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",
    environment="staging",
)

Client-side (JavaScript):

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

const conversation = await Conversation.startSession({
  agentId: "your-agent-id",
  environment: "staging",
  onMessage: (msg) => console.log("Agent:", msg.message),
  onUserTranscript: (t) => console.log("User:", t.message),
  onError: (e) => console.error(e)
});

React Hook: Wrap hook consumers in ConversationProvider. Prefer granular hooks such as useConversationControls and useConversationStatus for session controls and UI state; useConversation remains available as the convenience all-in-one hook. Pass provider-level callbacks such as onError when you want React to handle conversation errors in one place.

import {
  ConversationProvider,
  useConversationControls,
  useConversationStatus,
} from "@elevenlabs/react";

function Agent({ signedUrl }: { signedUrl: string }) {
  const { startSession, endSession } = useConversationControls();
  const { status } = useConversationStatus();

  if (status === "connected") {
    return <button onClick={endSession}>End conversation</button>;
  }

  return (
    <button onClick={() => startSession({ signedUrl })}>
      Start conversation
    </button>
  );
}

function App({ signedUrl }: { signedUrl: string }) {
  return (
    <ConversationProvider
      onError={(error) => console.error("Conversation error:", error)}
    >
      <Agent signedUrl={signedUrl} />
    </ConversationProvider>
  );
}

Configuration

Provider	Models
OpenAI	gpt-5.4, 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-3.1-pro-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, qwen35-35b-a3b, qwen35-397b-a17b, 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, capability flags such as image-input support, and model-specific reasoning effort 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.

System Prompt Structure

Section the prompt with markdown headings — the model prioritizes and interprets instructions more reliably (prompting guide):

# Personality   – named character, 2-3 traits
# Environment   – where they work, who they talk to
# Tone          – vocal style as 4-5 bullets
# Goal          – what success looks like (numbered for multi-step flows)

Keep instructions short and action-based. Mark critical steps with "This step is important." For critical refusal/safety rules, include concise instructions in the prompt and also configure independent custom Guardrails via platform_settings.guardrails (see Guardrails).

Tools

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

Workspace environment variables can resolve per-environment server tool URLs, headers, and auth connections, and runtime system variables such as {{system__conversation_history}} can pass full conversation context into tool calls when needed.

"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.

Built-in System Tools

Set under conversation_config.agent.prompt.built_in_tools. {} enables defaults; provide description to customize; omit to disable.

Tool	Enable for
end_call	All agents
language_detection	Multilingual agents
transfer_to_number	Phone-based human escalation
transfer_to_agent	Multi-agent workflows
skip_turn	Tutoring / coaching (silent listening)
voicemail_detection	Outbound calling
play_keypad_touch_tone	IVR navigation

Integration Tools

Pre-built connectors managed by the platform. Create a connection with credentials, then attach via tool_ids:

Integration	Use case
calcom	Scheduling appointments
salesforce	CRM lookups, case creation
hubspot	CRM, marketing, contacts
zendesk	Support ticketing

Three-step flow: POST /v1/convai/api-integrations/{id}/connections → GET /v1/convai/api-integrations/{id}/tools → POST /v1/convai/tools with api_integration_id and api_integration_connection_id. Attach to the agent with "prompt": {"tool_ids": ["tool_xxxx"]}. Inline tools and tool_ids can coexist — prefer an integration over a duplicate custom webhook.

Public-API Webhook Examples

No-auth APIs useful for prototypes (URLs must be HTTPS):

Tool	URL	Purpose
get_weather	https://wttr.in/{location}?format=j1	Current weather
search_wikipedia	https://en.wikipedia.org/api/rest_v1/page/summary/{topic}	Topic summary
get_exchange_rate	https://open.er-api.com/v6/latest/{base_currency}	FX rates

Workflows

Route conversations through discrete steps with branching logic. Define under the agent's top-level workflow field. Reference: Agent Workflows.

Node types: start (ID must be "start_node"), end, override_agent (subagent step with label + additional_prompt), dispatch_tool (executes a tool with success/failure routing), agent_transfer, transfer_to_number.

Edge types: unconditional, llm (natural-language condition), expression (deterministic data check). Tool nodes have separate success/failure edges.

Scope tools per step with additional_tool_ids on a node — prevents the wrong tool firing at the wrong step. Set additional_tool_ids: [] on conversational routing nodes such as greeting and classify_intent so they only converse:

{
  "type": "override_agent",
  "label": "Book Appointment",
  "additional_prompt": "Discuss preferred dates and doctors. Show the booking form once agreed.",
  "additional_tool_ids": ["show_booking_form", "display_appointment_card"],
  "position": {"x": 0, "y": 400}
}

Include position ({x, y}) on every node so the editor renders cleanly. Start at y=0, put end at the bottom, and space branches horizontally at x=-150 and x=150; suggested spacing is 200px vertical between levels and 300px horizontal between branches. Keep workflows to 4-7 nodes and always have a path to end.

Guardrails

Layered safety enforcement that runs independently of the LLM — configured under platform_settings.guardrails, not in the system prompt. Reference: Guardrails.

"platform_settings": {
  "guardrails": {
    "version": "1",
    "focus": {"is_enabled": true},
    "prompt_injection": {"is_enabled": true},
    "content": {"config": {"harassment": {"is_enabled": true, "threshold": 0.5}}},
    "custom": {
      "config": {
        "configs": [{
          "is_enabled": true,
          "name": "No medical diagnoses",
          "prompt": "Block the agent from providing medical diagnoses or treatment advice.",
          "execution_mode": "blocking",
          "trigger_action": {"type": "retry", "feedback": "Reason: {{trigger_reason}}"}
        }]
      }
    }
  }
}

Types: focus (on-topic), prompt_injection (manipulation defense), content (category filters), custom (LLM-evaluated domain rules). Content categories include harassment, profanity, sexual, violence, self_harm, and medical_and_legal_information — threshold range 0.0–1.0 (default 0.3). Custom rules use execution_mode: "blocking" with a trigger_action (e.g., retry with feedback). Custom guardrails evaluate in parallel and fail-open.

Per vertical: healthcare/finance/legal → enable medical_and_legal_information; education/youth → sexual/violence/self_harm/profanity; support/sales → harassment/profanity. All agents benefit from focus + prompt_injection + 2-4 custom rules.

Testing Agents

Three test types via POST /v1/convai/agent-testing/create, then attached with PATCH on the agent. Reference: Agent Testing.

Type	Purpose
llm	Scenario test — does the agent respond appropriately to a message?
tool	Tool-call test — right tool, right parameters?
simulation	Multi-turn flow with a simulated user persona

// Tool-call test (snake_case throughout; chat_history role is "user" or "agent")
{
  "name": "Books with correct doctor and date",
  "type": "tool",
  "chat_history": [
    {"role": "user", "message": "Dr. Smith on March 5 at 2pm", "time_in_call_secs": 10}
  ],
  "tool_call_parameters": {
    "referenced_tool": {"id": "show_booking_form", "type": "client"},
    "parameters": [
      {"path": "doctor_name", "eval": {"type": "llm", "description": "Should reference Dr. Smith"}},
      {"path": "date", "eval": {"type": "regex", "pattern": "2025-03-05|March 5"}}
    ]
  }
}

Eval strategies: exact, regex, llm. Attach via PATCH:

curl -s -X PATCH "https://api.elevenlabs.io/v1/convai/agents/{agent_id}" \
  -H "xi-api-key: $ELEVENLABS_API_KEY" -H "Content-Type: application/json" \
  -d '{"platform_settings": {"testing": {"attached_tests": [{"test_id": "test_xxxx"}]}}}'

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