About ElevenLabs
ElevenLabs provides conversational AI agents with speech synthesis. The elevenlabs-moss package provides MossClientTool, which registers Moss search as an ElevenLabs client tool named "search_knowledge_base". When the agent needs context, it calls the tool and Moss returns results in under 10ms.
Why Use Moss with ElevenLabs
01
MossClientTool registers as an ElevenLabs client tool
The agent calls `search_knowledge_base` when it needs context
02
Sub-10ms retrieval eliminates delay
The agent gets context before the user notices any pause
03
Configure in the ElevenLabs dashboard
Add a Client tool with parameter `name=query`, `type=string`
04
Hybrid search via alpha parameter
Balance semantic and keyword matching for optimal results
05
Pre-load indexes at startup with load_index()
For consistent low-latency responses
Quick Start
from elevenlabs.conversational_ai.conversation import ClientTools, Conversation
from elevenlabs.conversational_ai.default_audio_interface import DefaultAudioInterface
from elevenlabs import ElevenLabs
from elevenlabs_moss import MossClientTool
moss_tool = MossClientTool(
index_name="support-docs",
tool_name="search_knowledge_base",
top_k=3,
)
await moss_tool.load_index()
client_tools = ClientTools()
moss_tool.register(client_tools)
conversation = Conversation(
client=ElevenLabs(api_key="your-api-key"),
agent_id="your-agent-id",
requires_auth=False,
audio_interface=DefaultAudioInterface(),
client_tools=client_tools,
)
conversation.start_session()Get Started in 3 Steps
01
Install elevenlabs-moss
Run pip install elevenlabs-moss to install the ElevenLabs integration package for Moss.
02
Configure the ElevenLabs dashboard
In the ElevenLabs agent dashboard, add a Client tool. Set the tool name to search_knowledge_base with a parameter named "query" of type string.
03
Register and start conversation
Create a MossClientTool, call load_index(), then register() it into your client_tools dict. Pass client_tools to the ElevenLabs Conversation constructor.
Frequently asked questions
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