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/elevenlabs-agents

Build conversational AI voice agents on the ElevenLabs platform. Configure agent + tools + knowledge base, integrate SDK (React / React Native / Swift / JS / server-side), test, deploy. Use whenever the user mentions ElevenLabs, building a voice agent, an AI phone system, an AI

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jezweb-skills
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Claude Code

$ npx -y skills add jezweb/claude-skills --skill elevenlabs-agents --agent claude-code

SKILL.md

elevenlabs-agents.SKILL.md
name: elevenlabs-agents
description: "Build conversational AI voice agents on the ElevenLabs platform. Configure agent + tools + knowledge base, integrate SDK (React / React Native / Swift / JS / server-side), test, deploy. Use whenever the user mentions ElevenLabs, building a voice agent, an AI phone system, an AI receptionist, conversational AI, or troubleshooting deprecated @11labs packages, webhook errors, CSP violations, localhost allowlist, or tool parsing errors."
compatibility: claude-code-only

ElevenLabs Agent Builder

Build a production-ready conversational AI voice agent. Produces a configured agent with tools, knowledge base, and SDK integration.

Packages

npm install @elevenlabs/react           # React SDK
npm install @elevenlabs/client          # JavaScript SDK (browser + server)
npm install @elevenlabs/react-native    # React Native SDK
npm install @elevenlabs/elevenlabs-js   # Full API (server only)
npm install -g @elevenlabs/agents-cli   # CLI ("Agents as Code")

**DEPRECATED:** `@11labs/react`, `@11labs/client` -- uninstall if present.

**Server-only warning:** `@elevenlabs/elevenlabs-js` uses Node.js `child_process` and won't work in browsers. Use `@elevenlabs/client` for browser environments, or create a proxy server.

Workflow

Step 1: Create Agent via Dashboard or CLI

**Dashboard:** https://elevenlabs.io/app/conversational-ai -> Create Agent

**CLI (Agents as Code):**

elevenlabs agents init
elevenlabs agents add "Support Bot" --template customer-service
# Edit agent_configs/support-bot.json
elevenlabs agents push --env dev

Templates: `default`, `minimal`, `voice-only`, `text-only`, `customer-service`, `assistant`.

Configure:

  • **Voice** -- Choose from 5000+ voices or clone
  • **LLM** -- GPT, Claude, Gemini, or custom
  • **System prompt** -- Use the 6-component framework below
  • **First message** -- What the agent says when conversation starts

Step 2: Write the System Prompt

Use the 6-component framework for effective agent prompts:

**1. Personality** -- who the agent is:

You are [NAME], a [ROLE] at [COMPANY].
You have [EXPERIENCE]. Your traits: [LIST TRAITS].

**2. Environment** -- communication context:

You're communicating via [phone/chat/video].
Consider [environmental factors]. Adapt to [context].

**3. Tone** -- speech patterns and formality:

Tone: Professional yet warm. Use contractions for natural speech.
Avoid jargon. Keep responses to 2-3 sentences. Ask one question at a time.

**4. Goal** -- objectives and success criteria:

Primary Goal: Resolve customer issues on the first call.
Success: Customer verbally confirms issue is resolved.

**5. Guardrails** -- boundaries and ethics:

Never: provide medical/legal/financial advice, share confidential info.
Always: verify identity before account access, document interactions.
Escalation: customer requests manager, issue beyond knowledge base.

**6. Tools** -- available functions and when to use them:

1. lookup_order(order_id) -- Use when customer mentions an order.
2. transfer_to_supervisor() -- Use when issue requires manager approval.
Always explain what you're doing before calling a tool.

Step 3: Add Tools

**Client-side tools (run in browser):**

const clientTools = {
  updateCart: {
    description: "Add or remove items from the shopping cart",
    parameters: z.object({
      action: z.enum(['add', 'remove']),
      item: z.string(),
      quantity: z.number().min(1)
    }),
    handler: async ({ action, item, quantity }) => {
      const cart = getCart();
      action === 'add' ? cart.add(item, quantity) : cart.remove(item, quantity);
      return { success: true, total: cart.total, items: cart.items.length };
    }
  },
  navigate: {
    description: "Navigate user to a different page",
    parameters: z.object({ url: z.string().url() }),
    handler: async ({ url }) => { window.location.href = url; return { success: true }; }
  }
};

**Server-side tools (webhooks):**

{
  "name": "get_weather",
  "description": "Fetch current weather for a city",
  "url": "https://api.weather.com/v1/current",
  "method": "GET",
  "parameters": {
    "type": "object",
    "properties": {
      "city": { "type": "string", "description": "City name" }
    },
    "required": ["city"]
  },
  "headers": {
    "Authorization": "Bearer {{secret__weather_api_key}}"
  }
}

Use `{{secret__key_name}}` for API keys in webhook headers -- never hardcode.

**MCP Tools -- CRITICAL COMPATIBILITY NOTE:**

ElevenLabs labels their MCP integration as "Streamable HTTP" but does NOT support the actual MCP 2025-03-26 Streamable HTTP spec (SSE responses). ElevenLabs expects:

  • Plain JSON responses (`application/json`), NOT SSE (`text/event-stream`)
  • Protocol version `2024-11-05`, NOT `2025-03-26`
  • Simple JSON-RPC over HTTP with direct JSON responses

What does NOT work:

  • Official MCP SDK's `createMcpHandler` (returns SSE)
  • Cloudflare Agents SDK `McpServer.serve()` (returns SSE)
  • Any server returning `Content-Type: text/event-stream`

Working MCP server pattern for ElevenLabs:

import { Hono } from 'hono';
import { cors } from 'hono/cors';

const tools = [{
  name: "my_tool",
  description: "Tool description",
  inputSchema: {
    type: "object",
    properties: { param1: { type: "string", description: "Description" } },
    required: ["param1"]
  }
}];

async function handleMCPRequest(request, env) {
  const { id, method, params } = request;
  switch (method) {
    case 'initialize':
      return {
        jsonrpc: '2.0', id,
        result: {
          protocolVersion: '2024-11-05',  // MUST be 2024-11-05
          serverInfo: { name: 'my-mcp', version: '1.0.0' },
          capabilities: { tools: {} }
        }
      };
    case 'tools/list':
      return { jsonrpc: '2.0', id, result: { tools } };
    case 'tools/call':
      const result = await handleTool(params.name, params.arguments, env);
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Questions about elevenlabs-agents.

Which coding agents can use elevenlabs-agents?
elevenlabs-agents installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.