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 agentsAuto-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
Installation
$ npx promptcreek add agentsAuto-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
- Installation Guide - SDK setup and migration
- Agent Configuration - All config options and CRUD examples
- Client Tools - Webhook, client, and system tools
- Widget Embedding - Website integration
- Outbound Calls - Twilio phone call integration
Supported Agents
Attribution
Details
- License
- MIT
- Source
- admin
- Published
- 3/18/2026
Tags
Related Skills
Agent Protocol
Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.
Agent Workflow Designer
Agent Workflow Designer
CTO Advisor
Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Use when assessing technical debt, scaling engineering teams, evaluating technologies, making architecture decisions, establishing engineering metrics, or when user mentions CTO, tech debt, technical debt, team scaling, architecture decisions, technology evaluation, engineering metrics, DORA metrics, or technology strategy.