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Agents

Build voice AI agents with ElevenLabs. Use when creating voice assistants, customer service bots, interactive voice characters, or any real-time voice conversation experience.

$ npx promptcreek add agents

Auto-detects your installed agents and installs the skill to each one.

What This Skill Does

The ElevenLabs Agents Platform enables users to build voice AI agents capable of natural conversations. It supports multiple LLM providers, custom tools, and easy web embedding, allowing for versatile agent creation. It's designed for developers and businesses looking to integrate conversational AI into their applications.

When to Use

  • Create a customer service chatbot with voice interaction.
  • Build a virtual assistant for internal company use.
  • Develop an interactive educational tool.
  • Design a voice-controlled smart home interface.
  • Implement a voice-based game character.
  • Prototype a new conversational AI product.

Key Features

Supports multiple LLM providers.
Allows for custom tool integration.
Enables easy web embedding.
Offers templates for quick agent creation.
Provides Python, JavaScript, and cURL interfaces.
Supports versioning for agent configurations.

Installation

Run in your project directory:
$ npx promptcreek add agents

Auto-detects your installed agents (Claude Code, Cursor, Codex, etc.) and installs the skill to each one.

View Full Skill Content

ElevenLabs Agents Platform

Build voice AI agents with natural conversations, multiple LLM providers, custom tools, and easy web embedding.

> Setup: See Installation Guide for CLI and SDK setup.

Quick Start with CLI

The ElevenLabs CLI is the recommended way to create and manage agents:

# Install CLI and authenticate

npm install -g @elevenlabs/cli

elevenlabs auth login

Initialize project and create an agent

elevenlabs agents init

elevenlabs agents add "My Assistant" --template complete

Push to ElevenLabs platform

elevenlabs agents push

Available templates: complete, minimal, voice-only, text-only, customer-service, assistant

Python

from elevenlabs import ElevenLabs

client = ElevenLabs()

agent = client.conversational_ai.agents.create(

name="My Assistant",

enable_versioning=True,

conversation_config={

"agent": {

"first_message": "Hello! How can I help?",

"language": "en",

"prompt": {

"prompt": "You are a helpful assistant. Be concise and friendly.",

"llm": "gemini-2.0-flash",

"temperature": 0.7

}

},

"tts": {"voice_id": "JBFqnCBsd6RMkjVDRZzb"}

}

)

JavaScript

import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js";

const client = new ElevenLabsClient();

const agent = await client.conversationalAi.agents.create({

name: "My Assistant",

enableVersioning: true,

conversationConfig: {

agent: {

firstMessage: "Hello! How can I help?",

language: "en",

prompt: {

prompt: "You are a helpful assistant.",

llm: "gemini-2.0-flash",

temperature: 0.7

}

},

tts: { voiceId: "JBFqnCBsd6RMkjVDRZzb" }

}

});

cURL

curl -X POST "https://api.elevenlabs.io/v1/convai/agents/create?enable_versioning=true" \

-H "xi-api-key: $ELEVENLABS_API_KEY" -H "Content-Type: application/json" \

-d '{"name": "My Assistant", "conversation_config": {"agent": {"first_message": "Hello!", "language": "en", "prompt": {"prompt": "You are helpful.", "llm": "gemini-2.0-flash"}}, "tts": {"voice_id": "JBFqnCBsd6RMkjVDRZzb"}}}'

Starting Conversations

Server-side (Python): Get signed URL for client connection:

signed_url = client.conversational_ai.conversations.get_signed_url(agent_id="your-agent-id")

Client-side (JavaScript):

import { Conversation } from "@elevenlabs/client";

const conversation = await Conversation.startSession({

agentId: "your-agent-id",

onMessage: (msg) => console.log("Agent:", msg.message),

onUserTranscript: (t) => console.log("User:", t.message),

onError: (e) => console.error(e)

});

React Hook:

import { useConversation } from "@elevenlabs/react";

const conversation = useConversation({ onMessage: (msg) => console.log(msg) });

// Get signed URL from backend, then:

await conversation.startSession({ signedUrl: token });

Configuration

| Provider | Models |

|----------|--------|

| OpenAI | gpt-5, gpt-5-mini, gpt-5-nano, gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-4o, gpt-4o-mini, gpt-4-turbo |

| Anthropic | claude-sonnet-4-6, claude-sonnet-4-5, claude-sonnet-4, claude-haiku-4-5, claude-3-7-sonnet, claude-3-5-sonnet, claude-3-haiku |

| Google | gemini-3.1-flash-lite-preview, gemini-3-pro-preview, gemini-3-flash-preview, gemini-2.5-flash, gemini-2.5-flash-lite, gemini-2.0-flash, gemini-2.0-flash-lite |

| ElevenLabs | glm-45-air-fp8, qwen3-30b-a3b, gpt-oss-120b |

| Custom | custom-llm (bring your own endpoint) |

Use GET /v1/convai/llm/list to inspect the current model catalog, including deprecation state, token/context limits, and capability flags such as image-input support.

Popular voices: JBFqnCBsd6RMkjVDRZzb (George), EXAVITQu4vr4xnSDxMaL (Sarah), onwK4e9ZLuTAKqWW03F9 (Daniel), XB0fDUnXU5powFXDhCwa (Charlotte)

Turn eagerness: patient (waits longer for user to finish), normal, or eager (responds quickly)

See Agent Configuration for all options.

Tools

Extend agents with webhook, client, or built-in system tools. Tools are defined inside conversation_config.agent.prompt:

"prompt": {

"prompt": "You are a helpful assistant that can check the weather.",

"llm": "gemini-2.0-flash",

"tools": [

# Webhook: server-side API call

{"type": "webhook", "name": "get_weather", "description": "Get weather",

"api_schema": {"url": "https://api.example.com/weather", "method": "POST",

"request_body_schema": {"type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"]}}},

# Client: runs in the browser

{"type": "client", "name": "show_product", "description": "Display a product",

"parameters": {"type": "object", "properties": {"productId": {"type": "string"}}, "required": ["productId"]}}

],

"built_in_tools": {

"end_call": {},

"transfer_to_number": {"transfers": [{"transfer_destination": {"type": "phone", "phone_number": "+1234567890"}, "condition": "User asks for human support"}]}

}

}

Client tools run in browser:

clientTools: {

show_product: async ({ productId }) => {

document.getElementById("product").src = /products/${productId};

return { success: true };

}

}

See Client Tools Reference for complete documentation.

Widget Embedding

<elevenlabs-convai agent-id="your-agent-id"></elevenlabs-convai>

<script src="https://unpkg.com/@elevenlabs/convai-widget-embed" async type="text/javascript"></script>

Customize with attributes: avatar-image-url, action-text, start-call-text, end-call-text.

See Widget Embedding Reference for all options.

Outbound Calls

Make outbound phone calls using your agent via Twilio integration:

Python

response = client.conversational_ai.twilio.outbound_call(

agent_id="your-agent-id",

agent_phone_number_id="your-phone-number-id",

to_number="+1234567890",

call_recording_enabled=True

)

print(f"Call initiated: {response.conversation_id}")

JavaScript

const response = await client.conversationalAi.twilio.outboundCall({

agentId: "your-agent-id",

agentPhoneNumberId: "your-phone-number-id",

toNumber: "+1234567890",

callRecordingEnabled: true,

});

cURL

curl -X POST "https://api.elevenlabs.io/v1/convai/twilio/outbound-call" \

-H "xi-api-key: $ELEVENLABS_API_KEY" -H "Content-Type: application/json" \

-d '{"agent_id": "your-agent-id", "agent_phone_number_id": "your-phone-number-id", "to_number": "+1234567890", "call_recording_enabled": true}'

See Outbound Calls Reference for configuration overrides and dynamic variables.

Managing Agents

Using CLI (Recommended)

# List agents and check status

elevenlabs agents list

elevenlabs agents status

Import agents from platform to local config

elevenlabs agents pull # Import all agents

elevenlabs agents pull --agent <agent-id> # Import specific agent

Push local changes to platform

elevenlabs agents push # Upload configurations

elevenlabs agents push --dry-run # Preview changes first

Add tools

elevenlabs tools add-webhook "Weather API"

elevenlabs tools add-client "UI Tool"

Project Structure

The CLI creates a project structure for managing agents:

your_project/

├── agents.json # Agent definitions

├── tools.json # Tool configurations

├── tests.json # Test configurations

├── agent_configs/ # Individual agent configs

├── tool_configs/ # Individual tool configs

└── test_configs/ # Individual test configs

SDK Examples

# List

agents = client.conversational_ai.agents.list()

Get

agent = client.conversational_ai.agents.get(agent_id="your-agent-id")

Update (partial - only include fields to change)

client.conversational_ai.agents.update(agent_id="your-agent-id", name="New Name")

client.conversational_ai.agents.update(agent_id="your-agent-id",

conversation_config={

"agent": {"prompt": {"prompt": "New instructions", "llm": "claude-sonnet-4"}}

})

Delete

client.conversational_ai.agents.delete(agent_id="your-agent-id")

See Agent Configuration for all configuration options and SDK examples.

Error Handling

try:

agent = client.conversational_ai.agents.create(...)

except Exception as e:

print(f"API error: {e}")

Common errors: 401 (invalid key), 404 (not found), 422 (invalid config), 429 (rate limit)

References

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Supported Agents

Claude CodeCursorCodexGemini CLIAiderWindsurfOpenClaw

Attribution

Details

License
MIT
Source
admin
Published
3/18/2026

Tags

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