# This file was auto-generated by Fern from our API Definition. from __future__ import annotations import typing from ...core.client_wrapper import AsyncClientWrapper, SyncClientWrapper from ...core.request_options import RequestOptions from ...types.adhoc_agent_config_override_for_test_request_model import AdhocAgentConfigOverrideForTestRequestModel from ...types.agent_platform_settings_request_model import AgentPlatformSettingsRequestModel from ...types.agent_simulated_chat_test_response_model import AgentSimulatedChatTestResponseModel from ...types.agent_sort_by import AgentSortBy from ...types.agent_workflow_request_model import AgentWorkflowRequestModel from ...types.conversation_simulation_specification import ConversationSimulationSpecification from ...types.conversational_config import ConversationalConfig from ...types.create_agent_response_model import CreateAgentResponseModel from ...types.get_agent_response_model import GetAgentResponseModel from ...types.get_agents_page_response_model import GetAgentsPageResponseModel from ...types.get_test_suite_invocation_response_model import GetTestSuiteInvocationResponseModel from ...types.prompt_evaluation_criteria import PromptEvaluationCriteria from ...types.single_test_run_request_model import SingleTestRunRequestModel from ...types.sort_direction import SortDirection from .raw_client import AsyncRawAgentsClient, RawAgentsClient if typing.TYPE_CHECKING: from .knowledge_base.client import AsyncKnowledgeBaseClient, KnowledgeBaseClient from .link.client import AsyncLinkClient, LinkClient from .llm_usage.client import AsyncLlmUsageClient, LlmUsageClient from .widget.client import AsyncWidgetClient, WidgetClient # this is used as the default value for optional parameters OMIT = typing.cast(typing.Any, ...) class AgentsClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._raw_client = RawAgentsClient(client_wrapper=client_wrapper) self._client_wrapper = client_wrapper self._widget: typing.Optional[WidgetClient] = None self._link: typing.Optional[LinkClient] = None self._knowledge_base: typing.Optional[KnowledgeBaseClient] = None self._llm_usage: typing.Optional[LlmUsageClient] = None @property def with_raw_response(self) -> RawAgentsClient: """ Retrieves a raw implementation of this client that returns raw responses. Returns ------- RawAgentsClient """ return self._raw_client def create( self, *, conversation_config: ConversationalConfig, platform_settings: typing.Optional[AgentPlatformSettingsRequestModel] = OMIT, workflow: typing.Optional[AgentWorkflowRequestModel] = OMIT, name: typing.Optional[str] = OMIT, tags: typing.Optional[typing.Sequence[str]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> CreateAgentResponseModel: """ Create an agent from a config object Parameters ---------- conversation_config : ConversationalConfig Conversation configuration for an agent platform_settings : typing.Optional[AgentPlatformSettingsRequestModel] Platform settings for the agent are all settings that aren't related to the conversation orchestration and content. workflow : typing.Optional[AgentWorkflowRequestModel] Workflow for the agent. This is used to define the flow of the conversation and how the agent interacts with tools. name : typing.Optional[str] A name to make the agent easier to find tags : typing.Optional[typing.Sequence[str]] Tags to help classify and filter the agent request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreateAgentResponseModel Successful Response Examples -------- from elevenlabs import ConversationalConfig, ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.create( conversation_config=ConversationalConfig(), ) """ _response = self._raw_client.create( conversation_config=conversation_config, platform_settings=platform_settings, workflow=workflow, name=name, tags=tags, request_options=request_options, ) return _response.data def get(self, agent_id: str, *, request_options: typing.Optional[RequestOptions] = None) -> GetAgentResponseModel: """ Retrieve config for an agent Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.get( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) """ _response = self._raw_client.get(agent_id, request_options=request_options) return _response.data def delete(self, agent_id: str, *, request_options: typing.Optional[RequestOptions] = None) -> None: """ Delete an agent Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- None Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.delete( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) """ _response = self._raw_client.delete(agent_id, request_options=request_options) return _response.data def update( self, agent_id: str, *, conversation_config: typing.Optional[ConversationalConfig] = OMIT, platform_settings: typing.Optional[AgentPlatformSettingsRequestModel] = OMIT, workflow: typing.Optional[AgentWorkflowRequestModel] = OMIT, name: typing.Optional[str] = OMIT, tags: typing.Optional[typing.Sequence[str]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> GetAgentResponseModel: """ Patches an Agent settings Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. conversation_config : typing.Optional[ConversationalConfig] Conversation configuration for an agent platform_settings : typing.Optional[AgentPlatformSettingsRequestModel] Platform settings for the agent are all settings that aren't related to the conversation orchestration and content. workflow : typing.Optional[AgentWorkflowRequestModel] Workflow for the agent. This is used to define the flow of the conversation and how the agent interacts with tools. name : typing.Optional[str] A name to make the agent easier to find tags : typing.Optional[typing.Sequence[str]] Tags to help classify and filter the agent request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.update( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) """ _response = self._raw_client.update( agent_id, conversation_config=conversation_config, platform_settings=platform_settings, workflow=workflow, name=name, tags=tags, request_options=request_options, ) return _response.data def list( self, *, page_size: typing.Optional[int] = None, search: typing.Optional[str] = None, archived: typing.Optional[bool] = None, sort_direction: typing.Optional[SortDirection] = None, sort_by: typing.Optional[AgentSortBy] = None, cursor: typing.Optional[str] = None, request_options: typing.Optional[RequestOptions] = None, ) -> GetAgentsPageResponseModel: """ Returns a list of your agents and their metadata. Parameters ---------- page_size : typing.Optional[int] How many Agents to return at maximum. Can not exceed 100, defaults to 30. search : typing.Optional[str] Search by agents name. archived : typing.Optional[bool] Filter agents by archived status sort_direction : typing.Optional[SortDirection] The direction to sort the results sort_by : typing.Optional[AgentSortBy] The field to sort the results by cursor : typing.Optional[str] Used for fetching next page. Cursor is returned in the response. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentsPageResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.list( page_size=1, search="search", archived=True, sort_direction="asc", sort_by="name", cursor="cursor", ) """ _response = self._raw_client.list( page_size=page_size, search=search, archived=archived, sort_direction=sort_direction, sort_by=sort_by, cursor=cursor, request_options=request_options, ) return _response.data def duplicate( self, agent_id: str, *, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> CreateAgentResponseModel: """ Create a new agent by duplicating an existing one Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. name : typing.Optional[str] A name to make the agent easier to find request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreateAgentResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.duplicate( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) """ _response = self._raw_client.duplicate(agent_id, name=name, request_options=request_options) return _response.data def simulate_conversation( self, agent_id: str, *, simulation_specification: ConversationSimulationSpecification, extra_evaluation_criteria: typing.Optional[typing.Sequence[PromptEvaluationCriteria]] = OMIT, new_turns_limit: typing.Optional[int] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AgentSimulatedChatTestResponseModel: """ Run a conversation between the agent and a simulated user. Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. simulation_specification : ConversationSimulationSpecification A specification detailing how the conversation should be simulated extra_evaluation_criteria : typing.Optional[typing.Sequence[PromptEvaluationCriteria]] A list of evaluation criteria to test new_turns_limit : typing.Optional[int] Maximum number of new turns to generate in the conversation simulation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AgentSimulatedChatTestResponseModel Successful Response Examples -------- from elevenlabs import ( AgentConfig, ConversationSimulationSpecification, ElevenLabs, ) client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.simulate_conversation( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfig( first_message="Hello, how can I help you today?", language="en", disable_first_message_interruptions=False, ), ), ) """ _response = self._raw_client.simulate_conversation( agent_id, simulation_specification=simulation_specification, extra_evaluation_criteria=extra_evaluation_criteria, new_turns_limit=new_turns_limit, request_options=request_options, ) return _response.data def simulate_conversation_stream( self, agent_id: str, *, simulation_specification: ConversationSimulationSpecification, extra_evaluation_criteria: typing.Optional[typing.Sequence[PromptEvaluationCriteria]] = OMIT, new_turns_limit: typing.Optional[int] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> None: """ Run a conversation between the agent and a simulated user and stream back the response. Response is streamed back as partial lists of messages that should be concatenated and once the conversation has complete a single final message with the conversation analysis will be sent. Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. simulation_specification : ConversationSimulationSpecification A specification detailing how the conversation should be simulated extra_evaluation_criteria : typing.Optional[typing.Sequence[PromptEvaluationCriteria]] A list of evaluation criteria to test new_turns_limit : typing.Optional[int] Maximum number of new turns to generate in the conversation simulation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- None Examples -------- from elevenlabs import ( AgentConfig, ConversationSimulationSpecification, ElevenLabs, ) client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.simulate_conversation_stream( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfig( first_message="Hello, how can I help you today?", language="en", disable_first_message_interruptions=False, ), ), ) """ _response = self._raw_client.simulate_conversation_stream( agent_id, simulation_specification=simulation_specification, extra_evaluation_criteria=extra_evaluation_criteria, new_turns_limit=new_turns_limit, request_options=request_options, ) return _response.data def run_tests( self, agent_id: str, *, tests: typing.Sequence[SingleTestRunRequestModel], agent_config_override: typing.Optional[AdhocAgentConfigOverrideForTestRequestModel] = OMIT, branch_id: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> GetTestSuiteInvocationResponseModel: """ Run selected tests on the agent with provided configuration. If the agent configuration is provided, it will be used to override default agent configuration. Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. tests : typing.Sequence[SingleTestRunRequestModel] List of tests to run on the agent agent_config_override : typing.Optional[AdhocAgentConfigOverrideForTestRequestModel] Configuration overrides to use for testing. If not provided, the agent's default configuration will be used. branch_id : typing.Optional[str] ID of the branch to run the tests on. If not provided, the tests will be run on the agent default configuration. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetTestSuiteInvocationResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs, SingleTestRunRequestModel client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.run_tests( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", tests=[ SingleTestRunRequestModel( test_id="test_id", ) ], ) """ _response = self._raw_client.run_tests( agent_id, tests=tests, agent_config_override=agent_config_override, branch_id=branch_id, request_options=request_options, ) return _response.data @property def widget(self): if self._widget is None: from .widget.client import WidgetClient # noqa: E402 self._widget = WidgetClient(client_wrapper=self._client_wrapper) return self._widget @property def link(self): if self._link is None: from .link.client import LinkClient # noqa: E402 self._link = LinkClient(client_wrapper=self._client_wrapper) return self._link @property def knowledge_base(self): if self._knowledge_base is None: from .knowledge_base.client import KnowledgeBaseClient # noqa: E402 self._knowledge_base = KnowledgeBaseClient(client_wrapper=self._client_wrapper) return self._knowledge_base @property def llm_usage(self): if self._llm_usage is None: from .llm_usage.client import LlmUsageClient # noqa: E402 self._llm_usage = LlmUsageClient(client_wrapper=self._client_wrapper) return self._llm_usage class AsyncAgentsClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._raw_client = AsyncRawAgentsClient(client_wrapper=client_wrapper) self._client_wrapper = client_wrapper self._widget: typing.Optional[AsyncWidgetClient] = None self._link: typing.Optional[AsyncLinkClient] = None self._knowledge_base: typing.Optional[AsyncKnowledgeBaseClient] = None self._llm_usage: typing.Optional[AsyncLlmUsageClient] = None @property def with_raw_response(self) -> AsyncRawAgentsClient: """ Retrieves a raw implementation of this client that returns raw responses. Returns ------- AsyncRawAgentsClient """ return self._raw_client async def create( self, *, conversation_config: ConversationalConfig, platform_settings: typing.Optional[AgentPlatformSettingsRequestModel] = OMIT, workflow: typing.Optional[AgentWorkflowRequestModel] = OMIT, name: typing.Optional[str] = OMIT, tags: typing.Optional[typing.Sequence[str]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> CreateAgentResponseModel: """ Create an agent from a config object Parameters ---------- conversation_config : ConversationalConfig Conversation configuration for an agent platform_settings : typing.Optional[AgentPlatformSettingsRequestModel] Platform settings for the agent are all settings that aren't related to the conversation orchestration and content. workflow : typing.Optional[AgentWorkflowRequestModel] Workflow for the agent. This is used to define the flow of the conversation and how the agent interacts with tools. name : typing.Optional[str] A name to make the agent easier to find tags : typing.Optional[typing.Sequence[str]] Tags to help classify and filter the agent request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreateAgentResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs, ConversationalConfig client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.create( conversation_config=ConversationalConfig(), ) asyncio.run(main()) """ _response = await self._raw_client.create( conversation_config=conversation_config, platform_settings=platform_settings, workflow=workflow, name=name, tags=tags, request_options=request_options, ) return _response.data async def get( self, agent_id: str, *, request_options: typing.Optional[RequestOptions] = None ) -> GetAgentResponseModel: """ Retrieve config for an agent Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.get( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) asyncio.run(main()) """ _response = await self._raw_client.get(agent_id, request_options=request_options) return _response.data async def delete(self, agent_id: str, *, request_options: typing.Optional[RequestOptions] = None) -> None: """ Delete an agent Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- None Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.delete( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) asyncio.run(main()) """ _response = await self._raw_client.delete(agent_id, request_options=request_options) return _response.data async def update( self, agent_id: str, *, conversation_config: typing.Optional[ConversationalConfig] = OMIT, platform_settings: typing.Optional[AgentPlatformSettingsRequestModel] = OMIT, workflow: typing.Optional[AgentWorkflowRequestModel] = OMIT, name: typing.Optional[str] = OMIT, tags: typing.Optional[typing.Sequence[str]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> GetAgentResponseModel: """ Patches an Agent settings Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. conversation_config : typing.Optional[ConversationalConfig] Conversation configuration for an agent platform_settings : typing.Optional[AgentPlatformSettingsRequestModel] Platform settings for the agent are all settings that aren't related to the conversation orchestration and content. workflow : typing.Optional[AgentWorkflowRequestModel] Workflow for the agent. This is used to define the flow of the conversation and how the agent interacts with tools. name : typing.Optional[str] A name to make the agent easier to find tags : typing.Optional[typing.Sequence[str]] Tags to help classify and filter the agent request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.update( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) asyncio.run(main()) """ _response = await self._raw_client.update( agent_id, conversation_config=conversation_config, platform_settings=platform_settings, workflow=workflow, name=name, tags=tags, request_options=request_options, ) return _response.data async def list( self, *, page_size: typing.Optional[int] = None, search: typing.Optional[str] = None, archived: typing.Optional[bool] = None, sort_direction: typing.Optional[SortDirection] = None, sort_by: typing.Optional[AgentSortBy] = None, cursor: typing.Optional[str] = None, request_options: typing.Optional[RequestOptions] = None, ) -> GetAgentsPageResponseModel: """ Returns a list of your agents and their metadata. Parameters ---------- page_size : typing.Optional[int] How many Agents to return at maximum. Can not exceed 100, defaults to 30. search : typing.Optional[str] Search by agents name. archived : typing.Optional[bool] Filter agents by archived status sort_direction : typing.Optional[SortDirection] The direction to sort the results sort_by : typing.Optional[AgentSortBy] The field to sort the results by cursor : typing.Optional[str] Used for fetching next page. Cursor is returned in the response. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentsPageResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.list( page_size=1, search="search", archived=True, sort_direction="asc", sort_by="name", cursor="cursor", ) asyncio.run(main()) """ _response = await self._raw_client.list( page_size=page_size, search=search, archived=archived, sort_direction=sort_direction, sort_by=sort_by, cursor=cursor, request_options=request_options, ) return _response.data async def duplicate( self, agent_id: str, *, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> CreateAgentResponseModel: """ Create a new agent by duplicating an existing one Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. name : typing.Optional[str] A name to make the agent easier to find request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreateAgentResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.duplicate( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) asyncio.run(main()) """ _response = await self._raw_client.duplicate(agent_id, name=name, request_options=request_options) return _response.data async def simulate_conversation( self, agent_id: str, *, simulation_specification: ConversationSimulationSpecification, extra_evaluation_criteria: typing.Optional[typing.Sequence[PromptEvaluationCriteria]] = OMIT, new_turns_limit: typing.Optional[int] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AgentSimulatedChatTestResponseModel: """ Run a conversation between the agent and a simulated user. Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. simulation_specification : ConversationSimulationSpecification A specification detailing how the conversation should be simulated extra_evaluation_criteria : typing.Optional[typing.Sequence[PromptEvaluationCriteria]] A list of evaluation criteria to test new_turns_limit : typing.Optional[int] Maximum number of new turns to generate in the conversation simulation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AgentSimulatedChatTestResponseModel Successful Response Examples -------- import asyncio from elevenlabs import ( AgentConfig, AsyncElevenLabs, ConversationSimulationSpecification, ) client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.simulate_conversation( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfig( first_message="Hello, how can I help you today?", language="en", disable_first_message_interruptions=False, ), ), ) asyncio.run(main()) """ _response = await self._raw_client.simulate_conversation( agent_id, simulation_specification=simulation_specification, extra_evaluation_criteria=extra_evaluation_criteria, new_turns_limit=new_turns_limit, request_options=request_options, ) return _response.data async def simulate_conversation_stream( self, agent_id: str, *, simulation_specification: ConversationSimulationSpecification, extra_evaluation_criteria: typing.Optional[typing.Sequence[PromptEvaluationCriteria]] = OMIT, new_turns_limit: typing.Optional[int] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> None: """ Run a conversation between the agent and a simulated user and stream back the response. Response is streamed back as partial lists of messages that should be concatenated and once the conversation has complete a single final message with the conversation analysis will be sent. Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. simulation_specification : ConversationSimulationSpecification A specification detailing how the conversation should be simulated extra_evaluation_criteria : typing.Optional[typing.Sequence[PromptEvaluationCriteria]] A list of evaluation criteria to test new_turns_limit : typing.Optional[int] Maximum number of new turns to generate in the conversation simulation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- None Examples -------- import asyncio from elevenlabs import ( AgentConfig, AsyncElevenLabs, ConversationSimulationSpecification, ) client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.simulate_conversation_stream( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfig( first_message="Hello, how can I help you today?", language="en", disable_first_message_interruptions=False, ), ), ) asyncio.run(main()) """ _response = await self._raw_client.simulate_conversation_stream( agent_id, simulation_specification=simulation_specification, extra_evaluation_criteria=extra_evaluation_criteria, new_turns_limit=new_turns_limit, request_options=request_options, ) return _response.data async def run_tests( self, agent_id: str, *, tests: typing.Sequence[SingleTestRunRequestModel], agent_config_override: typing.Optional[AdhocAgentConfigOverrideForTestRequestModel] = OMIT, branch_id: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> GetTestSuiteInvocationResponseModel: """ Run selected tests on the agent with provided configuration. If the agent configuration is provided, it will be used to override default agent configuration. Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. tests : typing.Sequence[SingleTestRunRequestModel] List of tests to run on the agent agent_config_override : typing.Optional[AdhocAgentConfigOverrideForTestRequestModel] Configuration overrides to use for testing. If not provided, the agent's default configuration will be used. branch_id : typing.Optional[str] ID of the branch to run the tests on. If not provided, the tests will be run on the agent default configuration. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetTestSuiteInvocationResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs, SingleTestRunRequestModel client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.run_tests( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", tests=[ SingleTestRunRequestModel( test_id="test_id", ) ], ) asyncio.run(main()) """ _response = await self._raw_client.run_tests( agent_id, tests=tests, agent_config_override=agent_config_override, branch_id=branch_id, request_options=request_options, ) return _response.data @property def widget(self): if self._widget is None: from .widget.client import AsyncWidgetClient # noqa: E402 self._widget = AsyncWidgetClient(client_wrapper=self._client_wrapper) return self._widget @property def link(self): if self._link is None: from .link.client import AsyncLinkClient # noqa: E402 self._link = AsyncLinkClient(client_wrapper=self._client_wrapper) return self._link @property def knowledge_base(self): if self._knowledge_base is None: from .knowledge_base.client import AsyncKnowledgeBaseClient # noqa: E402 self._knowledge_base = AsyncKnowledgeBaseClient(client_wrapper=self._client_wrapper) return self._knowledge_base @property def llm_usage(self): if self._llm_usage is None: from .llm_usage.client import AsyncLlmUsageClient # noqa: E402 self._llm_usage = AsyncLlmUsageClient(client_wrapper=self._client_wrapper) return self._llm_usage