -> Any async

Send a PUT request.

patch(path: str, *, json: dict) -> Any async

Send a PATCH request.

delete(path: str, *, json: Optional[Any] = None) -> None async

Send a DELETE request.

stream(path: str, method: str, *, json: Optional[dict] = None, params: Optional[QueryParamTypes] = None) -> AsyncIterator[StreamPart] async

Stream results using SSE.

AssistantsClient

Client for managing assistants in LangGraph.

This class provides methods to interact with assistants, which are versioned configurations of your graph.

Example:

client = get_client()
assistant = await client.assistants.get("assistant_id_123")

get(assistant_id: str) -> Assistant async

Get an assistant by ID.

Parameters:

• assistant_id (str) –

  The ID of the assistant to get.

Returns:

• Assistant ( Assistant ) –

  Assistant Object.

Example Usage:

assistant = await client.assistants.get(
    assistant_id="my_assistant_id"
)
print(assistant)

----------------------------------------------------

{
    'assistant_id': 'my_assistant_id',
    'graph_id': 'agent',
    'created_at': '2024-06-25T17:10:33.109781+00:00',
    'updated_at': '2024-06-25T17:10:33.109781+00:00',
    'config': {},
    'metadata': {'created_by': 'system'}
}

get_graph(assistant_id: str, *, xray: Union[int, bool] = False) -> dict[str, list[dict[str, Any]]] async

Get the graph of an assistant by ID.

Parameters:

• assistant_id (str) –

  The ID of the assistant to get the graph of.

• xray (Union[int, bool], default: False ) –

  Include graph representation of subgraphs. If an integer value is provided, only subgraphs with a depth less than or equal to the value will be included.

Returns:

• Graph ( dict[str, list[dict[str, Any]]] ) –

  The graph information for the assistant in JSON format.

Example Usage:

graph_info = await client.assistants.get_graph(
    assistant_id="my_assistant_id"
)
print(graph_info)

--------------------------------------------------------------------------------------------------------------------------

{
    'nodes':
        [
            {'id': '__start__', 'type': 'schema', 'data': '__start__'},
            {'id': '__end__', 'type': 'schema', 'data': '__end__'},
            {'id': 'agent','type': 'runnable','data': {'id': ['langgraph', 'utils', 'RunnableCallable'],'name': 'agent'}},
        ],
    'edges':
        [
            {'source': '__start__', 'target': 'agent'},
            {'source': 'agent','target': '__end__'}
        ]
}

get_schemas(assistant_id: str) -> GraphSchema async

Get the schemas of an assistant by ID.

Parameters:

• assistant_id (str) –

  The ID of the assistant to get the schema of.

Returns:

• GraphSchema ( GraphSchema ) –

  The graph schema for the assistant.

Example Usage:

schema = await client.assistants.get_schemas(
    assistant_id="my_assistant_id"
)
print(schema)

----------------------------------------------------------------------------------------------------------------------------

{
    'graph_id': 'agent',
    'state_schema':
        {
            'title': 'LangGraphInput',
            '$ref': '#/definitions/AgentState',
            'definitions':
                {
                    'BaseMessage':
                        {
                            'title': 'BaseMessage',
                            'description': 'Base abstract Message class. Messages are the inputs and outputs of ChatModels.',
                            'type': 'object',
                            'properties':
                                {
                                 'content':
                                    {
                                        'title': 'Content',
                                        'anyOf': [
                                            {'type': 'string'},
                                            {'type': 'array','items': {'anyOf': [{'type': 'string'}, {'type': 'object'}]}}
                                        ]
                                    },
                                'additional_kwargs':
                                    {
                                        'title': 'Additional Kwargs',
                                        'type': 'object'
                                    },
                                'response_metadata':
                                    {
                                        'title': 'Response Metadata',
                                        'type': 'object'
                                    },
                                'type':
                                    {
                                        'title': 'Type',
                                        'type': 'string'
                                    },
                                'name':
                                    {
                                        'title': 'Name',
                                        'type': 'string'
                                    },
                                'id':
                                    {
                                        'title': 'Id',
                                        'type': 'string'
                                    }
                                },
                            'required': ['content', 'type']
                        },
                    'AgentState':
                        {
                            'title': 'AgentState',
                            'type': 'object',
                            'properties':
                                {
                                    'messages':
                                        {
                                            'title': 'Messages',
                                            'type': 'array',
                                            'items': {'$ref': '#/definitions/BaseMessage'}
                                        }
                                },
                            'required': ['messages']
                        }
                }
        },
    'config_schema':
        {
            'title': 'Configurable',
            'type': 'object',
            'properties':
                {
                    'model_name':
                        {
                            'title': 'Model Name',
                            'enum': ['anthropic', 'openai'],
                            'type': 'string'
                        }
                }
        }
}

get_subgraphs(assistant_id: str, namespace: Optional[str] = None, recurse: bool = False) -> Subgraphs async

Get the schemas of an assistant by ID.

Parameters:

• assistant_id (str) –

  The ID of the assistant to get the schema of.

Returns:

• Subgraphs ( Subgraphs ) –

  The graph schema for the assistant.

create(graph_id: Optional[str], config: Optional[Config] = None, *, metadata: Json = None, assistant_id: Optional[str] = None, if_exists: Optional[OnConflictBehavior] = None, name: Optional[str] = None) -> Assistant async

Create a new assistant.

Useful when graph is configurable and you want to create different assistants based on different configurations.

Parameters:

• graph_id (Optional[str]) –

  The ID of the graph the assistant should use. The graph ID is normally set in your langgraph.json configuration.

• config (Optional[Config], default: None ) –

  Configuration to use for the graph.

• metadata (Json, default: None ) –

  Metadata to add to assistant.

• assistant_id (Optional[str], default: None ) –

  Assistant ID to use, will default to a random UUID if not provided.

• if_exists (Optional[OnConflictBehavior], default: None ) –

  How to handle duplicate creation. Defaults to 'raise' under the hood. Must be either 'raise' (raise error if duplicate), or 'do_nothing' (return existing assistant).

• name (Optional[str], default: None ) –

  The name of the assistant. Defaults to 'Untitled' under the hood.

Returns:

• Assistant ( Assistant ) –

  The created assistant.

Example Usage:

assistant = await client.assistants.create(
    graph_id="agent",
    config={"configurable": {"model_name": "openai"}},
    metadata={"number":1},
    assistant_id="my-assistant-id",
    if_exists="do_nothing",
    name="my_name"
)

update(assistant_id: str, *, graph_id: Optional[str] = None, config: Optional[Config] = None, metadata: Json = None, name: Optional[str] = None) -> Assistant async

Update an assistant.

Use this to point to a different graph, update the configuration, or change the metadata of an assistant.

Parameters:

• assistant_id (str) –

  Assistant to update.

• graph_id (Optional[str], default: None ) –

  The ID of the graph the assistant should use. The graph ID is normally set in your langgraph.json configuration. If None, assistant will keep pointing to same graph.

• config (Optional[Config], default: None ) –

  Configuration to use for the graph.

• metadata (Json, default: None ) –

  Metadata to merge with existing assistant metadata.

• name (Optional[str], default: None ) –

  The new name for the assistant.

Returns:

• Assistant ( Assistant ) –

  The updated assistant.

Example Usage:

assistant = await client.assistants.update(
    assistant_id='e280dad7-8618-443f-87f1-8e41841c180f',
    graph_id="other-graph",
    config={"configurable": {"model_name": "anthropic"}},
    metadata={"number":2}
)

delete(assistant_id: str) -> None async

Delete an assistant.

Parameters:

• assistant_id (str) –

  The assistant ID to delete.

Returns:

• None –

  None

Example Usage:

await client.assistants.delete(
    assistant_id="my_assistant_id"
)

search(*, metadata: Json = None, graph_id: Optional[str] = None, limit: int = 10, offset: int = 0) -> list[Assistant] async

Search for assistants.

Parameters:

• metadata (Json, default: None ) –

  Metadata to filter by. Exact match filter for each KV pair.

• graph_id (Optional[str], default: None ) –

  The ID of the graph to filter by. The graph ID is normally set in your langgraph.json configuration.

• limit (int, default: 10 ) –

  The maximum number of results to return.

• offset (int, default: 0 ) –

  The number of results to skip.

Returns:

• list[Assistant] –

  list[Assistant]: A list of assistants.

Example Usage:

assistants = await client.assistants.search(
    metadata = {"name":"my_name"},
    graph_id="my_graph_id",
    limit=5,
    offset=5
)

get_versions(assistant_id: str, metadata: Json = None, limit: int = 10, offset: int = 0) -> list[AssistantVersion] async

List all versions of an assistant.

Parameters:

• assistant_id (str) –

  The assistant ID to get versions for.

• metadata (Json, default: None ) –

  Metadata to filter versions by. Exact match filter for each KV pair.

• limit (int, default: 10 ) –

  The maximum number of versions to return.

• offset (int, default: 0 ) –

  The number of versions to skip.

Returns:

• list[AssistantVersion] –

  list[Assistant]: A list of assistants.

Example Usage:

assistant_versions = await client.assistants.get_versions(
    assistant_id="my_assistant_id"
)

set_latest(assistant_id: str, version: int) -> Assistant async

Change the version of an assistant.

Parameters:

• assistant_id (str) –

  The assistant ID to delete.

• version (int) –

  The version to change to.

Returns:

• Assistant ( Assistant ) –

  Assistant Object.

Example Usage:

new_version_assistant = await client.assistants.set_latest(
    assistant_id="my_assistant_id",
    version=3
)

ThreadsClient

Client for managing threads in LangGraph.

A thread maintains the state of a graph across multiple interactions/invocations (aka runs). It accumulates and persists the graph's state, allowing for continuity between separate invocations of the graph.

Example:

client = get_client()
new_thread = await client.threads.create(metadata={"user_id": "123"})

get(thread_id: str) -> Thread async

Get a thread by ID.

Parameters:

• thread_id (str) –

  The ID of the thread to get.

Returns:

• Thread ( Thread ) –

  Thread object.

Example Usage:

thread = await client.threads.get(
    thread_id="my_thread_id"
)
print(thread)

-----------------------------------------------------

{
    'thread_id': 'my_thread_id',
    'created_at': '2024-07-18T18:35:15.540834+00:00',
    'updated_at': '2024-07-18T18:35:15.540834+00:00',
    'metadata': {'graph_id': 'agent'}
}

create(*, metadata: Json = None, thread_id: Optional[str] = None, if_exists: Optional[OnConflictBehavior] = None) -> Thread async

Create a new thread.

Parameters:

• metadata (Json, default: None ) –

  Metadata to add to thread.

• thread_id (Optional[str], default: None ) –

  ID of thread. If None, ID will be a randomly generated UUID.

• if_exists (Optional[OnConflictBehavior], default: None ) –

  How to handle duplicate creation. Defaults to 'raise' under the hood. Must be either 'raise' (raise error if duplicate), or 'do_nothing' (return existing thread).

Returns:

• Thread ( Thread ) –

  The created thread.

Example Usage:

thread = await client.threads.create(
    metadata={"number":1},
    thread_id="my-thread-id",
    if_exists="raise"
)

update(thread_id: str, *, metadata: dict[str, Any]) -> Thread async

Update a thread.

Parameters:

• thread_id (str) –

  ID of thread to update.

• metadata (dict[str, Any]) –

  Metadata to merge with existing thread metadata.

Returns:

• Thread ( Thread ) –

  The created thread.

Example Usage:

thread = await client.threads.update(
    thread_id="my-thread-id",
    metadata={"number":1},
)

delete(thread_id: str) -> None async

Delete a thread.

Parameters:

• thread_id (str) –

  The ID of the thread to delete.

Returns:

• None –

  None

Example Usage:

await client.threads.delete(
    thread_id="my_thread_id"
)

search(*, metadata: Json = None, values: Json = None, status: Optional[ThreadStatus] = None, limit: int = 10, offset: int = 0) -> list[Thread] async

Search for threads.

Parameters:

• metadata (Json, default: None ) –

  Thread metadata to filter on.

• values (Json, default: None ) –

  State values to filter on.

• status (Optional[ThreadStatus], default: None ) –

  Thread status to filter on. Must be one of 'idle', 'busy', 'interrupted' or 'error'.

• limit (int, default: 10 ) –

  Limit on number of threads to return.

• offset (int, default: 0 ) –

  Offset in threads table to start search from.

Returns:

• list[Thread] –

  list[Thread]: List of the threads matching the search parameters.

Example Usage:

threads = await client.threads.search(
    metadata={"number":1},
    status="interrupted",
    limit=15,
    offset=5
)

copy(thread_id: str) -> None async

Copy a thread.

Parameters:

• thread_id (str) –

  The ID of the thread to copy.

Returns:

• None –

  None

Example Usage:

await client.threads.copy(
    thread_id="my_thread_id"
)

get_state(thread_id: str, checkpoint: Optional[Checkpoint] = None, checkpoint_id: Optional[str] = None, *, subgraphs: bool = False) -> ThreadState async

Get the state of a thread.

Parameters:

• thread_id (str) –

  The ID of the thread to get the state of.

• checkpoint (Optional[Checkpoint], default: None ) –

  The checkpoint to get the state of.

• subgraphs (bool, default: False ) –

  Include subgraphs states.

Returns:

• ThreadState ( ThreadState ) –

  the thread of the state.

Example Usage:

thread_state = await client.threads.get_state(
    thread_id="my_thread_id",
    checkpoint_id="my_checkpoint_id"
)
print(thread_state)

----------------------------------------------------------------------------------------------------------------------------------------------------------------------

{
    'values': {
        'messages': [
            {
                'content': 'how are you?',
                'additional_kwargs': {},
                'response_metadata': {},
                'type': 'human',
                'name': None,
                'id': 'fe0a5778-cfe9-42ee-b807-0adaa1873c10',
                'example': False
            },
            {
                'content': "I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, honest, and harmless.",
                'additional_kwargs': {},
                'response_metadata': {},
                'type': 'ai',
                'name': None,
                'id': 'run-159b782c-b679-4830-83c6-cef87798fe8b',
                'example': False,
                'tool_calls': [],
                'invalid_tool_calls': [],
                'usage_metadata': None
            }
        ]
    },
    'next': [],
    'checkpoint':
        {
            'thread_id': 'e2496803-ecd5-4e0c-a779-3226296181c2',
            'checkpoint_ns': '',
            'checkpoint_id': '1ef4a9b8-e6fb-67b1-8001-abd5184439d1'
        }
    'metadata':
        {
            'step': 1,
            'run_id': '1ef4a9b8-d7da-679a-a45a-872054341df2',
            'source': 'loop',
            'writes':
                {
                    'agent':
                        {
                            'messages': [
                                {
                                    'id': 'run-159b782c-b679-4830-83c6-cef87798fe8b',
                                    'name': None,
                                    'type': 'ai',
                                    'content': "I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, honest, and harmless.",
                                    'example': False,
                                    'tool_calls': [],
                                    'usage_metadata': None,
                                    'additional_kwargs': {},
                                    'response_metadata': {},
                                    'invalid_tool_calls': []
                                }
                            ]
                        }
                },
    'user_id': None,
    'graph_id': 'agent',
    'thread_id': 'e2496803-ecd5-4e0c-a779-3226296181c2',
    'created_by': 'system',
    'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'},
    'created_at': '2024-07-25T15:35:44.184703+00:00',
    'parent_config':
        {
            'thread_id': 'e2496803-ecd5-4e0c-a779-3226296181c2',
            'checkpoint_ns': '',
            'checkpoint_id': '1ef4a9b8-d80d-6fa7-8000-9300467fad0f'
        }
}

update_state(thread_id: str, values: Optional[Union[dict, Sequence[dict]]], *, as_node: Optional[str] = None, checkpoint: Optional[Checkpoint] = None, checkpoint_id: Optional[str] = None) -> ThreadUpdateStateResponse async

Update the state of a thread.

Parameters:

• thread_id (str) –

  The ID of the thread to update.

• values (Optional[Union[dict, Sequence[dict]]]) –

  The values to update the state with.

• as_node (Optional[str], default: None ) –

  Update the state as if this node had just executed.

• checkpoint (Optional[Checkpoint], default: None ) –

  The checkpoint to update the state of.

Returns:

• ThreadUpdateStateResponse ( ThreadUpdateStateResponse ) –

  Response after updating a thread's state.

Example Usage:

response = await client.threads.update_state(
    thread_id="my_thread_id",
    values={"messages":[{"role": "user", "content": "hello!"}]},
    as_node="my_node",
)
print(response)

----------------------------------------------------------------------------------------------------------------------------------------------------------------------

{
    'checkpoint': {
        'thread_id': 'e2496803-ecd5-4e0c-a779-3226296181c2',
        'checkpoint_ns': '',
        'checkpoint_id': '1ef4a9b8-e6fb-67b1-8001-abd5184439d1',
        'checkpoint_map': {}
    }
}

get_history(thread_id: str, *, limit: int = 10, before: Optional[str | Checkpoint] = None, metadata: Optional[dict] = None, checkpoint: Optional[Checkpoint] = None) -> list[ThreadState] async

Get the state history of a thread.

Parameters:

• thread_id (str) –

  The ID of the thread to get the state history for.

• checkpoint (Optional[Checkpoint], default: None ) –

  Return states for this subgraph. If empty defaults to root.

• limit (int, default: 10 ) –

  The maximum number of states to return.

• before (Optional[str | Checkpoint], default: None ) –

  Return states before this checkpoint.

• metadata (Optional[dict], default: None ) –

  Filter states by metadata key-value pairs.

Returns:

• list[ThreadState] –

  list[ThreadState]: the state history of the thread.

Example Usage:

thread_state = await client.threads.get_history(
    thread_id="my_thread_id",
    limit=5,
)

RunsClient

Client for managing runs in LangGraph.

A run is a single assistant invocation with optional input, config, and metadata. This client manages runs, which can be stateful (on threads) or stateless.

Example:

client = get_client()
run = await client.runs.create(assistant_id="asst_123", thread_id="thread_456", input={"query": "Hello"})

stream(thread_id: Optional[str], assistant_id: str, *, input: Optional[dict] = None, command: Optional[Command] = None, stream_mode: Union[StreamMode, Sequence[StreamMode]] = 'values', stream_subgraphs: bool = False, metadata: Optional[dict] = None, config: Optional[Config] = None, checkpoint: Optional[Checkpoint] = None, checkpoint_id: Optional[str] = None, interrupt_before: Optional[Union[All, Sequence[str]]] = None, interrupt_after: Optional[Union[All, Sequence[str]]] = None, feedback_keys: Optional[Sequence[str]] = None, on_disconnect: Optional[DisconnectMode] = None, on_completion: Optional[OnCompletionBehavior] = None, webhook: Optional[str] = None, multitask_strategy: Optional[MultitaskStrategy] = None, if_not_exists: Optional[IfNotExists] = None, after_seconds: Optional[int] = None) -> AsyncIterator[StreamPart]

Create a run and stream the results.

Parameters:

• thread_id (Optional[str]) –

  the thread ID to assign to the thread. If None will create a stateless run.

• assistant_id (str) –

  The assistant ID or graph name to stream from. If using graph name, will default to first assistant created from that graph.

• input (Optional[dict], default: None ) –

  The input to the graph.

• command (Optional[Command], default: None ) –

  A command to execute. Cannot be combined with input.

• stream_mode (Union[StreamMode, Sequence[StreamMode]], default: 'values' ) –

  The stream mode(s) to use.

• stream_subgraphs (bool, default: False ) –

  Whether to stream output from subgraphs.

• metadata (Optional[dict], default: None ) –

  Metadata to assign to the run.

• config (Optional[Config], default: None ) –

  The configuration for the assistant.

• checkpoint (Optional[Checkpoint], default: None ) –

  The checkpoint to resume from.

• interrupt_before (Optional[Union[All, Sequence[str]]], default: None ) –

  Nodes to interrupt immediately before they get executed.

• interrupt_after (Optional[Union[All, Sequence[str]]], default: None ) –

  Nodes to Nodes to interrupt immediately after they get executed.

• feedback_keys (Optional[Sequence[str]], default: None ) –

  Feedback keys to assign to run.

• on_disconnect (Optional[DisconnectMode], default: None ) –

  The disconnect mode to use. Must be one of 'cancel' or 'continue'.

• on_completion (Optional[OnCompletionBehavior], default: None ) –

  Whether to delete or keep the thread created for a stateless run. Must be one of 'delete' or 'keep'.

• webhook (Optional[str], default: None ) –

  Webhook to call after LangGraph API call is done.

• multitask_strategy (Optional[MultitaskStrategy], default: None ) –

  Multitask strategy to use. Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'.

• if_not_exists (Optional[IfNotExists], default: None ) –

  How to handle missing thread. Defaults to 'reject'. Must be either 'reject' (raise error if missing), or 'create' (create new thread).

• after_seconds (Optional[int], default: None ) –

  The number of seconds to wait before starting the run. Use to schedule future runs.

Returns:

• AsyncIterator[StreamPart] –

  AsyncIterator[StreamPart]: Asynchronous iterator of stream results.

Example Usage:

async for chunk in client.runs.stream(
    thread_id=None,
    assistant_id="agent",
    input={"messages": [{"role": "user", "content": "how are you?"}]},
    stream_mode=["values","debug"],
    metadata={"name":"my_run"},
    config={"configurable": {"model_name": "anthropic"}},
    interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"],
    interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"],
    feedback_keys=["my_feedback_key_1","my_feedback_key_2"],
    webhook="https://my.fake.webhook.com",
    multitask_strategy="interrupt"
):
    print(chunk)

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

StreamPart(event='metadata', data={'run_id': '1ef4a9b8-d7da-679a-a45a-872054341df2'})
StreamPart(event='values', data={'messages': [{'content': 'how are you?', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'fe0a5778-cfe9-42ee-b807-0adaa1873c10', 'example': False}]})
StreamPart(event='values', data={'messages': [{'content': 'how are you?', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'fe0a5778-cfe9-42ee-b807-0adaa1873c10', 'example': False}, {'content': "I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, honest, and harmless.", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-159b782c-b679-4830-83c6-cef87798fe8b', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]})
StreamPart(event='end', data=None)

create(thread_id: Optional[str], assistant_id: str, *, input: Optional[dict] = None, command: Optional[Command] = None, stream_mode: Union[StreamMode, Sequence[StreamMode]] = 'values', stream_subgraphs: bool = False, metadata: Optional[dict] = None, config: Optional[Config] = None, checkpoint: Optional[Checkpoint] = None, checkpoint_id: Optional[str] = None, interrupt_before: Optional[Union[All, Sequence[str]]] = None, interrupt_after: Optional[Union[All, Sequence[str]]] = None, webhook: Optional[str] = None, multitask_strategy: Optional[MultitaskStrategy] = None, if_not_exists: Optional[IfNotExists] = None, on_completion: Optional[OnCompletionBehavior] = None, after_seconds: Optional[int] = None) -> Run async

Create a background run.

Parameters:

• thread_id (Optional[str]) –

  the thread ID to assign to the thread. If None will create a stateless run.

• assistant_id (str) –

  The assistant ID or graph name to stream from. If using graph name, will default to first assistant created from that graph.

• input (Optional[dict], default: None ) –

  The input to the graph.

• command (Optional[Command], default: None ) –

  A command to execute. Cannot be combined with input.

• stream_mode (Union[StreamMode, Sequence[StreamMode]], default: 'values' ) –

  The stream mode(s) to use.

• stream_subgraphs (bool, default: False ) –

  Whether to stream output from subgraphs.

• metadata (Optional[dict], default: None ) –

  Metadata to assign to the run.

• config (Optional[Config], default: None ) –

  The configuration for the assistant.

• checkpoint (Optional[Checkpoint], default: None ) –

  The checkpoint to resume from.

• interrupt_before (Optional[Union[All, Sequence[str]]], default: None ) –

  Nodes to interrupt immediately before they get executed.

• interrupt_after (Optional[Union[All, Sequence[str]]], default: None ) –

  Nodes to Nodes to interrupt immediately after they get executed.

• webhook (Optional[str], default: None ) –

  Webhook to call after LangGraph API call is done.

• multitask_strategy (Optional[MultitaskStrategy], default: None ) –

  Multitask strategy to use. Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'.

• on_completion (Optional[OnCompletionBehavior], default: None ) –

  Whether to delete or keep the thread created for a stateless run. Must be one of 'delete' or 'keep'.

• if_not_exists (Optional[IfNotExists], default: None ) –

  How to handle missing thread. Defaults to 'reject'. Must be either 'reject' (raise error if missing), or 'create' (create new thread).

• after_seconds (Optional[int], default: None ) –

  The number of seconds to wait before starting the run. Use to schedule future runs.

Returns:

• Run ( Run ) –

  The created background run.

Example Usage:

background_run = await client.runs.create(
    thread_id="my_thread_id",
    assistant_id="my_assistant_id",
    input={"messages": [{"role": "user", "content": "hello!"}]},
    metadata={"name":"my_run"},
    config={"configurable": {"model_name": "openai"}},
    interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"],
    interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"],
    webhook="https://my.fake.webhook.com",
    multitask_strategy="interrupt"
)
print(background_run)

--------------------------------------------------------------------------------

{
    'run_id': 'my_run_id',
    'thread_id': 'my_thread_id',
    'assistant_id': 'my_assistant_id',
    'created_at': '2024-07-25T15:35:42.598503+00:00',
    'updated_at': '2024-07-25T15:35:42.598503+00:00',
    'metadata': {},
    'status': 'pending',
    'kwargs':
        {
            'input':
                {
                    'messages': [
                        {
                            'role': 'user',
                            'content': 'how are you?'
                        }
                    ]
                },
            'config':
                {
                    'metadata':
                        {
                            'created_by': 'system'
                        },
                    'configurable':
                        {
                            'run_id': 'my_run_id',
                            'user_id': None,
                            'graph_id': 'agent',
                            'thread_id': 'my_thread_id',
                            'checkpoint_id': None,
                            'model_name': "openai",
                            'assistant_id': 'my_assistant_id'
                        }
                },
            'webhook': "https://my.fake.webhook.com",
            'temporary': False,
            'stream_mode': ['values'],
            'feedback_keys': None,
            'interrupt_after': ["node_to_stop_after_1","node_to_stop_after_2"],
            'interrupt_before': ["node_to_stop_before_1","node_to_stop_before_2"]
        },
    'multitask_strategy': 'interrupt'
}

create_batch(payloads: list[RunCreate]) -> list[Run] async

Create a batch of stateless background runs.

wait(thread_id: Optional[str], assistant_id: str, *, input: Optional[dict] = None, command: Optional[Command] = None, metadata: Optional[dict] = None, config: Optional[Config] = None, checkpoint: Optional[Checkpoint] = None, checkpoint_id: Optional[str] = None, interrupt_before: Optional[Union[All, Sequence[str]]] = None, interrupt_after: Optional[Union[All, Sequence[str]]] = None, webhook: Optional[str] = None, on_disconnect: Optional[DisconnectMode] = None, on_completion: Optional[OnCompletionBehavior] = None, multitask_strategy: Optional[MultitaskStrategy] = None, if_not_exists: Optional[IfNotExists] = None, after_seconds: Optional[int] = None, raise_error: bool = True) -> Union[list[dict], dict[str, Any]] async

Create a run, wait until it finishes and return the final state.

Parameters:

• thread_id (Optional[str]) –

  the thread ID to create the run on. If None will create a stateless run.

• assistant_id (str) –

  The assistant ID or graph name to run. If using graph name, will default to first assistant created from that graph.

• input (Optional[dict], default: None ) –

  The input to the graph.

• command (Optional[Command], default: None ) –

  A command to execute. Cannot be combined with input.

• metadata (Optional[dict], default: None ) –

  Metadata to assign to the run.

• config (Optional[Config], default: None ) –

  The configuration for the assistant.

• checkpoint (Optional[Checkpoint], default: None ) –

  The checkpoint to resume from.

• interrupt_before (Optional[Union[All, Sequence[str]]], default: None ) –

  Nodes to interrupt immediately before they get executed.

• interrupt_after (Optional[Union[All, Sequence[str]]], default: None ) –

  Nodes to Nodes to interrupt immediately after they get executed.

• webhook (Optional[str], default: None ) –

  Webhook to call after LangGraph API call is done.

• on_disconnect (Optional[DisconnectMode], default: None ) –

  The disconnect mode to use. Must be one of 'cancel' or 'continue'.

• on_completion (Optional[OnCompletionBehavior], default: None ) –

  Whether to delete or keep the thread created for a stateless run. Must be one of 'delete' or 'keep'.

• multitask_strategy (Optional[MultitaskStrategy], default: None ) –

  Multitask strategy to use. Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'.

• if_not_exists (Optional[IfNotExists], default: None ) –

  How to handle missing thread. Defaults to 'reject'. Must be either 'reject' (raise error if missing), or 'create' (create new thread).

• after_seconds (Optional[int], default: None ) –

  The number of seconds to wait before starting the run. Use to schedule future runs.

Returns:

• Union[list[dict], dict[str, Any]] –

  Union[list[dict], dict[str, Any]]: The output of the run.

Example Usage:

final_state_of_run = await client.runs.wait(
    thread_id=None,
    assistant_id="agent",
    input={"messages": [{"role": "user", "content": "how are you?"}]},
    metadata={"name":"my_run"},
    config={"configurable": {"model_name": "anthropic"}},
    interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"],
    interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"],
    webhook="https://my.fake.webhook.com",
    multitask_strategy="interrupt"
)
print(final_state_of_run)

-------------------------------------------------------------------------------------------------------------------------------------------

{
    'messages': [
        {
            'content': 'how are you?',
            'additional_kwargs': {},
            'response_metadata': {},
            'type': 'human',
            'name': None,
            'id': 'f51a862c-62fe-4866-863b-b0863e8ad78a',
            'example': False
        },
        {
            'content': "I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, honest, and harmless.",
            'additional_kwargs': {},
            'response_metadata': {},
            'type': 'ai',
            'name': None,
            'id': 'run-bf1cd3c6-768f-4c16-b62d-ba6f17ad8b36',
            'example': False,
            'tool_calls': [],
            'invalid_tool_calls': [],
            'usage_metadata': None
        }
    ]
}

list(thread_id: str, *, limit: int = 10, offset: int = 0, status: Optional[RunStatus] = None) -> List[Run] async

List runs.

Parameters:

• thread_id (str) –

  The thread ID to list runs for.

• limit (int, default: 10 ) –

  The maximum number of results to return.

• offset (int, default: 0 ) –

  The number of results to skip.

• status (Optional[RunStatus], default: None ) –

  The status of the run to filter by.

Returns:

• List[Run] –

  List[Run]: The runs for the thread.

Example Usage:

await client.runs.delete(
    thread_id="thread_id_to_delete",
    limit=5,
    offset=5,
)

get(thread_id: str, run_id: str) -> Run async

Get a run.

Parameters:

• thread_id (str) –

  The thread ID to get.

• run_id (str) –

  The run ID to get.

Returns:

• Run ( Run ) –

  Run object.

Example Usage:

run = await client.runs.get(
    thread_id="thread_id_to_delete",
    run_id="run_id_to_delete",
)

cancel(thread_id: str, run_id: str, *, wait: bool = False, action: CancelAction = 'interrupt') -> None async

Get a run.

Parameters:

• thread_id (str) –

  The thread ID to cancel.

• run_id (str) –

  The run ID to cancek.

• wait (bool, default: False ) –

  Whether to wait until run has completed.

• action (CancelAction, default: 'interrupt' ) –

  Action to take when cancelling the run. Possible values are interrupt or rollback. Default is interrupt.

Returns:

• None –

  None

Example Usage:

await client.runs.cancel(
    thread_id="thread_id_to_cancel",
    run_id="run_id_to_cancel",
    wait=True,
    action="interrupt"
)

join(thread_id: str, run_id: str) -> dict async

Block until a run is done. Returns the final state of the thread.

Parameters:

• thread_id (str) –

  The thread ID to join.

• run_id (str) –

  The run ID to join.

Returns:

• dict –

  None

Example Usage:

result =await client.runs.join(
    thread_id="thread_id_to_join",
    run_id="run_id_to_join"
)

join_stream(thread_id: str, run_id: str, *, cancel_on_disconnect: bool = False) -> AsyncIterator[StreamPart]

Stream output from a run in real-time, until the run is done. Output is not buffered, so any output produced before this call will not be received here.

Parameters:

• thread_id (str) –

  The thread ID to join.

• run_id (str) –

  The run ID to join.

• cancel_on_disconnect (bool, default: False ) –

  Whether to cancel the run when the stream is disconnected.

Returns:

• AsyncIterator[StreamPart] –

  None

Example Usage:

await client.runs.join_stream(
    thread_id="thread_id_to_join",
    run_id="run_id_to_join"
)

delete(thread_id: str, run_id: str) -> None async

Delete a run.

Parameters:

• thread_id (str) –

  The thread ID to delete.

• run_id (str) –

  The run ID to delete.

Returns:

• None –

  None

Example Usage:

await client.runs.delete(
    thread_id="thread_id_to_delete",
    run_id="run_id_to_delete"
)

CronClient

Client for managing recurrent runs (cron jobs) in LangGraph.

A run is a single invocation of an assistant with optional input and config. This client allows scheduling recurring runs to occur automatically.

Example:

client = get_client()
cron_job = await client.crons.create_for_thread(
    thread_id="thread_123",
    assistant_id="asst_456",
    schedule="0 9 * * *",
    input={"message": "Daily update"}
)

create_for_thread(thread_id: str, assistant_id: str, *, schedule: str, input: Optional[dict] = None, metadata: Optional[dict] = None, config: Optional[Config] = None, interrupt_before: Optional[Union[All, list[str]]] = None, interrupt_after: Optional[Union[All, list[str]]] = None, webhook: Optional[str] = None, multitask_strategy: Optional[str] = None) -> Run async

Create a cron job for a thread.

Parameters:

• thread_id (str) –

  the thread ID to run the cron job on.

• assistant_id (str) –

  The assistant ID or graph name to use for the cron job. If using graph name, will default to first assistant created from that graph.

• schedule (str) –

  The cron schedule to execute this job on.

• input (Optional[dict], default: None ) –

  The input to the graph.

• metadata (Optional[dict], default: None ) –

  Metadata to assign to the cron job runs.

• config (Optional[Config], default: None ) –

  The configuration for the assistant.

• interrupt_before (Optional[Union[All, list[str]]], default: None ) –

  Nodes to interrupt immediately before they get executed.

• interrupt_after (Optional[Union[All, list[str]]], default: None ) –

  Nodes to Nodes to interrupt immediately after they get executed.

• webhook (Optional[str], default: None ) –

  Webhook to call after LangGraph API call is done.

• multitask_strategy (Optional[str], default: None ) –

  Multitask strategy to use. Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'.

Returns:

• Run ( Run ) –

  The cron run.

Example Usage:

cron_run = await client.crons.create_for_thread(
    thread_id="my-thread-id",
    assistant_id="agent",
    schedule="27 15 * * *",
    input={"messages": [{"role": "user", "content": "hello!"}]},
    metadata={"name":"my_run"},
    config={"configurable": {"model_name": "openai"}},
    interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"],
    interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"],
    webhook="https://my.fake.webhook.com",
    multitask_strategy="interrupt"
)

create(assistant_id: str, *, schedule: str, input: Optional[dict] = None, metadata: Optional[dict] = None, config: Optional[Config] = None, interrupt_before: Optional[Union[All, list[str]]] = None, interrupt_after: Optional[Union[All, list[str]]] = None, webhook: Optional[str] = None, multitask_strategy: Optional[str] = None) -> Run async

Create a cron run.

Parameters:

• assistant_id (str) –

  The assistant ID or graph name to use for the cron job. If using graph name, will default to first assistant created from that graph.

• schedule (str) –

  The cron schedule to execute this job on.

• input (Optional[dict], default: None ) –

  The input to the graph.

• metadata (Optional[dict], default: None ) –

  Metadata to assign to the cron job runs.

• config (Optional[Config], default: None ) –

  The configuration for the assistant.

• interrupt_before (Optional[Union[All, list[str]]], default: None ) –

  Nodes to interrupt immediately before they get executed.

• interrupt_after (Optional[Union[All, list[str]]], default: None ) –

  Nodes to Nodes to interrupt immediately after they get executed.

• webhook (Optional[str], default: None ) –

  Webhook to call after LangGraph API call is done.

• multitask_strategy (Optional[str], default: None ) –

  Multitask strategy to use. Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'.

Returns:

• Run ( Run ) –

  The cron run.

Example Usage:

cron_run = client.crons.create(
    assistant_id="agent",
    schedule="27 15 * * *",
    input={"messages": [{"role": "user", "content": "hello!"}]},
    metadata={"name":"my_run"},
    config={"configurable": {"model_name": "openai"}},
    interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"],
    interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"],
    webhook="https://my.fake.webhook.com",
    multitask_strategy="interrupt"
)

delete(cron_id: str) -> None async

Delete a cron.

Parameters:

• cron_id (str) –

  The cron ID to delete.

Returns:

• None –

  None

Example Usage:

await client.crons.delete(
    cron_id="cron_to_delete"
)

search(*, assistant_id: Optional[str] = None, thread_id: Optional[str] = None, limit: int = 10, offset: int = 0) -> list[Cron] async

Get a list of cron jobs.

Parameters:

• assistant_id (Optional[str], default: None ) –

  The assistant ID or graph name to search for.

• thread_id (Optional[str], default: None ) –

  the thread ID to search for.

• limit (int, default: 10 ) –

  The maximum number of results to return.

• offset (int, default: 0 ) –

  The number of results to skip.

Returns:

• list[Cron] –

  list[Cron]: The list of cron jobs returned by the search,

Example Usage:

cron_jobs = await client.crons.search(
    assistant_id="my_assistant_id",
    thread_id="my_thread_id",
    limit=5,
    offset=5,
)
print(cron_jobs)

----------------------------------------------------------

[
    {
        'cron_id': '1ef3cefa-4c09-6926-96d0-3dc97fd5e39b',
        'assistant_id': 'my_assistant_id',
        'thread_id': 'my_thread_id',
        'user_id': None,
        'payload':
            {
                'input': {'start_time': ''},
                'schedule': '4 * * * *',
                'assistant_id': 'my_assistant_id'
            },
        'schedule': '4 * * * *',
        'next_run_date': '2024-07-25T17:04:00+00:00',
        'end_time': None,
        'created_at': '2024-07-08T06:02:23.073257+00:00',
        'updated_at': '2024-07-08T06:02:23.073257+00:00'
    }
]

StoreClient

Client for interacting with the graph's shared storage.

The Store provides a key-value storage system for persisting data across graph executions, allowing for stateful operations and data sharing across threads.

Example:

client = get_client()
await client.store.put_item(["users", "user123"], "mem-123451342", {"name": "Alice", "score": 100})

put_item(namespace: Sequence[str], /, key: str, value: dict[str, Any], index: Optional[Union[Literal[False], list[str]]] = None) -> None async

Store or update an item.

Parameters:

• namespace (Sequence[str]) –

  A list of strings representing the namespace path.

• key (str) –

  The unique identifier for the item within the namespace.

• value (dict[str, Any]) –

  A dictionary containing the item's data.

• index (Optional[Union[Literal[False], list[str]]], default: None ) –

  Controls search indexing - None (use defaults), False (disable), or list of field paths to index.

Returns:

• None –

  None

Example Usage:

await client.store.put_item(
    ["documents", "user123"],
    key="item456",
    value={"title": "My Document", "content": "Hello World"}
)

get_item(namespace: Sequence[str], /, key: str) -> Item async

Retrieve a single item.

Parameters:

• key (str) –

  The unique identifier for the item.

• namespace (Sequence[str]) –

  Optional list of strings representing the namespace path.

Returns:

• Item ( Item ) –

  The retrieved item.

Example Usage:

item = await client.store.get_item(
    ["documents", "user123"],
    key="item456",
)
print(item)

----------------------------------------------------------------

{
    'namespace': ['documents', 'user123'],
    'key': 'item456',
    'value': {'title': 'My Document', 'content': 'Hello World'},
    'created_at': '2024-07-30T12:00:00Z',
    'updated_at': '2024-07-30T12:00:00Z'
}

delete_item(namespace: Sequence[str], /, key: str) -> None async

Delete an item.

Parameters:

• key (str) –

  The unique identifier for the item.

• namespace (Sequence[str]) –

  Optional list of strings representing the namespace path.

Returns:

• None –

  None

Example Usage:

await client.store.delete_item(
    ["documents", "user123"],
    key="item456",
)

search_items(namespace_prefix: Sequence[str], /, filter: Optional[dict[str, Any]] = None, limit: int = 10, offset: int = 0, query: Optional[str] = None) -> SearchItemsResponse async

Search for items within a namespace prefix.

Parameters:

• namespace_prefix (Sequence[str]) –

  List of strings representing the namespace prefix.

• filter (Optional[dict[str, Any]], default: None ) –

  Optional dictionary of key-value pairs to filter results.

• limit (int, default: 10 ) –

  Maximum number of items to return (default is 10).

• offset (int, default: 0 ) –

  Number of items to skip before returning results (default is 0).

• query (Optional[str], default: None ) –

  Optional query for natural language search.

Returns:

• SearchItemsResponse –

  List[Item]: A list of items matching the search criteria.

Example Usage:

items = await client.store.search_items(
    ["documents"],
    filter={"author": "John Doe"},
    limit=5,
    offset=0
)
print(items)

----------------------------------------------------------------

{
    "items": [
        {
            "namespace": ["documents", "user123"],
            "key": "item789",
            "value": {
                "title": "Another Document",
                "author": "John Doe"
            },
            "created_at": "2024-07-30T12:00:00Z",
            "updated_at": "2024-07-30T12:00:00Z"
        },
        # ... additional items ...
    ]
}

list_namespaces(prefix: Optional[List[str]] = None, suffix: Optional[List[str]] = None, max_depth: Optional[int] = None, limit: int = 100, offset: int = 0) -> ListNamespaceResponse async

List namespaces with optional match conditions.

Parameters:

• prefix (Optional[List[str]], default: None ) –

  Optional list of strings representing the prefix to filter namespaces.

• suffix (Optional[List[str]], default: None ) –

  Optional list of strings representing the suffix to filter namespaces.

• max_depth (Optional[int], default: None ) –

  Optional integer specifying the maximum depth of namespaces to return.

• limit (int, default: 100 ) –

  Maximum number of namespaces to return (default is 100).

• offset (int, default: 0 ) –

  Number of namespaces to skip before returning results (default is 0).

Returns:

• ListNamespaceResponse –

  List[List[str]]: A list of namespaces matching the criteria.

Example Usage:

namespaces = await client.store.list_namespaces(
    prefix=["documents"],
    max_depth=3,
    limit=10,
    offset=0
)
print(namespaces)

----------------------------------------------------------------

[
    ["documents", "user123", "reports"],
    ["documents", "user456", "invoices"],
    ...
]

SyncLangGraphClient

Synchronous client for interacting with the LangGraph API.

This class provides synchronous access to LangGraph API endpoints for managing assistants, threads, runs, cron jobs, and data storage.

Example:

client = get_sync_client()
assistant = client.assistants.get("asst_123")

SyncHttpClient

get(path: str, *, params: Optional[QueryParamTypes] = None) -> Any

Send a GET request.

post(path: str, *, json: Optional[dict]) -> Any

Send a POST request.

put(path: str, *, json: dict) -> Any

Send a PUT request.

patch(path: str, *, json: dict) -> Any

Send a PATCH request.

delete(path: str, *, json: Optional[Any] = None) -> None

Send a DELETE request.

stream(path: str, method: str, *, json: Optional[dict] = None, params: Optional[QueryParamTypes] = None) -> Iterator[StreamPart]

Stream the results of a request using SSE.

SyncAssistantsClient

Client for managing assistants in LangGraph synchronously.

This class provides methods to interact with assistants, which are versioned configurations of your graph.

Example:

client = get_client()
assistant = client.assistants.get("assistant_id_123")

get(assistant_id: str) -> Assistant

Get an assistant by ID.

Parameters:

• assistant_id (str) –

  The ID of the assistant to get.

Returns:

• Assistant ( Assistant ) –

  Assistant Object.

Example Usage:

assistant = client.assistants.get(
    assistant_id="my_assistant_id"
)
print(assistant)

----------------------------------------------------

{
    'assistant_id': 'my_assistant_id',
    'graph_id': 'agent',
    'created_at': '2024-06-25T17:10:33.109781+00:00',
    'updated_at': '2024-06-25T17:10:33.109781+00:00',
    'config': {},
    'metadata': {'created_by': 'system'}
}

get_graph(assistant_id: str, *, xray: Union[int, bool] = False) -> dict[str, list[dict[str, Any]]]

Get the graph of an assistant by ID.

Parameters:

• assistant_id (str) –

  The ID of the assistant to get the graph of.

• xray (Union[int, bool], default: False ) –

  Include graph representation of subgraphs. If an integer value is provided, only subgraphs with a depth less than or equal to the value will be included.

Returns:

• Graph ( dict[str, list[dict[str, Any]]] ) –

  The graph information for the assistant in JSON format.

Example Usage:

graph_info = client.assistants.get_graph(
    assistant_id="my_assistant_id"
)
print(graph_info)

--------------------------------------------------------------------------------------------------------------------------

{
    'nodes':
        [
            {'id': '__start__', 'type': 'schema', 'data': '__start__'},
            {'id': '__end__', 'type': 'schema', 'data': '__end__'},
            {'id': 'agent','type': 'runnable','data': {'id': ['langgraph', 'utils', 'RunnableCallable'],'name': 'agent'}},
        ],
    'edges':
        [
            {'source': '__start__', 'target': 'agent'},
            {'source': 'agent','target': '__end__'}
        ]
}

get_schemas(assistant_id: str) -> GraphSchema

Get the schemas of an assistant by ID.

Parameters:

• assistant_id (str) –

  The ID of the assistant to get the schema of.

Returns:

• GraphSchema ( GraphSchema ) –

  The graph schema for the assistant.

Example Usage:

schema = client.assistants.get_schemas(
    assistant_id="my_assistant_id"
)
print(schema)

----------------------------------------------------------------------------------------------------------------------------

{
    'graph_id': 'agent',
    'state_schema':
        {
            'title': 'LangGraphInput',
            '$ref': '#/definitions/AgentState',
            'definitions':
                {
                    'BaseMessage':
                        {
                            'title': 'BaseMessage',
                            'description': 'Base abstract Message class. Messages are the inputs and outputs of ChatModels.',
                            'type': 'object',
                            'properties':
                                {
                                 'content':
                                    {
                                        'title': 'Content',
                                        'anyOf': [
                                            {'type': 'string'},
                                            {'type': 'array','items': {'anyOf': [{'type': 'string'}, {'type': 'object'}]}}
                                        ]
                                    },
                                'additional_kwargs':
                                    {
                                        'title': 'Additional Kwargs',
                                        'type': 'object'
                                    },
                                'response_metadata':
                                    {
                                        'title': 'Response Metadata',
                                        'type': 'object'
                                    },
                                'type':
                                    {
                                        'title': 'Type',
                                        'type': 'string'
                                    },
                                'name':
                                    {
                                        'title': 'Name',
                                        'type': 'string'
                                    },
                                'id':
                                    {
                                        'title': 'Id',
                                        'type': 'string'
                                    }
                                },
                            'required': ['content', 'type']
                        },
                    'AgentState':
                        {
                            'title': 'AgentState',
                            'type': 'object',
                            'properties':
                                {
                                    'messages':
                                        {
                                            'title': 'Messages',
                                            'type': 'array',
                                            'items': {'$ref': '#/definitions/BaseMessage'}
                                        }
                                },
                            'required': ['messages']
                        }
                }
        },
    'config_schema':
        {
            'title': 'Configurable',
            'type': 'object',
            'properties':
                {
                    'model_name':
                        {
                            'title': 'Model Name',
                            'enum': ['anthropic', 'openai'],
                            'type': 'string'
                        }
                }
        }
}

get_subgraphs(assistant_id: str, namespace: Optional[str] = None, recurse: bool = False) -> Subgraphs

Get the schemas of an assistant by ID.

Parameters:

• assistant_id (str) –

  The ID of the assistant to get the schema of.

Returns:

• Subgraphs ( Subgraphs ) –

  The graph schema for the assistant.

create(graph_id: Optional[str], config: Optional[Config] = None, *, metadata: Json = None, assistant_id: Optional[str] = None, if_exists: Optional[OnConflictBehavior] = None, name: Optional[str] = None) -> Assistant

Create a new assistant.

Useful when graph is configurable and you want to create different assistants based on different configurations.

Parameters:

• graph_id (Optional[str]) –

  The ID of the graph the assistant should use. The graph ID is normally set in your langgraph.json configuration.

• config (Optional[Config], default: None ) –

  Configuration to use for the graph.

• metadata (Json, default: None ) –

  Metadata to add to assistant.

• assistant_id (Optional[str], default: None ) –

  Assistant ID to use, will default to a random UUID if not provided.

• if_exists (Optional[OnConflictBehavior], default: None ) –

  How to handle duplicate creation. Defaults to 'raise' under the hood. Must be either 'raise' (raise error if duplicate), or 'do_nothing' (return existing assistant).

• name (Optional[str], default: None ) –

  The name of the assistant. Defaults to 'Untitled' under the hood.

Returns:

• Assistant ( Assistant ) –

  The created assistant.

Example Usage:

assistant = client.assistants.create(
    graph_id="agent",
    config={"configurable": {"model_name": "openai"}},
    metadata={"number":1},
    assistant_id="my-assistant-id",
    if_exists="do_nothing",
    name="my_name"
)

update(assistant_id: str, *, graph_id: Optional[str] = None, config: Optional[Config] = None, metadata: Json = None, name: Optional[str] = None) -> Assistant

Update an assistant.

Use this to point to a different graph, update the configuration, or change the metadata of an assistant.

Parameters:

• assistant_id (str) –

  Assistant to update.

• graph_id (Optional[str], default: None ) –

  The ID of the graph the assistant should use. The graph ID is normally set in your langgraph.json configuration. If None, assistant will keep pointing to same graph.

• config (Optional[Config], default: None ) –

  Configuration to use for the graph.

• metadata (Json, default: None ) –

  Metadata to merge with existing assistant metadata.

• name (Optional[str], default: None ) –

  The new name for the assistant.

Returns:

• Assistant ( Assistant ) –

  The updated assistant.

Example Usage:

assistant = client.assistants.update(
    assistant_id='e280dad7-8618-443f-87f1-8e41841c180f',
    graph_id="other-graph",
    config={"configurable": {"model_name": "anthropic"}},
    metadata={"number":2}
)

delete(assistant_id: str) -> None

Delete an assistant.

Parameters:

• assistant_id (str) –

  The assistant ID to delete.

Returns:

• None –

  None

Example Usage:

client.assistants.delete(
    assistant_id="my_assistant_id"
)

search(*, metadata: Json = None, graph_id: Optional[str] = None, limit: int = 10, offset: int = 0) -> list[Assistant]

Search for assistants.

Parameters:

• metadata (Json, default: None ) –

  Metadata to filter by. Exact match filter for each KV pair.

• graph_id (Optional[str], default: None ) –

  The ID of the graph to filter by. The graph ID is normally set in your langgraph.json configuration.

• limit (int, default: 10 ) –

  The maximum number of results to return.

• offset (int, default: 0 ) –

  The number of results to skip.

Returns:

• list[Assistant] –

  list[Assistant]: A list of assistants.

Example Usage:

assistants = client.assistants.search(
    metadata = {"name":"my_name"},
    graph_id="my_graph_id",
    limit=5,
    offset=5
)

get_versions(assistant_id: str, metadata: Json = None, limit: int = 10, offset: int = 0) -> list[AssistantVersion]

List all versions of an assistant.

Parameters:

• assistant_id (str) –

  The assistant ID to get versions for.

• metadata (Json, default: None ) –

  Metadata to filter versions by. Exact match filter for each KV pair.

• limit (int, default: 10 ) –

  The maximum number of versions to return.

• offset (int, default: 0 ) –

  The number of versions to skip.

Returns:

• list[AssistantVersion] –

  list[Assistant]: A list of assistants.

Example Usage:

assistant_versions = await client.assistants.get_versions(
    assistant_id="my_assistant_id"
)

set_latest(assistant_id: str, version: int) -> Assistant

Change the version of an assistant.

Parameters:

• assistant_id (str) –

  The assistant ID to delete.

• version (int) –

  The version to change to.

Returns:

• Assistant ( Assistant ) –

  Assistant Object.

Example Usage:

new_version_assistant = await client.assistants.set_latest(
    assistant_id="my_assistant_id",
    version=3
)

SyncThreadsClient

Synchronous client for managing threads in LangGraph.

This class provides methods to create, retrieve, and manage threads, which represent conversations or stateful interactions.

Example:

client = get_sync_client()
thread = client.threads.create(metadata={"user_id": "123"})

get(thread_id: str) -> Thread

Get a thread by ID.

Parameters:

• thread_id (str) –

  The ID of the thread to get.

Returns:

• Thread ( Thread ) –

  Thread object.

Example Usage:

thread = client.threads.get(
    thread_id="my_thread_id"
)
print(thread)

-----------------------------------------------------

{
    'thread_id': 'my_thread_id',
    'created_at': '2024-07-18T18:35:15.540834+00:00',
    'updated_at': '2024-07-18T18:35:15.540834+00:00',
    'metadata': {'graph_id': 'agent'}
}

create(*, metadata: Json = None, thread_id: Optional[str] = None, if_exists: Optional[OnConflictBehavior] = None) -> Thread

Create a new thread.

Parameters:

• metadata (Json, default: None ) –

  Metadata to add to thread.

• thread_id (Optional[str], default: None ) –

  ID of thread. If None, ID will be a randomly generated UUID.

• if_exists (Optional[OnConflictBehavior], default: None ) –

  How to handle duplicate creation. Defaults to 'raise' under the hood. Must be either 'raise' (raise error if duplicate), or 'do_nothing' (return existing thread).

Returns:

• Thread ( Thread ) –

  The created thread.

Example Usage:

thread = client.threads.create(
    metadata={"number":1},
    thread_id="my-thread-id",
    if_exists="raise"
)

update(thread_id: str, *, metadata: dict[str, Any]) -> Thread

Update a thread.

Parameters:

• thread_id (str) –

  ID of thread to update.

• metadata (dict[str, Any]) –

  Metadata to merge with existing thread metadata.

Returns:

• Thread ( Thread ) –

  The created thread.

Example Usage:

thread = client.threads.update(
    thread_id="my-thread-id",
    metadata={"number":1},
)

delete(thread_id: str) -> None

Delete a thread.

Parameters:

• thread_id (str) –

  The ID of the thread to delete.

Returns:

• None –

  None

Example Usage:

client.threads.delete(
    thread_id="my_thread_id"
)

search(*, metadata: Json = None, values: Json = None, status: Optional[ThreadStatus] = None, limit: int = 10, offset: int = 0) -> list[Thread]

Search for threads.

Parameters:

• metadata (Json, default: None ) –

  Thread metadata to filter on.

• values (Json, default: None ) –

  State values to filter on.

• status (Optional[ThreadStatus], default: None ) –

  Thread status to filter on. Must be one of 'idle', 'busy', 'interrupted' or 'error'.

• limit (int, default: 10 ) –

  Limit on number of threads to return.

• offset (int, default: 0 ) –

  Offset in threads table to start search from.

Returns:

• list[Thread] –

  list[Thread]: List of the threads matching the search parameters.

Example Usage:

threads = client.threads.search(
    metadata={"number":1},
    status="interrupted",
    limit=15,
    offset=5
)

copy(thread_id: str) -> None

Copy a thread.

Parameters:

• thread_id (str) –

  The ID of the thread to copy.

Returns:

• None –

  None

Example Usage:

client.threads.copy(
    thread_id="my_thread_id"
)

get_state(thread_id: str, checkpoint: Optional[Checkpoint] = None, checkpoint_id: Optional[str] = None, *, subgraphs: bool = False) -> ThreadState

Get the state of a thread.

Parameters:

• thread_id (str) –

  The ID of the thread to get the state of.

• checkpoint (Optional[Checkpoint], default: None ) –

  The checkpoint to get the state of.

• subgraphs (bool, default: False ) –

  Include subgraphs states.

Returns:

• ThreadState ( ThreadState ) –

  the thread of the state.

Example Usage:

thread_state = client.threads.get_state(
    thread_id="my_thread_id",
    checkpoint_id="my_checkpoint_id"
)
print(thread_state)

----------------------------------------------------------------------------------------------------------------------------------------------------------------------

{
    'values': {
        'messages': [
            {
                'content': 'how are you?',
                'additional_kwargs': {},
                'response_metadata': {},
                'type': 'human',
                'name': None,
                'id': 'fe0a5778-cfe9-42ee-b807-0adaa1873c10',
                'example': False
            },
            {
                'content': "I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, honest, and harmless.",
                'additional_kwargs': {},
                'response_metadata': {},
                'type': 'ai',
                'name': None,
                'id': 'run-159b782c-b679-4830-83c6-cef87798fe8b',
                'example': False,
                'tool_calls': [],
                'invalid_tool_calls': [],
                'usage_metadata': None
            }
        ]
    },
    'next': [],
    'checkpoint':
        {
            'thread_id': 'e2496803-ecd5-4e0c-a779-3226296181c2',
            'checkpoint_ns': '',
            'checkpoint_id': '1ef4a9b8-e6fb-67b1-8001-abd5184439d1'
        }
    'metadata':
        {
            'step': 1,
            'run_id': '1ef4a9b8-d7da-679a-a45a-872054341df2',
            'source': 'loop',
            'writes':
                {
                    'agent':
                        {
                            'messages': [
                                {
                                    'id': 'run-159b782c-b679-4830-83c6-cef87798fe8b',
                                    'name': None,
                                    'type': 'ai',
                                    'content': "I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, honest, and harmless.",
                                    'example': False,
                                    'tool_calls': [],
                                    'usage_metadata': None,
                                    'additional_kwargs': {},
                                    'response_metadata': {},
                                    'invalid_tool_calls': []
                                }
                            ]
                        }
                },
    'user_id': None,
    'graph_id': 'agent',
    'thread_id': 'e2496803-ecd5-4e0c-a779-3226296181c2',
    'created_by': 'system',
    'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'},
    'created_at': '2024-07-25T15:35:44.184703+00:00',
    'parent_config':
        {
            'thread_id': 'e2496803-ecd5-4e0c-a779-3226296181c2',
            'checkpoint_ns': '',
            'checkpoint_id': '1ef4a9b8-d80d-6fa7-8000-9300467fad0f'
        }
}

update_state(thread_id: str, values: Optional[Union[dict, Sequence[dict]]], *, as_node: Optional[str] = None, checkpoint: Optional[Checkpoint] = None, checkpoint_id: Optional[str] = None) -> ThreadUpdateStateResponse

Update the state of a thread.

Parameters:

• thread_id (str) –

  The ID of the thread to update.

• values (Optional[Union[dict, Sequence[dict]]]) –

  The values to update the state with.

• as_node (Optional[str], default: None ) –

  Update the state as if this node had just executed.

• checkpoint (Optional[Checkpoint], default: None ) –

  The checkpoint to update the state of.

Returns:

• ThreadUpdateStateResponse ( ThreadUpdateStateResponse ) –

  Response after updating a thread's state.

Example Usage:

response = client.threads.update_state(
    thread_id="my_thread_id",
    values={"messages":[{"role": "user", "content": "hello!"}]},
    as_node="my_node",
)
print(response)

----------------------------------------------------------------------------------------------------------------------------------------------------------------------

{
    'checkpoint': {
        'thread_id': 'e2496803-ecd5-4e0c-a779-3226296181c2',
        'checkpoint_ns': '',
        'checkpoint_id': '1ef4a9b8-e6fb-67b1-8001-abd5184439d1',
        'checkpoint_map': {}
    }
}

get_history(thread_id: str, *, limit: int = 10, before: Optional[str | Checkpoint] = None, metadata: Optional[dict] = None, checkpoint: Optional[Checkpoint] = None) -> list[ThreadState]

Get the state history of a thread.

Parameters:

• thread_id (str) –

  The ID of the thread to get the state history for.

• checkpoint (Optional[Checkpoint], default: None ) –

  Return states for this subgraph. If empty defaults to root.

• limit (int, default: 10 ) –

  The maximum number of states to return.

• before (Optional[str | Checkpoint], default: None ) –

  Return states before this checkpoint.

• metadata (Optional[dict], default: None ) –

  Filter states by metadata key-value pairs.

Returns:

• list[ThreadState] –

  list[ThreadState]: the state history of the thread.

Example Usage:

thread_state = client.threads.get_history(
    thread_id="my_thread_id",
    limit=5,
    before="my_timestamp",
    metadata={"name":"my_name"}
)

SyncRunsClient

Synchronous client for managing runs in LangGraph.

This class provides methods to create, retrieve, and manage runs, which represent individual executions of graphs.

Example:

client = get_sync_client()
run = client.runs.create(thread_id="thread_123", assistant_id="asst_456")

stream(thread_id: Optional[str], assistant_id: str, *, input: Optional[dict] = None, command: Optional[Command] = None, stream_mode: Union[StreamMode, Sequence[StreamMode]] = 'values', stream_subgraphs: bool = False, metadata: Optional[dict] = None, config: Optional[Config] = None, checkpoint: Optional[Checkpoint] = None, checkpoint_id: Optional[str] = None, interrupt_before: Optional[Union[All, Sequence[str]]] = None, interrupt_after: Optional[Union[All, Sequence[str]]] = None, feedback_keys: Optional[Sequence[str]] = None, on_disconnect: Optional[DisconnectMode] = None, on_completion: Optional[OnCompletionBehavior] = None, webhook: Optional[str] = None, multitask_strategy: Optional[MultitaskStrategy] = None, if_not_exists: Optional[IfNotExists] = None, after_seconds: Optional[int] = None) -> Iterator[StreamPart]

Create a run and stream the results.

Parameters:

• thread_id (Optional[str]) –

  the thread ID to assign to the thread. If None will create a stateless run.

• assistant_id (str) –

  The assistant ID or graph name to stream from. If using graph name, will default to first assistant created from that graph.

• input (Optional[dict], default: None ) –

  The input to the graph.

• command (Optional[Command], default: None ) –

  The command to execute.

• stream_mode (Union[StreamMode, Sequence[StreamMode]], default: 'values' ) –

  The stream mode(s) to use.

• stream_subgraphs (bool, default: False ) –

  Whether to stream output from subgraphs.

• metadata (Optional[dict], default: None ) –

  Metadata to assign to the run.

• config (Optional[Config], default: None ) –

  The configuration for the assistant.

• checkpoint (Optional[Checkpoint], default: None ) –

  The checkpoint to resume from.

• interrupt_before (Optional[Union[All, Sequence[str]]], default: None ) –

  Nodes to interrupt immediately before they get executed.

• interrupt_after (Optional[Union[All, Sequence[str]]], default: None ) –

  Nodes to Nodes to interrupt immediately after they get executed.

• feedback_keys (Optional[Sequence[str]], default: None ) –

  Feedback keys to assign to run.

• on_disconnect (Optional[DisconnectMode], default: None ) –

  The disconnect mode to use. Must be one of 'cancel' or 'continue'.

• on_completion (Optional[OnCompletionBehavior], default: None ) –

  Whether to delete or keep the thread created for a stateless run. Must be one of 'delete' or 'keep'.

• webhook (Optional[str], default: None ) –

  Webhook to call after LangGraph API call is done.

• multitask_strategy (Optional[MultitaskStrategy], default: None ) –

  Multitask strategy to use. Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'.

• if_not_exists (Optional[IfNotExists], default: None ) –

  How to handle missing thread. Defaults to 'reject'. Must be either 'reject' (raise error if missing), or 'create' (create new thread).

• after_seconds (Optional[int], default: None ) –

  The number of seconds to wait before starting the run. Use to schedule future runs.

Returns:

• Iterator[StreamPart] –

  Iterator[StreamPart]: Iterator of stream results.

Example Usage:

async for chunk in client.runs.stream(
    thread_id=None,
    assistant_id="agent",
    input={"messages": [{"role": "user", "content": "how are you?"}]},
    stream_mode=["values","debug"],
    metadata={"name":"my_run"},
    config={"configurable": {"model_name": "anthropic"}},
    interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"],
    interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"],
    feedback_keys=["my_feedback_key_1","my_feedback_key_2"],
    webhook="https://my.fake.webhook.com",
    multitask_strategy="interrupt"
):
    print(chunk)

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

StreamPart(event='metadata', data={'run_id': '1ef4a9b8-d7da-679a-a45a-872054341df2'})
StreamPart(event='values', data={'messages': [{'content': 'how are you?', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'fe0a5778-cfe9-42ee-b807-0adaa1873c10', 'example': False}]})
StreamPart(event='values', data={'messages': [{'content': 'how are you?', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'fe0a5778-cfe9-42ee-b807-0adaa1873c10', 'example': False}, {'content': "I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, honest, and harmless.", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-159b782c-b679-4830-83c6-cef87798fe8b', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]})
StreamPart(event='end', data=None)

create(thread_id: Optional[str], assistant_id: str, *, input: Optional[dict] = None, command: Optional[Command] = None, stream_mode: Union[StreamMode, Sequence[StreamMode]] = 'values', stream_subgraphs: bool = False, metadata: Optional[dict] = None, config: Optional[Config] = None, checkpoint: Optional[Checkpoint] = None, checkpoint_id: Optional[str] = None, interrupt_before: Optional[Union[All, Sequence[str]]] = None, interrupt_after: Optional[Union[All, Sequence[str]]] = None, webhook: Optional[str] = None, multitask_strategy: Optional[MultitaskStrategy] = None, on_completion: Optional[OnCompletionBehavior] = None, if_not_exists: Optional[IfNotExists] = None, after_seconds: Optional[int] = None) -> Run

Create a background run.

Parameters:

• thread_id (Optional[str]) –

  the thread ID to assign to the thread. If None will create a stateless run.

• assistant_id (str) –

  The assistant ID or graph name to stream from. If using graph name, will default to first assistant created from that graph.

• input (Optional[dict], default: None ) –

  The input to the graph.

• command (Optional[Command], default: None ) –

  The command to execute.

• stream_mode (Union[StreamMode, Sequence[StreamMode]], default: 'values' ) –

  The stream mode(s) to use.

• stream_subgraphs (bool, default: False ) –

  Whether to stream output from subgraphs.

• metadata (Optional[dict], default: None ) –

  Metadata to assign to the run.

• config (Optional[Config], default: None ) –

  The configuration for the assistant.

• checkpoint (Optional[Checkpoint], default: None ) –

  The checkpoint to resume from.

• interrupt_before (Optional[Union[All, Sequence[str]]], default: None ) –

  Nodes to interrupt immediately before they get executed.

• interrupt_after (Optional[Union[All, Sequence[str]]], default: None ) –

  Nodes to Nodes to interrupt immediately after they get executed.

• webhook (Optional[str], default: None ) –

  Webhook to call after LangGraph API call is done.

• multitask_strategy (Optional[MultitaskStrategy], default: None ) –

  Multitask strategy to use. Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'.

• on_completion (Optional[OnCompletionBehavior], default: None ) –

  Whether to delete or keep the thread created for a stateless run. Must be one of 'delete' or 'keep'.

• if_not_exists (Optional[IfNotExists], default: None ) –

  How to handle missing thread. Defaults to 'reject'. Must be either 'reject' (raise error if missing), or 'create' (create new thread).

• after_seconds (Optional[int], default: None ) –

  The number of seconds to wait before starting the run. Use to schedule future runs.

Returns:

• Run ( Run ) –

  The created background run.

Example Usage:

background_run = client.runs.create(
    thread_id="my_thread_id",
    assistant_id="my_assistant_id",
    input={"messages": [{"role": "user", "content": "hello!"}]},
    metadata={"name":"my_run"},
    config={"configurable": {"model_name": "openai"}},
    interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"],
    interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"],
    webhook="https://my.fake.webhook.com",
    multitask_strategy="interrupt"
)
print(background_run)

--------------------------------------------------------------------------------

{
    'run_id': 'my_run_id',
    'thread_id': 'my_thread_id',
    'assistant_id': 'my_assistant_id',
    'created_at': '2024-07-25T15:35:42.598503+00:00',
    'updated_at': '2024-07-25T15:35:42.598503+00:00',
    'metadata': {},
    'status': 'pending',
    'kwargs':
        {
            'input':
                {
                    'messages': [
                        {
                            'role': 'user',
                            'content': 'how are you?'
                        }
                    ]
                },
            'config':
                {
                    'metadata':
                        {
                            'created_by': 'system'
                        },
                    'configurable':
                        {
                            'run_id': 'my_run_id',
                            'user_id': None,
                            'graph_id': 'agent',
                            'thread_id': 'my_thread_id',
                            'checkpoint_id': None,
                            'model_name': "openai",
                            'assistant_id': 'my_assistant_id'
                        }
                },
            'webhook': "https://my.fake.webhook.com",
            'temporary': False,
            'stream_mode': ['values'],
            'feedback_keys': None,
            'interrupt_after': ["node_to_stop_after_1","node_to_stop_after_2"],
            'interrupt_before': ["node_to_stop_before_1","node_to_stop_before_2"]
        },
    'multitask_strategy': 'interrupt'
}

create_batch(payloads: list[RunCreate]) -> list[Run]

Create a batch of stateless background runs.

wait(thread_id: Optional[str], assistant_id: str, *, input: Optional[dict] = None, command: Optional[Command] = None, metadata: Optional[dict] = None, config: Optional[Config] = None, checkpoint: Optional[Checkpoint] = None, checkpoint_id: Optional[str] = None, interrupt_before: Optional[Union[All, Sequence[str]]] = None, interrupt_after: Optional[Union[All, Sequence[str]]] = None, webhook: Optional[str] = None, on_disconnect: Optional[DisconnectMode] = None, on_completion: Optional[OnCompletionBehavior] = None, multitask_strategy: Optional[MultitaskStrategy] = None, if_not_exists: Optional[IfNotExists] = None, after_seconds: Optional[int] = None) -> Union[list[dict], dict[str, Any]]

Create a run, wait until it finishes and return the final state.

Parameters:

• thread_id (Optional[str]) –

  the thread ID to create the run on. If None will create a stateless run.

• assistant_id (str) –

  The assistant ID or graph name to run. If using graph name, will default to first assistant created from that graph.

• input (Optional[dict], default: None ) –

  The input to the graph.

• command (Optional[Command], default: None ) –

  The command to execute.

• metadata (Optional[dict], default: None ) –

  Metadata to assign to the run.

• config (Optional[Config], default: None ) –

  The configuration for the assistant.

• checkpoint (Optional[Checkpoint], default: None ) –

  The checkpoint to resume from.

• interrupt_before (Optional[Union[All, Sequence[str]]], default: None ) –

  Nodes to interrupt immediately before they get executed.

• interrupt_after (Optional[Union[All, Sequence[str]]], default: None ) –

  Nodes to Nodes to interrupt immediately after they get executed.

• webhook (Optional[str], default: None ) –

  Webhook to call after LangGraph API call is done.

• on_disconnect (Optional[DisconnectMode], default: None ) –

  The disconnect mode to use. Must be one of 'cancel' or 'continue'.

• on_completion (Optional[OnCompletionBehavior], default: None ) –

  Whether to delete or keep the thread created for a stateless run. Must be one of 'delete' or 'keep'.

• multitask_strategy (Optional[MultitaskStrategy], default: None ) –

  Multitask strategy to use. Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'.

• if_not_exists (Optional[IfNotExists], default: None ) –

  How to handle missing thread. Defaults to 'reject'. Must be either 'reject' (raise error if missing), or 'create' (create new thread).

• after_seconds (Optional[int], default: None ) –

  The number of seconds to wait before starting the run. Use to schedule future runs.

Returns:

• Union[list[dict], dict[str, Any]] –

  Union[list[dict], dict[str, Any]]: The output of the run.

Example Usage:

final_state_of_run = client.runs.wait(
    thread_id=None,
    assistant_id="agent",
    input={"messages": [{"role": "user", "content": "how are you?"}]},
    metadata={"name":"my_run"},
    config={"configurable": {"model_name": "anthropic"}},
    interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"],
    interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"],
    webhook="https://my.fake.webhook.com",
    multitask_strategy="interrupt"
)
print(final_state_of_run)

-------------------------------------------------------------------------------------------------------------------------------------------

{
    'messages': [
        {
            'content': 'how are you?',
            'additional_kwargs': {},
            'response_metadata': {},
            'type': 'human',
            'name': None,
            'id': 'f51a862c-62fe-4866-863b-b0863e8ad78a',
            'example': False
        },
        {
            'content': "I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, honest, and harmless.",
            'additional_kwargs': {},
            'response_metadata': {},
            'type': 'ai',
            'name': None,
            'id': 'run-bf1cd3c6-768f-4c16-b62d-ba6f17ad8b36',
            'example': False,
            'tool_calls': [],
            'invalid_tool_calls': [],
            'usage_metadata': None
        }
    ]
}

list(thread_id: str, *, limit: int = 10, offset: int = 0) -> List[Run]

List runs.

Parameters:

• thread_id (str) –

  The thread ID to list runs for.

• limit (int, default: 10 ) –

  The maximum number of results to return.

• offset (int, default: 0 ) –

  The number of results to skip.

Returns:

• List[Run] –

  List[Run]: The runs for the thread.

Example Usage:

client.runs.delete(
    thread_id="thread_id_to_delete",
    limit=5,
    offset=5,
)

get(thread_id: str, run_id: str) -> Run

Get a run.

Parameters:

• thread_id (str) –

  The thread ID to get.

• run_id (str) –

  The run ID to get.

Returns:

• Run ( Run ) –

  Run object.

Example Usage:

run = client.runs.get(
    thread_id="thread_id_to_delete",
    run_id="run_id_to_delete",
)

cancel(thread_id: str, run_id: str, *, wait: bool = False, action: CancelAction = 'interrupt') -> None

Get a run.

Parameters:

• thread_id (str) –

  The thread ID to cancel.

• run_id (str) –

  The run ID to cancek.

• wait (bool, default: False ) –

  Whether to wait until run has completed.

• action (CancelAction, default: 'interrupt' ) –

  Action to take when cancelling the run. Possible values are interrupt or rollback. Default is interrupt.

Returns:

• None –

  None

Example Usage:

client.runs.cancel(
    thread_id="thread_id_to_cancel",
    run_id="run_id_to_cancel",
    wait=True,
    action="interrupt"
)

join(thread_id: str, run_id: str) -> dict

Block until a run is done. Returns the final state of the thread.

Parameters:

• thread_id (str) –

  The thread ID to join.

• run_id (str) –

  The run ID to join.

Returns:

• dict –

  None

Example Usage:

client.runs.join(
    thread_id="thread_id_to_join",
    run_id="run_id_to_join"
)

join_stream(thread_id: str, run_id: str) -> Iterator[StreamPart]

Stream output from a run in real-time, until the run is done. Output is not buffered, so any output produced before this call will not be received here.

Parameters:

• thread_id (str) –

  The thread ID to join.

• run_id (str) –

  The run ID to join.

Returns:

• Iterator[StreamPart] –

  None

Example Usage:

client.runs.join_stream(
    thread_id="thread_id_to_join",
    run_id="run_id_to_join"
)

delete(thread_id: str, run_id: str) -> None

Delete a run.

Parameters:

• thread_id (str) –

  The thread ID to delete.

• run_id (str) –

  The run ID to delete.

Returns:

• None –

  None

Example Usage:

client.runs.delete(
    thread_id="thread_id_to_delete",
    run_id="run_id_to_delete"
)

SyncCronClient

Synchronous client for managing cron jobs in LangGraph.

This class provides methods to create and manage scheduled tasks (cron jobs) for automated graph executions.

Example:

client = get_sync_client()
cron_job = client.crons.create_for_thread(thread_id="thread_123", assistant_id="asst_456", schedule="0 * * * *")

create_for_thread(thread_id: str, assistant_id: str, *, schedule: str, input: Optional[dict] = None, metadata: Optional[dict] = None, config: Optional[Config] = None, interrupt_before: Optional[Union[All, list[str]]] = None, interrupt_after: Optional[Union[All, list[str]]] = None, webhook: Optional[str] = None, multitask_strategy: Optional[str] = None) -> Run

Create a cron job for a thread.

Parameters:

• thread_id (str) –

  the thread ID to run the cron job on.

• assistant_id (str) –

  The assistant ID or graph name to use for the cron job. If using graph name, will default to first assistant created from that graph.

• schedule (str) –

  The cron schedule to execute this job on.

• input (Optional[dict], default: None ) –

  The input to the graph.

• metadata (Optional[dict], default: None ) –

  Metadata to assign to the cron job runs.

• config (Optional[Config], default: None ) –

  The configuration for the assistant.

• interrupt_before (Optional[Union[All, list[str]]], default: None ) –

  Nodes to interrupt immediately before they get executed.

• interrupt_after (Optional[Union[All, list[str]]], default: None ) –

  Nodes to Nodes to interrupt immediately after they get executed.

• webhook (Optional[str], default: None ) –

  Webhook to call after LangGraph API call is done.

• multitask_strategy (Optional[str], default: None ) –

  Multitask strategy to use. Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'.

Returns:

• Run ( Run ) –

  The cron run.

Example Usage:

cron_run = client.crons.create_for_thread(
    thread_id="my-thread-id",
    assistant_id="agent",
    schedule="27 15 * * *",
    input={"messages": [{"role": "user", "content": "hello!"}]},
    metadata={"name":"my_run"},
    config={"configurable": {"model_name": "openai"}},
    interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"],
    interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"],
    webhook="https://my.fake.webhook.com",
    multitask_strategy="interrupt"
)

create(assistant_id: str, *, schedule: str, input: Optional[dict] = None, metadata: Optional[dict] = None, config: Optional[Config] = None, interrupt_before: Optional[Union[All, list[str]]] = None, interrupt_after: Optional[Union[All, list[str]]] = None, webhook: Optional[str] = None, multitask_strategy: Optional[str] = None) -> Run

Create a cron run.

Parameters:

• assistant_id (str) –

  The assistant ID or graph name to use for the cron job. If using graph name, will default to first assistant created from that graph.

• schedule (str) –

  The cron schedule to execute this job on.

• input (Optional[dict], default: None ) –

  The input to the graph.

• metadata (Optional[dict], default: None ) –

  Metadata to assign to the cron job runs.

• config (Optional[Config], default: None ) –

  The configuration for the assistant.

• interrupt_before (Optional[Union[All, list[str]]], default: None ) –

  Nodes to interrupt immediately before they get executed.

• interrupt_after (Optional[Union[All, list[str]]], default: None ) –

  Nodes to Nodes to interrupt immediately after they get executed.

• webhook (Optional[str], default: None ) –

  Webhook to call after LangGraph API call is done.

• multitask_strategy (Optional[str], default: None ) –

  Multitask strategy to use. Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'.

Returns:

• Run ( Run ) –

  The cron run.

Example Usage:

cron_run = client.crons.create(
    assistant_id="agent",
    schedule="27 15 * * *",
    input={"messages": [{"role": "user", "content": "hello!"}]},
    metadata={"name":"my_run"},
    config={"configurable": {"model_name": "openai"}},
    interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"],
    interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"],
    webhook="https://my.fake.webhook.com",
    multitask_strategy="interrupt"
)

delete(cron_id: str) -> None

Delete a cron.

Parameters:

• cron_id (str) –

  The cron ID to delete.

Returns:

• None –

  None

Example Usage:

client.crons.delete(
    cron_id="cron_to_delete"
)

search(*, assistant_id: Optional[str] = None, thread_id: Optional[str] = None, limit: int = 10, offset: int = 0) -> list[Cron]

Get a list of cron jobs.

Parameters:

• assistant_id (Optional[str], default: None ) –

  The assistant ID or graph name to search for.

• thread_id (Optional[str], default: None ) –

  the thread ID to search for.

• limit (int, default: 10 ) –

  The maximum number of results to return.

• offset (int, default: 0 ) –

  The number of results to skip.

Returns:

• list[Cron] –

  list[Cron]: The list of cron jobs returned by the search,

Example Usage:

cron_jobs = client.crons.search(
    assistant_id="my_assistant_id",
    thread_id="my_thread_id",
    limit=5,
    offset=5,
)
print(cron_jobs)

----------------------------------------------------------

[
    {
        'cron_id': '1ef3cefa-4c09-6926-96d0-3dc97fd5e39b',
        'assistant_id': 'my_assistant_id',
        'thread_id': 'my_thread_id',
        'user_id': None,
        'payload':
            {
                'input': {'start_time': ''},
                'schedule': '4 * * * *',
                'assistant_id': 'my_assistant_id'
            },
        'schedule': '4 * * * *',
        'next_run_date': '2024-07-25T17:04:00+00:00',
        'end_time': None,
        'created_at': '2024-07-08T06:02:23.073257+00:00',
        'updated_at': '2024-07-08T06:02:23.073257+00:00'
    }
]

SyncStoreClient

A client for synchronous operations on a key-value store.

Provides methods to interact with a remote key-value store, allowing storage and retrieval of items within namespaced hierarchies.

Example:

client = get_sync_client()
client.store.put_item(["users", "profiles"], "user123", {"name": "Alice", "age": 30})

put_item(namespace: Sequence[str], /, key: str, value: dict[str, Any], index: Optional[Union[Literal[False], list[str]]] = None) -> None

Store or update an item.

Parameters:

• namespace (Sequence[str]) –

  A list of strings representing the namespace path.

• key (str) –

  The unique identifier for the item within the namespace.

• value (dict[str, Any]) –

  A dictionary containing the item's data.

• index (Optional[Union[Literal[False], list[str]]], default: None ) –

  Controls search indexing - None (use defaults), False (disable), or list of field paths to index.

Returns:

• None –

  None

Example Usage:

client.store.put_item(
    ["documents", "user123"],
    key="item456",
    value={"title": "My Document", "content": "Hello World"}
)

get_item(namespace: Sequence[str], /, key: str) -> Item

Retrieve a single item.

Parameters:

• key (str) –

  The unique identifier for the item.

• namespace (Sequence[str]) –

  Optional list of strings representing the namespace path.

Returns:

• Item ( Item ) –

  The retrieved item.

Example Usage:

item = client.store.get_item(
    ["documents", "user123"],
    key="item456",
)
print(item)

----------------------------------------------------------------

{
    'namespace': ['documents', 'user123'],
    'key': 'item456',
    'value': {'title': 'My Document', 'content': 'Hello World'},
    'created_at': '2024-07-30T12:00:00Z',
    'updated_at': '2024-07-30T12:00:00Z'
}

delete_item(namespace: Sequence[str], /, key: str) -> None

Delete an item.

Parameters:

• key (str) –

  The unique identifier for the item.

• namespace (Sequence[str]) –

  Optional list of strings representing the namespace path.

Returns:

• None –

  None

Example Usage:

client.store.delete_item(
    ["documents", "user123"],
    key="item456",
)

search_items(namespace_prefix: Sequence[str], /, filter: Optional[dict[str, Any]] = None, limit: int = 10, offset: int = 0, query: Optional[str] = None) -> SearchItemsResponse

Search for items within a namespace prefix.

Parameters:

• namespace_prefix (Sequence[str]) –

  List of strings representing the namespace prefix.

• filter (Optional[dict[str, Any]], default: None ) –

  Optional dictionary of key-value pairs to filter results.

• limit (int, default: 10 ) –

  Maximum number of items to return (default is 10).

• offset (int, default: 0 ) –

  Number of items to skip before returning results (default is 0).

• query (Optional[str], default: None ) –

  Optional query for natural language search.

Returns:

• SearchItemsResponse –

  List[Item]: A list of items matching the search criteria.

Example Usage:

items = client.store.search_items(
    ["documents"],
    filter={"author": "John Doe"},
    limit=5,
    offset=0
)
print(items)

----------------------------------------------------------------

{
    "items": [
        {
            "namespace": ["documents", "user123"],
            "key": "item789",
            "value": {
                "title": "Another Document",
                "author": "John Doe"
            },
            "created_at": "2024-07-30T12:00:00Z",
            "updated_at": "2024-07-30T12:00:00Z"
        },
        # ... additional items ...
    ]
}

list_namespaces(prefix: Optional[List[str]] = None, suffix: Optional[List[str]] = None, max_depth: Optional[int] = None, limit: int = 100, offset: int = 0) -> ListNamespaceResponse

List namespaces with optional match conditions.

Parameters:

• prefix (Optional[List[str]], default: None ) –

  Optional list of strings representing the prefix to filter namespaces.

• suffix (Optional[List[str]], default: None ) –

  Optional list of strings representing the suffix to filter namespaces.

• max_depth (Optional[int], default: None ) –

  Optional integer specifying the maximum depth of namespaces to return.

• limit (int, default: 100 ) –

  Maximum number of namespaces to return (default is 100).

• offset (int, default: 0 ) –

  Number of namespaces to skip before returning results (default is 0).

Returns:

• ListNamespaceResponse –

  List[List[str]]: A list of namespaces matching the criteria.

Example Usage:

namespaces = client.store.list_namespaces(
    prefix=["documents"],
    max_depth=3,
    limit=10,
    offset=0
)
print(namespaces)

----------------------------------------------------------------

[
    ["documents", "user123", "reports"],
    ["documents", "user456", "invoices"],
    ...
]

get_headers(api_key: Optional[str], custom_headers: Optional[dict[str, str]]) -> dict[str, str]

Combine api_key and custom user-provided headers.

get_client(*, url: Optional[str] = None, api_key: Optional[str] = None, headers: Optional[dict[str, str]] = None) -> LangGraphClient

Get a LangGraphClient instance.

Parameters:

• url (Optional[str], default: None ) –

  The URL of the LangGraph API.

• api_key (Optional[str], default: None ) –

  The API key. If not provided, it will be read from the environment. Precedence: 1. explicit argument 2. LANGGRAPH_API_KEY 3. LANGSMITH_API_KEY 4. LANGCHAIN_API_KEY

• headers (Optional[dict[str, str]], default: None ) –

  Optional custom headers

Returns:

• LangGraphClient ( LangGraphClient ) –

  The top-level client for accessing AssistantsClient,

• LangGraphClient –

  ThreadsClient, RunsClient, and CronClient.

Example:

from langgraph_sdk import get_client

# get top-level LangGraphClient
client = get_client(url="http://localhost:8123")

# example usage: client.<model>.<method_name>()
assistants = await client.assistants.get(assistant_id="some_uuid")

get_sync_client(*, url: Optional[str] = None, api_key: Optional[str] = None, headers: Optional[dict[str, str]] = None) -> SyncLangGraphClient

Get a synchronous LangGraphClient instance.

Parameters:

• url (Optional[str], default: None ) –

  The URL of the LangGraph API.

• api_key (Optional[str], default: None ) –

  The API key. If not provided, it will be read from the environment. Precedence: 1. explicit argument 2. LANGGRAPH_API_KEY 3. LANGSMITH_API_KEY 4. LANGCHAIN_API_KEY

• headers (Optional[dict[str, str]], default: None ) –

  Optional custom headers

Returns: SyncLangGraphClient: The top-level synchronous client for accessing AssistantsClient, ThreadsClient, RunsClient, and CronClient.

Example:

from langgraph_sdk import get_sync_client

# get top-level synchronous LangGraphClient
client = get_sync_client(url="http://localhost:8123")

# example usage: client.<model>.<method_name>()
assistant = client.assistants.get(assistant_id="some_uuid")

Data models for interacting with the LangGraph API.

Json = Optional[dict[str, Any]] module-attribute

Represents a JSON-like structure, which can be None or a dictionary with string keys and any values.

RunStatus = Literal['pending', 'error', 'success', 'timeout', 'interrupted'] module-attribute

Represents the status of a run: - "pending": The run is waiting to start. - "error": The run encountered an error and stopped. - "success": The run completed successfully. - "timeout": The run exceeded its time limit. - "interrupted": The run was manually stopped or interrupted.

ThreadStatus = Literal['idle', 'busy', 'interrupted', 'error'] module-attribute

Represents the status of a thread: - "idle": The thread is not currently processing any task. - "busy": The thread is actively processing a task. - "interrupted": The thread's execution was interrupted. - "error": An exception occurred during task processing.

StreamMode = Literal['values', 'messages', 'updates', 'events', 'debug', 'custom', 'messages-tuple'] module-attribute

Defines the mode of streaming: - "values": Stream only the values. - "messages": Stream complete messages. - "updates": Stream updates to the state. - "events": Stream events occurring during execution. - "debug": Stream detailed debug information. - "custom": Stream custom events.

DisconnectMode = Literal['cancel', 'continue'] module-attribute

Specifies behavior on disconnection: - "cancel": Cancel the operation on disconnection. - "continue": Continue the operation even if disconnected.

MultitaskStrategy = Literal['reject', 'interrupt', 'rollback', 'enqueue'] module-attribute

Defines how to handle multiple tasks: - "reject": Reject new tasks when busy. - "interrupt": Interrupt current task for new ones. - "rollback": Roll back current task and start new one. - "enqueue": Queue new tasks for later execution.

OnConflictBehavior = Literal['raise', 'do_nothing'] module-attribute

Specifies behavior on conflict: - "raise": Raise an exception when a conflict occurs. - "do_nothing": Ignore conflicts and proceed.

OnCompletionBehavior = Literal['delete', 'keep'] module-attribute

Defines action after completion: - "delete": Delete resources after completion. - "keep": Retain resources after completion.

All = Literal['*'] module-attribute

Represents a wildcard or 'all' selector.

IfNotExists = Literal['create', 'reject'] module-attribute

Specifies behavior if the thread doesn't exist: - "create": Create a new thread if it doesn't exist. - "reject": Reject the operation if the thread doesn't exist.

CancelAction = Literal['interrupt', 'rollback'] module-attribute

Action to take when cancelling the run. - "interrupt": Simply cancel the run. - "rollback": Cancel the run. Then delete the run and associated checkpoints.

Config

Bases: TypedDict

Configuration options for a call.

tags: list[str] instance-attribute

Tags for this call and any sub-calls (eg. a Chain calling an LLM). You can use these to filter calls.

recursion_limit: int instance-attribute

Maximum number of times a call can recurse. If not provided, defaults to 25.

configurable: dict[str, Any] instance-attribute

Runtime values for attributes previously made configurable on this Runnable, or sub-Runnables, through .configurable_fields() or .configurable_alternatives(). Check .output_schema() for a description of the attributes that have been made configurable.

Checkpoint

Bases: TypedDict

Represents a checkpoint in the execution process.

thread_id: str instance-attribute

Unique identifier for the thread associated with this checkpoint.

checkpoint_ns: str instance-attribute

Namespace for the checkpoint, used for organization and retrieval.

checkpoint_id: Optional[str] instance-attribute

Optional unique identifier for the checkpoint itself.

checkpoint_map: Optional[dict[str, Any]] instance-attribute

Optional dictionary containing checkpoint-specific data.

GraphSchema

Bases: TypedDict

Defines the structure and properties of a graph.

graph_id: str instance-attribute

The ID of the graph.

input_schema: Optional[dict] instance-attribute

The schema for the graph input. Missing if unable to generate JSON schema from graph.

output_schema: Optional[dict] instance-attribute

The schema for the graph output. Missing if unable to generate JSON schema from graph.

state_schema: Optional[dict] instance-attribute

The schema for the graph state. Missing if unable to generate JSON schema from graph.

config_schema: Optional[dict] instance-attribute

The schema for the graph config. Missing if unable to generate JSON schema from graph.

AssistantBase

Bases: TypedDict

Base model for an assistant.

assistant_id: str instance-attribute

The ID of the assistant.

graph_id: str instance-attribute

The ID of the graph.

config: Config instance-attribute

The assistant config.

created_at: datetime instance-attribute

The time the assistant was created.

metadata: Json instance-attribute

The assistant metadata.

version: int instance-attribute

The version of the assistant

AssistantVersion

Bases: AssistantBase

Represents a specific version of an assistant.

assistant_id: str instance-attribute

The ID of the assistant.

graph_id: str instance-attribute

The ID of the graph.

config: Config instance-attribute

The assistant config.

created_at: datetime instance-attribute

The time the assistant was created.

metadata: Json instance-attribute

The assistant metadata.

version: int instance-attribute

The version of the assistant

Assistant

Bases: AssistantBase

Represents an assistant with additional properties.

assistant_id: str instance-attribute

The ID of the assistant.

graph_id: str instance-attribute

The ID of the graph.

config: Config instance-attribute

The assistant config.

created_at: datetime instance-attribute

The time the assistant was created.

metadata: Json instance-attribute

The assistant metadata.

version: int instance-attribute

The version of the assistant

updated_at: datetime instance-attribute

The last time the assistant was updated.

name: str instance-attribute

The name of the assistant

Interrupt

Bases: TypedDict

Represents an interruption in the execution flow.

value: Any instance-attribute

The value associated with the interrupt.

when: Literal['during'] instance-attribute

When the interrupt occurred.

resumable: bool instance-attribute

Whether the interrupt can be resumed.

ns: Optional[list[str]] instance-attribute

Optional namespace for the interrupt.

Thread

Bases: TypedDict

Represents a conversation thread.

thread_id: str instance-attribute

The ID of the thread.

created_at: datetime instance-attribute

The time the thread was created.

updated_at: datetime instance-attribute

The last time the thread was updated.

metadata: Json instance-attribute

The thread metadata.

status: ThreadStatus instance-attribute

The status of the thread, one of 'idle', 'busy', 'interrupted'.

values: Json instance-attribute

The current state of the thread.

interrupts: Dict[str, list[Interrupt]] instance-attribute

Interrupts which were thrown in this thread

ThreadTask

Bases: TypedDict

Represents a task within a thread.

ThreadState

Bases: TypedDict

Represents the state of a thread.

values: Union[list[dict], dict[str, Any]] instance-attribute

The state values.

next: Sequence[str] instance-attribute

The next nodes to execute. If empty, the thread is done until new input is received.

checkpoint: Checkpoint instance-attribute

The ID of the checkpoint.

metadata: Json instance-attribute

Metadata for this state

created_at: Optional[str] instance-attribute

Timestamp of state creation

parent_checkpoint: Optional[Checkpoint] instance-attribute

The ID of the parent checkpoint. If missing, this is the root checkpoint.

tasks: Sequence[ThreadTask] instance-attribute

Tasks to execute in this step. If already attempted, may contain an error.

ThreadUpdateStateResponse

Bases: TypedDict

Represents the response from updating a thread's state.

checkpoint: Checkpoint instance-attribute

Checkpoint of the latest state.

Run

Bases: TypedDict

Represents a single execution run.

run_id: str instance-attribute

The ID of the run.

thread_id: str instance-attribute

The ID of the thread.

assistant_id: str instance-attribute

The assistant that was used for this run.

created_at: datetime instance-attribute

The time the run was created.

updated_at: datetime instance-attribute

The last time the run was updated.

status: RunStatus instance-attribute

The status of the run. One of 'pending', 'running', "error", 'success', "timeout", "interrupted".

metadata: Json instance-attribute

The run metadata.

multitask_strategy: MultitaskStrategy instance-attribute

Strategy to handle concurrent runs on the same thread.

Cron

Bases: TypedDict

Represents a scheduled task.

cron_id: str instance-attribute

The ID of the cron.

thread_id: Optional[str] instance-attribute

The ID of the thread.

end_time: Optional[datetime] instance-attribute

The end date to stop running the cron.

schedule: str instance-attribute

The schedule to run, cron format.

created_at: datetime instance-attribute

The time the cron was created.

updated_at: datetime instance-attribute

The last time the cron was updated.

payload: dict instance-attribute

The run payload to use for creating new run.

RunCreate

Bases: TypedDict

Defines the parameters for initiating a background run.

thread_id: Optional[str] instance-attribute

The identifier of the thread to run. If not provided, the run is stateless.

assistant_id: str instance-attribute

The identifier of the assistant to use for this run.

input: Optional[dict] instance-attribute

Initial input data for the run.

metadata: Optional[dict] instance-attribute

Additional metadata to associate with the run.

config: Optional[Config] instance-attribute

Configuration options for the run.

checkpoint_id: Optional[str] instance-attribute

The identifier of a checkpoint to resume from.

interrupt_before: Optional[list[str]] instance-attribute

List of node names to interrupt execution before.

interrupt_after: Optional[list[str]] instance-attribute

List of node names to interrupt execution after.

webhook: Optional[str] instance-attribute

URL to send webhook notifications about the run's progress.

multitask_strategy: Optional[MultitaskStrategy] instance-attribute

Strategy for handling concurrent runs on the same thread.

Item

Bases: TypedDict

Represents a single document or data entry in the graph's Store.

Items are used to store cross-thread memories.

namespace: list[str] instance-attribute

The namespace of the item. A namespace is analogous to a document's directory.

key: str instance-attribute

The unique identifier of the item within its namespace.

In general, keys needn't be globally unique.

value: dict[str, Any] instance-attribute

The value stored in the item. This is the document itself.

created_at: datetime instance-attribute

The timestamp when the item was created.

updated_at: datetime instance-attribute

The timestamp when the item was last updated.

ListNamespaceResponse

Bases: TypedDict

Response structure for listing namespaces.

namespaces: list[list[str]] instance-attribute

A list of namespace paths, where each path is a list of strings.

SearchItem

Bases: Item

Item with an optional relevance score from search operations.

Attributes:

• score (Optional[float]) –

  Relevance/similarity score. Included when searching a compatible store with a natural language query.

namespace: list[str] instance-attribute

The namespace of the item. A namespace is analogous to a document's directory.

key: str instance-attribute

The unique identifier of the item within its namespace.

In general, keys needn't be globally unique.

value: dict[str, Any] instance-attribute

The value stored in the item. This is the document itself.

created_at: datetime instance-attribute

The timestamp when the item was created.

updated_at: datetime instance-attribute

The timestamp when the item was last updated.

SearchItemsResponse

Bases: TypedDict

Response structure for searching items.

items: list[SearchItem] instance-attribute

A list of items matching the search criteria.

StreamPart

Bases: NamedTuple

Represents a part of a stream response.

event: str instance-attribute

The type of event for this stream part.

data: dict instance-attribute

The data payload associated with the event.

Auth

Add custom authentication and authorization management to your LangGraph application.

The Auth class provides a unified system for handling authentication and authorization in LangGraph applications. It supports custom user authentication protocols and fine-grained authorization rules for different resources and actions.

To use, create a separate python file and add the path to the file to your LangGraph API configuration file (langgraph.json). Within that file, create an instance of the Auth class and register authentication and authorization handlers as needed.

Example langgraph.json file:

{
  "dependencies": ["."],
  "graphs": {
    "agent": "./my_agent/agent.py:graph"
  },
  "env": ".env",
  "auth": {
    "path": "./auth.py:my_auth"
  }

Then the LangGraph server will load your auth file and run it server-side whenever a request comes in.

Basic Usage

from langgraph_sdk import Auth

my_auth = Auth()

async def verify_token(token: str) -> str:
    # Verify token and return user_id
    # This would typically be a call to your auth server
    return "user_id"

@auth.authenticate
async def authenticate(authorization: str) -> str:
    # Verify token and return user_id
    result = await verify_token(authorization)
    if result != "user_id":
        raise Auth.exceptions.HTTPException(
            status_code=401, detail="Unauthorized"
        )
    return result

# Global fallback handler
@auth.on
async def authorize_default(params: Auth.on.value):
    return False # Reject all requests (default behavior)

@auth.on.threads.create
async def authorize_thread_create(params: Auth.on.threads.create.value):
    # Allow the allowed user to create a thread
    assert params.get("metadata", {}).get("owner") == "allowed_user"

Request Processing Flow

1. Authentication (your @auth.authenticate handler) is performed first on every request
2. For authorization, the most specific matching handler is called:
   • If a handler exists for the exact resource and action, it is used (e.g., @auth.on.threads.create)
   • Otherwise, if a handler exists for the resource with any action, it is used (e.g., @auth.on.threads)
   • Finally, if no specific handlers match, the global handler is used (e.g., @auth.on)
   • If no global handler is set, the request is accepted

This allows you to set default behavior with a global handler while overriding specific routes as needed.

types = types class-attribute instance-attribute

Reference to auth type definitions.

Provides access to all type definitions used in the auth system, like ThreadsCreate, AssistantsRead, etc.

exceptions = exceptions class-attribute instance-attribute

Reference to auth exception definitions.

Provides access to all exception definitions used in the auth system, like HTTPException, etc.

on = _On(self) instance-attribute

Entry point for authorization handlers that control access to specific resources.

The on class provides a flexible way to define authorization rules for different resources and actions in your application. It supports three main usage patterns:

1. Global handlers that run for all resources and actions
2. Resource-specific handlers that run for all actions on a resource
3. Resource and action specific handlers for fine-grained control

Each handler must be an async function that accepts two parameters

• ctx (AuthContext): Contains request context and authenticated user info
• value: The data being authorized (type varies by endpoint)

The handler should return one of:

- None or True: Accept the request
- False: Reject with 403 error
- FilterType: Apply filtering rules to the response

Examples

Global handler for all requests:

@auth.on
async def reject_unhandled_requests(ctx: AuthContext, value: Any) -> None:
    print(f"Request to {ctx.path} by {ctx.user.identity}")
    return False

Resource-specific handler. This would take precedence over the global handler for all actions on the threads resource:

@auth.on.threads
async def check_thread_access(ctx: AuthContext, value: Any) -> bool:
    # Allow access only to threads created by the user
    return value.get("created_by") == ctx.user.identity

Resource and action specific handler:

@auth.on.threads.delete
async def prevent_thread_deletion(ctx: AuthContext, value: Any) -> bool:
    # Only admins can delete threads
    return "admin" in ctx.user.permissions

Multiple resources or actions:

@auth.on(resources=["threads", "runs"], actions=["create", "update"])
async def rate_limit_writes(ctx: AuthContext, value: Any) -> bool:
    # Implement rate limiting for write operations
    return await check_rate_limit(ctx.user.identity)

authenticate(fn: AH) -> AH

Register an authentication handler function.

The authentication handler is responsible for verifying credentials and returning user scopes. It can accept any of the following parameters by name:

- request (Request): The raw ASGI request object
- body (dict): The parsed request body
- path (str): The request path, e.g., "/threads/abcd-1234-abcd-1234/runs/abcd-1234-abcd-1234/stream"
- method (str): The HTTP method, e.g., "GET"
- path_params (dict[str, str]): URL path parameters, e.g., {"thread_id": "abcd-1234-abcd-1234", "run_id": "abcd-1234-abcd-1234"}
- query_params (dict[str, str]): URL query parameters, e.g., {"stream": "true"}
- headers (dict[bytes, bytes]): Request headers
- authorization (str | None): The Authorization header value (e.g., "Bearer <token>")

Parameters:

• fn (Callable) –

  The authentication handler function to register. Must return a representation of the user. This could be a: - string (the user id) - dict containing {"identity": str, "permissions": list[str]} - or an object with identity and permissions properties Permissions can be optionally used by your handlers downstream.

Returns:

• AH –

  The registered handler function.

Raises:

• ValueError –

  If an authentication handler is already registered.

Examples

Basic token authentication:

@auth.authenticate
async def authenticate(authorization: str) -> str:
    user_id = verify_token(authorization)
    return user_id

Accept the full request context:

@auth.authenticate
async def authenticate(
    method: str,
    path: str,
    headers: dict[str, bytes]
) -> str:
    user = await verify_request(method, path, headers)
    return user

Return user name and permissions:

@auth.authenticate
async def authenticate(
    method: str,
    path: str,
    headers: dict[str, bytes]
) -> Auth.types.MinimalUserDict:
    permissions, user = await verify_request(method, path, headers)
    # Permissions could be things like ["runs:read", "runs:write", "threads:read", "threads:write"]
    return {
        "identity": user["id"],
        "permissions": permissions,
        "display_name": user["name"],
    }

Authentication and authorization types for LangGraph.

This module defines the core types used for authentication, authorization, and request handling in LangGraph. It includes user protocols, authentication contexts, and typed dictionaries for various API operations.

Note

All typing.TypedDict classes use total=False to make all fields optional by default.

RunStatus = typing.Literal['pending', 'error', 'success', 'timeout', 'interrupted'] module-attribute

Status of a run execution.

Values

• pending: Run is queued or in progress
• error: Run failed with an error
• success: Run completed successfully
• timeout: Run exceeded time limit
• interrupted: Run was manually interrupted

MultitaskStrategy = typing.Literal['reject', 'rollback', 'interrupt', 'enqueue'] module-attribute

Strategy for handling multiple concurrent tasks.

Values

• reject: Reject new tasks while one is in progress
• rollback: Cancel current task and start new one
• interrupt: Interrupt current task and start new one
• enqueue: Queue new tasks to run after current one

OnConflictBehavior = typing.Literal['raise', 'do_nothing'] module-attribute

Behavior when encountering conflicts.

Values

• raise: Raise an exception on conflict
• do_nothing: Silently ignore conflicts

IfNotExists = typing.Literal['create', 'reject'] module-attribute

Behavior when an entity doesn't exist.

Values

• create: Create the entity
• reject: Reject the operation

FilterType = typing.Union[typing.Dict[str, typing.Union[str, typing.Dict[typing.Literal['$eq', '$contains'], str]]], typing.Dict[str, str]] module-attribute

Response type for authorization handlers.

Supports exact matches and operators

• Exact match shorthand: {"field": "value"}
• Exact match: {"field": {"$eq": "value"}}
• Contains: {"field": {"$contains": "value"}}

Examples

Simple exact match filter for the resource owner:

filter = {"owner": "user-abcd123"}

Explicit version of the exact match filter:

filter = {"owner": {"$eq": "user-abcd123"}}

Containment:

filter = {"participants": {"$contains": "user-abcd123"}}

Combining filters (treated as a logical AND):

filter = {"owner": "user-abcd123", "participants": {"$contains": "user-efgh456"}}

ThreadStatus = typing.Literal['idle', 'busy', 'interrupted', 'error'] module-attribute

Status of a thread.

Values

• idle: Thread is available for work
• busy: Thread is currently processing
• interrupted: Thread was interrupted
• error: Thread encountered an error

MetadataInput = typing.Dict[str, typing.Any] module-attribute

Type for arbitrary metadata attached to entities.

Allows storing custom key-value pairs with any entity. Keys must be strings, values can be any JSON-serializable type.

Examples

metadata = {
    "created_by": "user123",
    "priority": 1,
    "tags": ["important", "urgent"]
}

HandlerResult = typing.Union[None, bool, FilterType] module-attribute

The result of a handler can be: * None | True: accept the request. * False: reject the request with a 403 error * FilterType: filter to apply

Authenticator = Callable[..., Awaitable[typing.Union[MinimalUser, str, BaseUser, MinimalUserDict, typing.Mapping[str, typing.Any]]]] module-attribute

Type for authentication functions.

An authenticator can return either: 1. A string (user_id) 2. A dict containing {"identity": str, "permissions": list[str]} 3. An object with identity and permissions properties

Permissions can be used downstream by your authorization logic to determine access permissions to different resources.

The authenticate decorator will automatically inject any of the following parameters by name if they are included in your function signature:

Parameters:

• request (Request) –

  The raw ASGI request object

• body (dict) –

  The parsed request body

• path (str) –

  The request path

• method (str) –

  The HTTP method (GET, POST, etc.)

• path_params (dict[str, str] | None) –

  URL path parameters

• query_params (dict[str, str] | None) –

  URL query parameters

• headers (dict[str, bytes] | None) –

  Request headers

• authorization (str | None) –

  The Authorization header value (e.g. "Bearer ")

Examples

Basic authentication with token:

from langgraph_sdk import Auth

auth = Auth()

@auth.authenticate
async def authenticate1(authorization: str) -> Auth.types.MinimalUserDict:
    return await get_user(authorization)

Authentication with multiple parameters:

@auth.authenticate
async def authenticate2(
    method: str,
    path: str,
    headers: dict[str, bytes]
) -> Auth.types.MinimalUserDict:
    # Custom auth logic using method, path and headers
    user = verify_request(method, path, headers)
    return user

Accepting the raw ASGI request:

MY_SECRET = "my-secret-key"
@auth.authenticate
async def get_current_user(request: Request) -> Auth.types.MinimalUserDict:
    try:
        token = (request.headers.get("authorization") or "").split(" ", 1)[1]
        payload = jwt.decode(token, MY_SECRET, algorithms=["HS256"])
    except (IndexError, InvalidTokenError):
        raise HTTPException(
            status_code=401,
            detail="Invalid token",
            headers={"WWW-Authenticate": "Bearer"},
        )

    async with httpx.AsyncClient() as client:
        response = await client.get(
            f"https://api.myauth-provider.com/auth/v1/user",
            headers={"Authorization": f"Bearer {MY_SECRET}"}
        )
        if response.status_code != 200:
            raise HTTPException(status_code=401, detail="User not found")

        user_data = response.json()
        return {
            "identity": user_data["id"],
            "display_name": user_data.get("name"),
            "permissions": user_data.get("permissions", []),
            "is_authenticated": True,
        }

MinimalUser

Bases: Protocol

User objects must at least expose the identity property.

identity: str property

The unique identifier for the user.

This could be a username, email, or any other unique identifier used to distinguish between different users in the system.

MinimalUserDict

Bases: TypedDict

The dictionary representation of a user.

identity: typing_extensions.Required[str] instance-attribute

The required unique identifier for the user.

display_name: str instance-attribute

The optional display name for the user.

is_authenticated: bool instance-attribute

Whether the user is authenticated. Defaults to True.

permissions: Sequence[str] instance-attribute

A list of permissions associated with the user.

You can use these in your @auth.on authorization logic to determine access permissions to different resources.

BaseUser

Bases: Protocol

The base ASGI user protocol

is_authenticated: bool property

Whether the user is authenticated.

display_name: str property

The display name of the user.

identity: str property

The unique identifier for the user.

permissions: Sequence[str] property

The permissions associated with the user.

StudioUser

A user object that's populated from authenticated requests from the LangGraph studio.

Note: Studio auth can be disabled in your langgraph.json config.

{
  "auth": {
    "disable_studio_auth": true
  }
}

You can use isinstance checks in your authorization handlers (@auth.on) to control access specifically for developers accessing the instance from the LangGraph Studio UI.

Examples

@auth.on
async def allow_developers(ctx: Auth.types.AuthContext, value: Any) -> None:
    if isinstance(ctx.user, Auth.types.StudioUser):
        return None
    ...
    return False

BaseAuthContext

Base class for authentication context.

Provides the fundamental authentication information needed for authorization decisions.

permissions: Sequence[str] instance-attribute

The permissions granted to the authenticated user.

user: BaseUser instance-attribute

The authenticated user.

AuthContext

Bases: BaseAuthContext

Complete authentication context with resource and action information.

Extends BaseAuthContext with specific resource and action being accessed, allowing for fine-grained access control decisions.

permissions: Sequence[str] instance-attribute

The permissions granted to the authenticated user.

user: BaseUser instance-attribute

The authenticated user.

resource: typing.Literal['runs', 'threads', 'crons', 'assistants'] instance-attribute

The resource being accessed.

action: typing.Literal['create', 'read', 'update', 'delete', 'search', 'create_run'] instance-attribute

The action being performed on the resource.

ThreadsCreate

Bases: TypedDict

Parameters for creating a new thread.

Examples

create_params = {
    "thread_id": UUID("123e4567-e89b-12d3-a456-426614174000"),
    "metadata": {"owner": "user123"},
    "if_exists": "do_nothing"
}

thread_id: UUID instance-attribute

Unique identifier for the thread.

metadata: MetadataInput instance-attribute

typing.Optional metadata to attach to the thread.

if_exists: OnConflictBehavior instance-attribute

Behavior when a thread with the same ID already exists.

ThreadsRead

Bases: TypedDict

Parameters for reading thread state or run information.

This type is used in three contexts: 1. Reading thread, thread version, or thread state information: Only thread_id is provided 2. Reading run information: Both thread_id and run_id are provided

thread_id: UUID instance-attribute

Unique identifier for the thread.

run_id: typing.Optional[UUID] instance-attribute

Run ID to filter by. Only used when reading run information within a thread.

ThreadsUpdate

Bases: TypedDict

Parameters for updating a thread or run.

Called for updates to a thread, thread version, or run cancellation.

thread_id: UUID instance-attribute

Unique identifier for the thread.

metadata: MetadataInput instance-attribute

typing.Optional metadata to update.

action: typing.Optional[typing.Literal['interrupt', 'rollback']] instance-attribute

typing.Optional action to perform on the thread.

ThreadsDelete

Bases: TypedDict

Parameters for deleting a thread.

Called for deletes to a thread, thread version, or run

thread_id: UUID instance-attribute

Unique identifier for the thread.

run_id: typing.Optional[UUID] instance-attribute

typing.Optional run ID to filter by.

ThreadsSearch

Bases: TypedDict

Parameters for searching threads.

Called for searches to threads or runs.

metadata: MetadataInput instance-attribute

typing.Optional metadata to filter by.

values: MetadataInput instance-attribute

typing.Optional values to filter by.

status: typing.Optional[ThreadStatus] instance-attribute

typing.Optional status to filter by.

limit: int instance-attribute

Maximum number of results to return.

offset: int instance-attribute

Offset for pagination.

thread_id: typing.Optional[UUID] instance-attribute

typing.Optional thread ID to filter by.

RunsCreate

Bases: TypedDict

Payload for creating a run.

Examples

create_params = {
    "assistant_id": UUID("123e4567-e89b-12d3-a456-426614174000"),
    "thread_id": UUID("123e4567-e89b-12d3-a456-426614174001"),
    "run_id": UUID("123e4567-e89b-12d3-a456-426614174002"),
    "status": "pending",
    "metadata": {"owner": "user123"},
    "prevent_insert_if_inflight": True,
    "multitask_strategy": "reject",
    "if_not_exists": "create",
    "after_seconds": 10,
    "kwargs": {"key": "value"},
    "action": "interrupt"
}

assistant_id: typing.Optional[UUID] instance-attribute

typing.Optional assistant ID to use for this run.

thread_id: typing.Optional[UUID] instance-attribute

typing.Optional thread ID to use for this run.

run_id: typing.Optional[UUID] instance-attribute

typing.Optional run ID to use for this run.

status: typing.Optional[RunStatus] instance-attribute

typing.Optional status for this run.

metadata: MetadataInput instance-attribute

typing.Optional metadata for the run.

prevent_insert_if_inflight: bool instance-attribute

Prevent inserting a new run if one is already in flight.

multitask_strategy: MultitaskStrategy instance-attribute

Multitask strategy for this run.

if_not_exists: IfNotExists instance-attribute

IfNotExists for this run.

after_seconds: int instance-attribute

Number of seconds to wait before creating the run.

kwargs: typing.Dict[str, typing.Any] instance-attribute

Keyword arguments to pass to the run.

action: typing.Optional[typing.Literal['interrupt', 'rollback']] instance-attribute

Action to take if updating an existing run.

AssistantsCreate

Bases: TypedDict

Payload for creating an assistant.

Examples

create_params = {
    "assistant_id": UUID("123e4567-e89b-12d3-a456-426614174000"),
    "graph_id": "graph123",
    "config": {"key": "value"},
    "metadata": {"owner": "user123"},
    "if_exists": "do_nothing",
    "name": "Assistant 1"
}

assistant_id: UUID instance-attribute

Unique identifier for the assistant.

graph_id: str instance-attribute

Graph ID to use for this assistant.

config: typing.Optional[typing.Union[typing.Dict[str, typing.Any], typing.Any]] instance-attribute

typing.Optional configuration for the assistant.

metadata: MetadataInput instance-attribute

typing.Optional metadata to attach to the assistant.

if_exists: OnConflictBehavior instance-attribute

Behavior when an assistant with the same ID already exists.

name: str instance-attribute

Name of the assistant.

AssistantsRead

Bases: TypedDict

Payload for reading an assistant.

Examples

read_params = {
    "assistant_id": UUID("123e4567-e89b-12d3-a456-426614174000"),
    "metadata": {"owner": "user123"}
}

assistant_id: UUID instance-attribute

Unique identifier for the assistant.

metadata: MetadataInput instance-attribute

typing.Optional metadata to filter by.

AssistantsUpdate

Bases: TypedDict

Payload for updating an assistant.

Examples

update_params = {
    "assistant_id": UUID("123e4567-e89b-12d3-a456-426614174000"),
    "graph_id": "graph123",
    "config": {"key": "value"},
    "metadata": {"owner": "user123"},
    "name": "Assistant 1",
    "version": 1
}

assistant_id: UUID instance-attribute

Unique identifier for the assistant.

graph_id: typing.Optional[str] instance-attribute

typing.Optional graph ID to update.

config: typing.Optional[typing.Union[typing.Dict[str, typing.Any], typing.Any]] instance-attribute

typing.Optional configuration to update.

metadata: MetadataInput instance-attribute

typing.Optional metadata to update.

name: typing.Optional[str] instance-attribute

typing.Optional name to update.

version: typing.Optional[int] instance-attribute

typing.Optional version to update.

AssistantsDelete

Bases: TypedDict

Payload for deleting an assistant.

Examples

delete_params = {
    "assistant_id": UUID("123e4567-e89b-12d3-a456-426614174000")
}

assistant_id: UUID instance-attribute

Unique identifier for the assistant.

AssistantsSearch

Bases: TypedDict

Payload for searching assistants.

Examples

search_params = {
    "graph_id": "graph123",
    "metadata": {"owner": "user123"},
    "limit": 10,
    "offset": 0
}

graph_id: typing.Optional[str] instance-attribute

typing.Optional graph ID to filter by.

metadata: MetadataInput instance-attribute

typing.Optional metadata to filter by.

limit: int instance-attribute

Maximum number of results to return.

offset: int instance-attribute

Offset for pagination.

CronsCreate

Bases: TypedDict

Payload for creating a cron job.

Examples

create_params = {
    "payload": {"key": "value"},
    "schedule": "0 0 * * *",
    "cron_id": UUID("123e4567-e89b-12d3-a456-426614174000"),
    "thread_id": UUID("123e4567-e89b-12d3-a456-426614174001"),
    "user_id": "user123",
    "end_time": datetime(2024, 3, 16, 10, 0, 0)
}

payload: typing.Dict[str, typing.Any] instance-attribute

Payload for the cron job.

schedule: str instance-attribute

Schedule for the cron job.

cron_id: typing.Optional[UUID] instance-attribute

typing.Optional unique identifier for the cron job.

thread_id: typing.Optional[UUID] instance-attribute

typing.Optional thread ID to use for this cron job.

user_id: typing.Optional[str] instance-attribute

typing.Optional user ID to use for this cron job.

end_time: typing.Optional[datetime] instance-attribute

typing.Optional end time for the cron job.

CronsDelete

Bases: TypedDict

Payload for deleting a cron job.

Examples

delete_params = {
    "cron_id": UUID("123e4567-e89b-12d3-a456-426614174000")
}

cron_id: UUID instance-attribute

Unique identifier for the cron job.

CronsRead

Bases: TypedDict

Payload for reading a cron job.

Examples

read_params = {
    "cron_id": UUID("123e4567-e89b-12d3-a456-426614174000")
}

cron_id: UUID instance-attribute

Unique identifier for the cron job.

CronsUpdate

Bases: TypedDict

Payload for updating a cron job.

Examples

update_params = {
    "cron_id": UUID("123e4567-e89b-12d3-a456-426614174000"),
    "payload": {"key": "value"},
    "schedule": "0 0 * * *"
}

cron_id: UUID instance-attribute

Unique identifier for the cron job.

payload: typing.Optional[typing.Dict[str, typing.Any]] instance-attribute

typing.Optional payload to update.

schedule: typing.Optional[str] instance-attribute

typing.Optional schedule to update.

CronsSearch

Bases: TypedDict

Payload for searching cron jobs.

Examples

search_params = {
    "assistant_id": UUID("123e4567-e89b-12d3-a456-426614174000"),
    "thread_id": UUID("123e4567-e89b-12d3-a456-426614174001"),
    "limit": 10,
    "offset": 0
}

assistant_id: typing.Optional[UUID] instance-attribute

typing.Optional assistant ID to filter by.

thread_id: typing.Optional[UUID] instance-attribute

typing.Optional thread ID to filter by.

limit: int instance-attribute

Maximum number of results to return.

offset: int instance-attribute

Offset for pagination.

on

Namespace for type definitions of different API operations.

This class organizes type definitions for create, read, update, delete, and search operations across different resources (threads, assistants, crons).

Usage

from langgraph_sdk import Auth

auth = Auth()

@auth.on
def handle_all(params: Auth.on.value):
    raise Exception("Not authorized")

@auth.on.threads.create
def handle_thread_create(params: Auth.on.threads.create.value):
    # Handle thread creation
    pass

@auth.on.assistants.search
def handle_assistant_search(params: Auth.on.assistants.search.value):
    # Handle assistant search
    pass

threads

Types for thread-related operations.

create

Type for thread creation parameters.

create_run

Type for creating or streaming a run.

read

Type for thread read parameters.

update

Type for thread update parameters.

delete

Type for thread deletion parameters.

search

Type for thread search parameters.

assistants

Types for assistant-related operations.

create

Type for assistant creation parameters.

read

Type for assistant read parameters.

update

Type for assistant update parameters.

delete

Type for assistant deletion parameters.

search

Type for assistant search parameters.

crons

Types for cron-related operations.

create

Type for cron creation parameters.

read

Type for cron read parameters.

update

Type for cron update parameters.

delete

Type for cron deletion parameters.

search

Type for cron search parameters.

Exceptions used in the auth system.

HTTPException

Bases: Exception

HTTP exception that you can raise to return a specific HTTP error response.

Since this is defined in the auth module, we default to a 401 status code.

Parameters:

• status_code (int, default: 401 ) –

  HTTP status code for the error. Defaults to 401 "Unauthorized".

• detail (str | None, default: None ) –

  Detailed error message. If None, uses a default message based on the status code.

• headers (Mapping[str, str] | None, default: None ) –

  Additional HTTP headers to include in the error response.

Example

Default:

raise HTTPException()
# HTTPException(status_code=401, detail='Unauthorized')

Add headers:

raise HTTPException(headers={"X-Custom-Header": "Custom Value"})
# HTTPException(status_code=401, detail='Unauthorized', headers={"WWW-Authenticate": "Bearer"})

Custom error:

raise HTTPException(status_code=404, detail="Not found")

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