AzureAIAgentsService#

class langchain_azure_ai.azure_ai_agents.agent_service.AzureAIAgentsService[source]#

Bases: BaseLLM

Azure AI Agents service integration for LangChain.

This class provides a LangChain-compatible interface to Azure AI Agents, enabling seamless integration of Azure AI Agents with LangChain workflows. It uses the Azure AI Projects SDK and Azure AI Agents SDK with a direct endpoint.

The service automatically manages agent lifecycle, thread creation/cleanup, and message handling while providing the standard LangChain LLM interface for use in chains, prompt templates, and other LangChain components.

Supported Configuration Parameters: - temperature: Controls randomness in responses (0.0 = deterministic,

1.0 = very random)

top_p: Controls diversity via nucleus sampling (0.0 to 1.0)
response_format: Specifies output format (e.g., JSON schema)
tools: List of tools the agent can use (code_interpreter, file_search, functions)
tool_resources: Resources for tools (file IDs, vector stores, etc.)
metadata: Custom key-value pairs for tracking and organization

Authentication Options: 1. Default Credential or other Token Credential

Examples

Using with direct endpoint:

Basic usage with endpoint:

Integration with LangChain chains:

Using with tools (code interpreter example):

Note

AzureAIAgentsService implements the standard Runnable Interface. 🏃

The Runnable Interface has additional methods that are available on runnables, such as with_config, with_types, with_retry, assign, bind, get_graph, and more.

param agent_description: str | None = None#

Optional human-readable description of the agent’s purpose.

This description is used for documentation and management purposes. It doesn’t affect the agent’s behavior but helps with organization.

param agent_name: str = 'langchain-agent'#

A descriptive name for the agent instance.

This name helps identify the agent in logs, Azure portal, and when managing multiple agents. Choose a name that describes the agent’s purpose or role.

param cache: BaseCache | bool | None = None#

Whether to cache the response.

If true, will use the global cache.
If false, will not use a cache
If None, will use the global cache if it’s set, otherwise no cache.
If instance of BaseCache, will use the provided cache.

Caching is not currently supported for streaming methods of models.

param callback_manager: BaseCallbackManager | None = None#: [DEPRECATED]

param callbacks: Callbacks = None#: Callbacks to add to the run trace.

param client_kwargs: Dict[str, Any] [Optional]#

Additional arguments passed to the Azure AI Projects client constructor.

Use this for advanced client configuration like custom timeouts, retry policies, or proxy settings. Most users won’t need to modify this.

param credential: TokenCredential | None = None#

Authentication credential for the Azure AI service.

Supported types: - TokenCredential: Azure AD credential (like DefaultAzureCredential)

If None, DefaultAzureCredential() is used for automatic Azure AD authentication.

param custom_get_token_ids: Callable[[str], list[int]] | None = None#: Optional encoder to use for counting tokens.

param endpoint: str [Required]#

The direct endpoint URI for Azure AI Agents service or from an Azure AI Foundry model.

Use this for direct access to Azure AI Agents.

Format: https://your-resource.inference.ai.azure.com

This parameter is required.

param instructions: str = 'You are a helpful AI assistant.'#

System instructions that define the agent’s behavior and personality.

This is the most important configuration for controlling how your agent behaves. Be specific about the agent’s role, capabilities, tone, and any constraints.

Example: ‘You are a financial analyst. Provide clear, data-driven insights and always cite your sources. Be conservative in your recommendations.’

param metadata: Dict[str, Any] | None = None#

Custom key-value pairs for tracking and organizing agents.

Use metadata to store information about the agent that helps with: - Project organization and categorization - Usage tracking and analytics - Integration with your own systems

Example: {‘project’: ‘customer-support’, ‘version’: ‘1.2’, ‘team’: ‘ai-ops’}

param model: str | None = None#

The name/ID of the model deployment to use for the agent.

This should match a model deployment in your Azure AI project or resource. Common examples: ‘gpt-4.1’, ‘gpt-4o-mini’, ‘deepseek-r1’.

The model determines the agent’s capabilities, cost, and performance characteristics.

param response_format: Dict[str, Any] | None = None#

Specifies the format that the model must output.

Use this to enforce structured outputs like JSON. The agent will format its responses according to this specification.

Example: {‘type’: ‘json_object’} for JSON responses Example: {‘type’: ‘json_schema’, ‘json_schema’: {…}} for specific schemas

param tags: list[str] | None = None#: Tags to add to the run trace.

param temperature: float | None = None#

Controls randomness in the agent’s responses (0.0 to 1.0).

0.0: Deterministic, consistent responses
0.3: Focused and coherent (good for factual tasks)
0.7: Balanced creativity and coherence (default-like)
1.0: Maximum creativity and randomness

Lower values for factual tasks, higher values for creative tasks.

param tool_resources: Any | None = None#

Resources required by the agent’s tools (file IDs, vector stores, etc.).

This contains the actual resources that tools need to operate: - File IDs for code_interpreter and file_search tools - Vector store configurations - Function schemas and implementations

Must correspond to the tools defined in the ‘tools’ parameter.

param tools: List[Dict[str, Any]] | None = None#

List of tool definitions that the agent can use during conversations.

Tools extend the agent’s capabilities beyond text generation. Common tools: - code_interpreter: Execute Python code and analyze data - file_search: Search through uploaded files - function: Call custom functions you define

Use the Azure AI Agents SDK to create proper tool definitions.

param top_p: float | None = None#

Controls diversity via nucleus sampling (0.0 to 1.0).

Alternative to temperature for controlling randomness. Only consider tokens in the top p probability mass. Lower values make responses more focused.

0.1: Very focused, only most likely tokens
0.9: More diverse token selection

Don’t use both temperature and top_p simultaneously.

param verbose: bool [Optional]#: Whether to print out response text.

class Config[source]#

Bases: object

Configuration for this pydantic object.

extra = 'forbid'#

Deprecated since version 0.1.7: Use invoke() instead. It will not be removed until langchain-core==1.0.

Check Cache and run the LLM on the given prompt and input.

Parameters:

prompt (str) – The prompt to generate from.
stop (list[str] | None) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
tags (list[str] | None) – List of tags to associate with the prompt.
metadata (dict[str, Any] | None) – Metadata to associate with the prompt.
**kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns:

The generated text.

Raises:

ValueError – If the prompt is not a string.

Return type:

str

Default implementation runs ainvoke in parallel using asyncio.gather.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

Parameters:

inputs (list[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]]]) – A list of inputs to the Runnable.
config (RunnableConfig | list[RunnableConfig] | None) – A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.
return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.

Returns:

A list of outputs from the Runnable.

Return type:

list[str]

async abatch_as_completed(

inputs: Sequence[Input],

config: RunnableConfig | Sequence[RunnableConfig] | None = None,

*,

return_exceptions: bool = False,

**kwargs: Any | None,

) → AsyncIterator[tuple[int, Output | Exception]]#

Run ainvoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

inputs (Sequence[Input]) – A list of inputs to the Runnable.
config (RunnableConfig | Sequence[RunnableConfig] | None) – A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.
return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.

Yields:

A tuple of the index of the input and the output from the Runnable.

Return type:

AsyncIterator[tuple[int, Output | Exception]]

async aclose() → None[source]#

Asynchronously close all underlying client connections and free resources.

This is the async version of close() that uses thread pool execution to avoid blocking the event loop during cleanup operations.

Example

try:: # Use the service response = await service.ainvoke(“Hello!”)
finally:: # Always clean up await service.aclose()

Return type:: None

async acreate_agent(

**kwargs: Any,

) → Agent[source]#

Asynchronously create a new agent with custom parameters.

This is the async version of create_agent() that uses thread pool execution to avoid blocking the event loop during agent creation.

Parameters:: **kwargs (Any) – Parameters to override or add to the agent creation.
Returns:: The newly created agent instance.
Return type:: Agent

async adelete_agent( agent_id: str | None = None, ) → None[source]#

Asynchronously delete an agent from the Azure AI service.

This is the async version of delete_agent() that uses thread pool execution to avoid blocking the event loop.

Parameters:: agent_id (str | None) – The ID of the agent to delete. If None, deletes the current cached agent.
Raises:: ValueError – If no agent_id is provided and no cached agent exists.
Return type:: None

Default implementation of ainvoke, calls invoke from a thread.

The default implementation allows usage of async code even if the Runnable did not implement a native async version of invoke.

Subclasses should override this method if they can run asynchronously.

Parameters:

input (PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]])
config (RunnableConfig | None)
stop (list[str] | None)
kwargs (Any)

Return type:

str

Default implementation of astream, which calls ainvoke.

Subclasses should override this method if they support streaming output.

Parameters:

input (PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]]) – The input to the Runnable.
config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.
stop (list[str] | None)

Yields:

The output of the Runnable.

Return type:

AsyncIterator[str]

async astream_events(

input: Any,

config: RunnableConfig | None = None,

*,

version: Literal['v1', 'v2'] = 'v2',

include_names: Sequence[str] | None = None,

include_types: Sequence[str] | None = None,

include_tags: Sequence[str] | None = None,

exclude_names: Sequence[str] | None = None,

exclude_types: Sequence[str] | None = None,

exclude_tags: Sequence[str] | None = None,

**kwargs: Any,

) → AsyncIterator[StreamEvent]#

Generate a stream of events.

Use to create an iterator over StreamEvents that provide real-time information about the progress of the Runnable, including StreamEvents from intermediate results.

A StreamEvent is a dictionary with the following schema:

event: str - Event names are of the format: on_[runnable_type]_(start|stream|end).
name: str - The name of the Runnable that generated the event.
run_id: str - randomly generated ID associated with the given execution of the Runnable that emitted the event. A child Runnable that gets invoked as part of the execution of a parent Runnable is assigned its own unique ID.
parent_ids: list[str] - The IDs of the parent runnables that generated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.
tags: Optional[list[str]] - The tags of the Runnable that generated the event.
metadata: Optional[dict[str, Any]] - The metadata of the Runnable that generated the event.
data: dict[str, Any]

Below is a table that illustrates some events that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.

Note

This reference table is for the v2 version of the schema.

event	name	chunk	input	output
`on_chat_model_start`	[model name]		`{"messages": [[SystemMessage, HumanMessage]]}`
`on_chat_model_stream`	[model name]	`AIMessageChunk(content="hello")`
`on_chat_model_end`	[model name]		`{"messages": [[SystemMessage, HumanMessage]]}`	`AIMessageChunk(content="hello world")`
`on_llm_start`	[model name]		`{'input': 'hello'}`
`on_llm_stream`	[model name]	``’Hello’ ``
`on_llm_end`	[model name]		`'Hello human!'`
`on_chain_start`	format_docs
`on_chain_stream`	format_docs	`'hello world!, goodbye world!'`
`on_chain_end`	format_docs		`[Document(...)]`	`'hello world!, goodbye world!'`
`on_tool_start`	some_tool		`{"x": 1, "y": "2"}`
`on_tool_end`	some_tool			`{"x": 1, "y": "2"}`
`on_retriever_start`	[retriever name]		`{"query": "hello"}`
`on_retriever_end`	[retriever name]		`{"query": "hello"}`	`[Document(...), ..]`
`on_prompt_start`	[template_name]		`{"question": "hello"}`
`on_prompt_end`	[template_name]		`{"question": "hello"}`	`ChatPromptValue(messages: [SystemMessage, ...])`

In addition to the standard events, users can also dispatch custom events (see example below).

Custom events will be only be surfaced with in the v2 version of the API!

A custom event has following format:

Attribute	Type	Description
name	str	A user defined name for the event.
data	Any	The data associated with the event. This can be anything, though we suggest making it JSON serializable.

Here are declarations associated with the standard events shown above:

format_docs:

def format_docs(docs: list[Document]) -> str:
    '''Format the docs.'''
    return ", ".join([doc.page_content for doc in docs])

format_docs = RunnableLambda(format_docs)

some_tool:

@tool
def some_tool(x: int, y: str) -> dict:
    '''Some_tool.'''
    return {"x": x, "y": y}

prompt:

template = ChatPromptTemplate.from_messages(
    [("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})

Example:

from langchain_core.runnables import RunnableLambda

async def reverse(s: str) -> str:
    return s[::-1]

chain = RunnableLambda(func=reverse)

events = [
    event async for event in chain.astream_events("hello", version="v2")
]

# will produce the following events (run_id, and parent_ids
# has been omitted for brevity):
[
    {
        "data": {"input": "hello"},
        "event": "on_chain_start",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"chunk": "olleh"},
        "event": "on_chain_stream",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"output": "olleh"},
        "event": "on_chain_end",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
]

Example: Dispatch Custom Event

from langchain_core.callbacks.manager import (
    adispatch_custom_event,
)
from langchain_core.runnables import RunnableLambda, RunnableConfig
import asyncio


async def slow_thing(some_input: str, config: RunnableConfig) -> str:
    """Do something that takes a long time."""
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 1 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 2 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    return "Done"

slow_thing = RunnableLambda(slow_thing)

async for event in slow_thing.astream_events("some_input", version="v2"):
    print(event)

Parameters:

input (Any) – The input to the Runnable.
config (Optional[RunnableConfig]) – The config to use for the Runnable.
version (Literal['v1', 'v2']) – The version of the schema to use either 'v2' or 'v1'. Users should use 'v2'. 'v1' is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in 'v2'.
include_names (Optional[Sequence[str]]) – Only include events from Runnables with matching names.
include_types (Optional[Sequence[str]]) – Only include events from Runnables with matching types.
include_tags (Optional[Sequence[str]]) – Only include events from Runnables with matching tags.
exclude_names (Optional[Sequence[str]]) – Exclude events from Runnables with matching names.
exclude_types (Optional[Sequence[str]]) – Exclude events from Runnables with matching types.
exclude_tags (Optional[Sequence[str]]) – Exclude events from Runnables with matching tags.
kwargs (Any) – Additional keyword arguments to pass to the Runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.

Yields:

An async stream of StreamEvents.

Raises:

NotImplementedError – If the version is not 'v1' or 'v2'.

Return type:

AsyncIterator[StreamEvent]

Default implementation runs invoke in parallel using a thread pool executor.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

Parameters:

inputs (list[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]]])
config (RunnableConfig | list[RunnableConfig] | None)
return_exceptions (bool)
kwargs (Any)

Return type:

list[str]

batch_as_completed(

inputs: Sequence[Input],

config: RunnableConfig | Sequence[RunnableConfig] | None = None,

*,

return_exceptions: bool = False,

**kwargs: Any | None,

) → Iterator[tuple[int, Output | Exception]]#

Run invoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

inputs (Sequence[Input])
config (RunnableConfig | Sequence[RunnableConfig] | None)
return_exceptions (bool)
kwargs (Any | None)

Return type:

Iterator[tuple[int, Output | Exception]]

bind(

**kwargs: Any,

) → Runnable[Input, Output]#

Bind arguments to a Runnable, returning a new Runnable.

Useful when a Runnable in a chain requires an argument that is not in the output of the previous Runnable or included in the user input.

Parameters:: kwargs (Any) – The arguments to bind to the Runnable.
Returns:: A new Runnable with the arguments bound.
Return type:: Runnable[Input, Output]

Example:

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

llm = ChatOllama(model='llama2')

# Without bind.
chain = (
    llm
    | StrOutputParser()
)

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'

# With bind.
chain = (
    llm.bind(stop=["three"])
    | StrOutputParser()
)

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'

close() → None[source]#

Close all underlying client connections and free resources.

This method properly closes the HTTP connections used by the Azure AI Projects client and any associated credentials. Call this method when you’re done using the service to ensure proper resource cleanup.

The method handles: - Closing the Azure AI Projects client’s HTTP session - Closing credential providers that support it (like DefaultAzureCredential) - Graceful handling if resources are already closed

Example

try:: # Use the service response = service.invoke(“Hello!”)
finally:: # Always clean up service.close()

Note

After calling close(), the service should not be used for new operations. Create a new instance if you need to continue working.

Return type:: None

configurable_alternatives(

which: ConfigurableField,

*,

default_key: str = 'default',

prefix_keys: bool = False,

**kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]],

) → RunnableSerializable#

Configure alternatives for Runnables that can be set at runtime.

Parameters:

which (ConfigurableField) – The ConfigurableField instance that will be used to select the alternative.
default_key (str) – The default key to use if no alternative is selected. Defaults to 'default'.
prefix_keys (bool) – Whether to prefix the keys with the ConfigurableField id. Defaults to False.
**kwargs (Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) – A dictionary of keys to Runnable instances or callables that return Runnable instances.

Returns:

A new Runnable with the alternatives configured.

Return type:

RunnableSerializable

from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatAnthropic(
    model_name="claude-3-7-sonnet-20250219"
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="anthropic",
    openai=ChatOpenAI()
)

# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)

# uses ChatOpenAI
print(
    model.with_config(
        configurable={"llm": "openai"}
    ).invoke("which organization created you?").content
)

configurable_fields(

**kwargs: ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption,

) → RunnableSerializable#

Configure particular Runnable fields at runtime.

Parameters:: **kwargs (ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) – A dictionary of ConfigurableField instances to configure.
Returns:: A new Runnable with the fields configured.
Return type:: RunnableSerializable

from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatOpenAI(max_tokens=20).configurable_fields(
    max_tokens=ConfigurableField(
        id="output_token_number",
        name="Max tokens in the output",
        description="The maximum number of tokens in the output",
    )
)

# max_tokens = 20
print(
    "max_tokens_20: ",
    model.invoke("tell me something about chess").content
)

# max_tokens = 200
print("max_tokens_200: ", model.with_config(
    configurable={"output_token_number": 200}
    ).invoke("tell me something about chess").content
)

create_agent(

**kwargs: Any,

) → Agent[source]#

Create a new agent with custom parameters, replacing cached agent.

This method allows you to create additional agents or modify the current agent’s configuration beyond what was set during service initialization. Unlike _get_or_create_agent(), this method always creates a new agent and replaces the cached instance.

The method starts with the base configuration from the service instance and then applies any overrides provided through kwargs. This is useful for scenarios where you need different agent configurations during runtime.

Parameters:: **kwargs (Any) – Parameters to override or add to the agent creation. Common parameters include: - model: Change the model name - name: Give the agent a different name - instructions: Modify the agent’s behavior instructions - temperature: Adjust response randomness - tools: Add or change available tools - tool_resources: Modify tool resources
Returns:: The newly created agent instance (also cached internally).
Return type:: Agent

Example

# Create an agent with different instructions agent = service.create_agent(

name=”data-analyst-v2”, instructions=”You are a specialized data analyst focusing on ” “financial data.”, temperature=0.3

)

# Create an agent with tools agent = service.create_agent(

tools=[{“type”: “code_interpreter”}], instructions=”You can analyze data and create visualizations.”

)

delete_agent( agent_id: str | None = None, ) → None[source]#

Delete an agent from the Azure AI service.

This method permanently removes an agent from your Azure AI project. By default, it deletes the current cached agent, but you can specify a different agent ID to delete any agent you have access to.

Parameters:: agent_id (str | None) – The ID of the agent to delete. If None, deletes the current cached agent. If no cached agent exists and no ID is provided, raises ValueError.
Raises:: ValueError – If no agent_id is provided and no cached agent exists.
Return type:: None

Example

# Delete the current agent service.delete_agent()

# Delete a specific agent by ID service.delete_agent(“agent_abc123”)

Warning

Agent deletion is permanent and cannot be undone. Any conversation threads using this agent will no longer work. Make sure you don’t need the agent before deleting it.

get_agent() → Agent | None[source]#

Get the current cached agent instance without creating a new one.

This method returns the agent that was created during the first generation call or through create_agent(). It does not trigger agent creation if none exists yet.

Returns:: The cached agent instance if it exists, None otherwise.
Return type:: Optional[Agent]

Example

# Check if an agent has been created agent = service.get_agent() if agent:

print(f”Agent ID: {agent.id}, Name: {agent.name}”)

else:: print(“No agent created yet”)

get_async_client() → AIProjectClient[source]#

Get the underlying Azure AI Projects client (same instance as sync version).

Note: The Azure AI Projects SDK doesn’t have separate async clients, so this returns the same client as get_client(). For async operations, use the async methods of this service which handle thread pool execution.

Returns:: The configured client instance.
Return type:: AIProjectClient

get_client() → AIProjectClient[source]#

Get the underlying Azure AI Projects client for advanced operations.

This method provides direct access to the Azure AI Projects client, allowing you to perform operations that aren’t exposed through the LangChain interface. This is useful for advanced scenarios like:

Managing files and file uploads
Creating and managing conversation threads manually
Accessing agent run details and metadata
Performing bulk operations
Using features not yet wrapped by this service

Returns:: The configured client instance.
Return type:: AIProjectClient

Example

# Upload a file for use with tools client = agent_service.get_client() uploaded_file = client.files.upload_and_poll(

file_path=”data.csv”, purpose=FilePurpose.AGENTS

)

# Create a thread manually for multi-turn conversations thread = client.threads.create()

Warning

When using the client directly, you’re responsible for proper resource management (e.g., cleaning up threads, deleting files).

get_num_tokens(text: str) → int#

Get the number of tokens present in the text.

Useful for checking if an input fits in a model’s context window.

Parameters:: text (str) – The string input to tokenize.
Returns:: The integer number of tokens in the text.
Return type:: int

get_num_tokens_from_messages( messages: list[BaseMessage], tools: Sequence | None = None, ) → int#

Get the number of tokens in the messages.

Useful for checking if an input fits in a model’s context window.

Note

The base implementation of get_num_tokens_from_messages ignores tool schemas.

Parameters:

messages (list[BaseMessage]) – The message inputs to tokenize.
tools (Sequence | None) – If provided, sequence of dict, BaseModel, function, or BaseTools to be converted to tool schemas.

Returns:

The sum of the number of tokens across the messages.

Return type:

int

get_token_ids(text: str) → list[int]#

Return the ordered ids of the tokens in a text.

Parameters:: text (str) – The string input to tokenize.
Returns:: A list of ids corresponding to the tokens in the text, in order they occur in the text.
Return type:: list[int]

Transform a single input into an output.

Parameters:

input (PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]]) – The input to the Runnable.
config (RunnableConfig | None) – A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.
stop (list[str] | None)
kwargs (Any)

Returns:

The output of the Runnable.

Return type:

str

save( file_path: Path | str, ) → None#

Save the LLM.

Parameters:: file_path (Path | str) – Path to file to save the LLM to.
Raises:: ValueError – If the file path is not a string or Path object.
Return type:: None

Example

llm.save(file_path="path/llm.yaml")

Default implementation of stream, which calls invoke.

Subclasses should override this method if they support streaming output.

Parameters:

input (PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]]) – The input to the Runnable.
config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.
stop (list[str] | None)

Yields:

The output of the Runnable.

Return type:

Iterator[str]

with_alisteners( *, on_start: AsyncListener | None = None, on_end: AsyncListener | None = None, on_error: AsyncListener | None = None, ) → Runnable[Input, Output]#

Bind async lifecycle listeners to a Runnable, returning a new Runnable.

The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.

Parameters:

on_start (Optional[AsyncListener]) – Called asynchronously before the Runnable starts running, with the Run object. Defaults to None.
on_end (Optional[AsyncListener]) – Called asynchronously after the Runnable finishes running, with the Run object. Defaults to None.
on_error (Optional[AsyncListener]) – Called asynchronously if the Runnable throws an error, with the Run object. Defaults to None.

Returns:

A new Runnable with the listeners bound.

Return type:

Runnable[Input, Output]

Example:

from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio

def format_t(timestamp: float) -> str:
    return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()

async def test_runnable(time_to_sleep : int):
    print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
    await asyncio.sleep(time_to_sleep)
    print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")

async def fn_start(run_obj : Runnable):
    print(f"on start callback starts at {format_t(time.time())}")
    await asyncio.sleep(3)
    print(f"on start callback ends at {format_t(time.time())}")

async def fn_end(run_obj : Runnable):
    print(f"on end callback starts at {format_t(time.time())}")
    await asyncio.sleep(2)
    print(f"on end callback ends at {format_t(time.time())}")

runnable = RunnableLambda(test_runnable).with_alisteners(
    on_start=fn_start,
    on_end=fn_end
)
async def concurrent_runs():
    await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))

asyncio.run(concurrent_runs())
Result:
on start callback starts at 2025-03-01T07:05:22.875378+00:00
on start callback starts at 2025-03-01T07:05:22.875495+00:00
on start callback ends at 2025-03-01T07:05:25.878862+00:00
on start callback ends at 2025-03-01T07:05:25.878947+00:00
Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
on end callback starts at 2025-03-01T07:05:27.882360+00:00
Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
on end callback starts at 2025-03-01T07:05:28.882428+00:00
on end callback ends at 2025-03-01T07:05:29.883893+00:00
on end callback ends at 2025-03-01T07:05:30.884831+00:00

with_config(

config: RunnableConfig | None = None,

**kwargs: Any,

) → Runnable[Input, Output]#

Bind config to a Runnable, returning a new Runnable.

Parameters:

config (RunnableConfig | None) – The config to bind to the Runnable.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.

Returns:

A new Runnable with the config bound.

Return type:

Runnable[Input, Output]

with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: tuple[type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) → RunnableWithFallbacksT[Input, Output]#

Add fallbacks to a Runnable, returning a new Runnable.

The new Runnable will try the original Runnable, and then each fallback in order, upon failures.

Parameters:

fallbacks (Sequence[Runnable[Input, Output]]) – A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle (tuple[type[BaseException], ...]) – A tuple of exception types to handle. Defaults to (Exception,).
exception_key (Optional[str]) – If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input. Defaults to None.

Returns:

A new Runnable that will try the original Runnable, and then each fallback in order, upon failures.

Return type:

RunnableWithFallbacksT[Input, Output]

Example

from typing import Iterator

from langchain_core.runnables import RunnableGenerator


def _generate_immediate_error(input: Iterator) -> Iterator[str]:
    raise ValueError()
    yield ""


def _generate(input: Iterator) -> Iterator[str]:
    yield from "foo bar"


runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
    [RunnableGenerator(_generate)]
    )
print(''.join(runnable.stream({}))) #foo bar

Parameters:

fallbacks (Sequence[Runnable[Input, Output]]) – A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle (tuple[type[BaseException], ...]) – A tuple of exception types to handle.
exception_key (Optional[str]) – If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input.

Returns:

A new Runnable that will try the original Runnable, and then each fallback in order, upon failures.

Return type:

RunnableWithFallbacksT[Input, Output]

with_listeners( *, on_start: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None, on_end: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None, on_error: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None, ) → Runnable[Input, Output]#

Bind lifecycle listeners to a Runnable, returning a new Runnable.

The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.

Parameters:

on_start (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – Called before the Runnable starts running, with the Run object. Defaults to None.
on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – Called after the Runnable finishes running, with the Run object. Defaults to None.
on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – Called if the Runnable throws an error, with the Run object. Defaults to None.

Returns:

A new Runnable with the listeners bound.

Return type:

Runnable[Input, Output]

Example:

from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run

import time

def test_runnable(time_to_sleep : int):
    time.sleep(time_to_sleep)

def fn_start(run_obj: Run):
    print("start_time:", run_obj.start_time)

def fn_end(run_obj: Run):
    print("end_time:", run_obj.end_time)

chain = RunnableLambda(test_runnable).with_listeners(
    on_start=fn_start,
    on_end=fn_end
)
chain.invoke(2)

with_retry(*, retry_if_exception_type: tuple[type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, exponential_jitter_params: Optional[ExponentialJitterParams] = None, stop_after_attempt: int = 3) → Runnable[Input, Output]#

Create a new Runnable that retries the original Runnable on exceptions.

Parameters:

retry_if_exception_type (tuple[type[BaseException], ...]) – A tuple of exception types to retry on. Defaults to (Exception,).
wait_exponential_jitter (bool) – Whether to add jitter to the wait time between retries. Defaults to True.
stop_after_attempt (int) – The maximum number of attempts to make before giving up. Defaults to 3.
exponential_jitter_params (Optional[ExponentialJitterParams]) – Parameters for tenacity.wait_exponential_jitter. Namely: initial, max, exp_base, and jitter (all float values).

Returns:

A new Runnable that retries the original Runnable on exceptions.

Return type:

Runnable[Input, Output]

Example:

from langchain_core.runnables import RunnableLambda

count = 0


def _lambda(x: int) -> None:
    global count
    count = count + 1
    if x == 1:
        raise ValueError("x is 1")
    else:
         pass


runnable = RunnableLambda(_lambda)
try:
    runnable.with_retry(
        stop_after_attempt=2,
        retry_if_exception_type=(ValueError,),
    ).invoke(1)
except ValueError:
    pass

assert (count == 2)

Not implemented on this class.

Parameters:

schema (dict | type)
kwargs (Any)

Return type:

with_types( *, input_type: type[Input] | None = None, output_type: type[Output] | None = None, ) → Runnable[Input, Output]#

Bind input and output types to a Runnable, returning a new Runnable.

Parameters:

input_type (type[Input] | None) – The input type to bind to the Runnable. Defaults to None.
output_type (type[Output] | None) – The output type to bind to the Runnable. Defaults to None.

Returns:

A new Runnable with the types bound.

Return type:

Runnable[Input, Output]