ElevenLabs Agents Platform

Overview

ElevenLabs Agents Platform is a comprehensive solution for building production-ready conversational AI voice agents. The platform coordinates four core components:

1. ASR (Automatic Speech Recognition) - Converts speech to text (32+ languages, sub-second latency)
2. LLM (Large Language Model) - Reasoning and response generation (GPT, Claude, Gemini, custom models)
3. TTS (Text-to-Speech) - Converts text to speech (5000+ voices, 31 languages, low latency)
4. Turn-Taking Model - Proprietary model that handles conversation timing and interruptions

🚨 Package Updates (January 2026)

ElevenLabs migrated to new scoped packages in August 2025. Current packages:

npm install @elevenlabs/react@0.12.3           # React SDK (Dec 2025: localization, Scribe fixes)
npm install @elevenlabs/client@0.12.2          # JavaScript SDK (Dec 2025: localization)
npm install @elevenlabs/react-native@0.5.7     # React Native SDK (Dec 2025: mic fixes, speed param)
npm install @elevenlabs/elevenlabs-js@2.30.0   # Base SDK (Jan 2026: latest)
npm install -g @elevenlabs/agents-cli@0.6.1    # CLI

DEPRECATED:

@11labs/react

@11labs/client

⚠️ CRITICAL: v1 TTS models were removed on 2025-12-15. Use Turbo v2/v2.5 only.

December 2025 Updates

Widget Improvements (v0.5.5):

• Microphone permission handling improvements (better UX for permission requests)
• Text-only mode (

  chat_mode: true

  ) no longer requires microphone access

• end_call

  system tool fix (no longer omits last message)

SDK Fixes:

• Scribe audio format parameter now correctly transmitted (v2.32.0, Jan 2026)
• React Native infinite loop fix in useEffect dependencies (v0.5.6)
• Speed parameter support in TTS overrides (v0.5.7)
• Localization support for chat UI terms (v0.12.3)

Package Selection Guide

Which ElevenLabs package should I use?

@elevenlabs/elevenlabs-js

@elevenlabs/client

@elevenlabs/react

@elevenlabs/react-native

⚠️ Why elevenlabs-js doesn't work in browser:

• Depends on Node.js

  child_process

  module (by design)
• Error:

  Module not found: Can't resolve 'child_process'

• Workaround for browser API access: Create proxy server endpoint using

  elevenlabs-js

  , call proxy from browser

Affected Frameworks:

• Next.js client components
• Vite browser builds
• Electron renderer process
• Tauri webview

Source: GitHub Issue #293

1. Quick Start

React SDK

npm install @elevenlabs/react zod

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

const { startConversation, stopConversation, status } = useConversation({
  agentId: 'your-agent-id',
  signedUrl: '/api/elevenlabs/auth', // Recommended (secure)
  // OR apiKey: process.env.NEXT_PUBLIC_ELEVENLABS_API_KEY,

  clientTools: { /* browser-side tools */ },
  onEvent: (event) => { /* transcript, agent_response, tool_call */ },
  serverLocation: 'us' // 'eu-residency' | 'in-residency' | 'global'
});

CLI ("Agents as Code")

npm install -g @elevenlabs/agents-cli
elevenlabs auth login
elevenlabs agents init                              # Creates agents.json, tools.json, tests.json
elevenlabs agents add "Bot" --template customer-service
elevenlabs agents push --env dev                    # Deploy
elevenlabs agents test "Bot"                        # Test

API (Programmatic)

import { ElevenLabsClient } from 'elevenlabs';
const client = new ElevenLabsClient({ apiKey: process.env.ELEVENLABS_API_KEY });

const agent = await client.agents.create({
  name: 'Support Bot',
  conversation_config: {
    agent: { prompt: { prompt: "...", llm: "gpt-4o" }, language: "en" },
    tts: { model_id: "eleven_turbo_v2_5", voice_id: "your-voice-id" }
  }
});

2. SDK Parameter Naming (camelCase vs snake_case)

CRITICAL: The JS SDK uses camelCase for parameters while the Python SDK and API use snake_case. Using snake_case in JS causes silent failures where parameters are ignored.

Common Parameters:

model_id

modelId

voice_id

voiceId

output_format

outputFormat

voice_settings

voiceSettings

Example:

// ❌ WRONG - parameter ignored (snake_case):
const stream = await elevenlabs.textToSpeech.convert(voiceId, {
  model_id: "eleven_v3",  // Silently ignored!
  text: "Hello"
});

// ✅ CORRECT - use camelCase:
const stream = await elevenlabs.textToSpeech.convert(voiceId, {
  modelId: "eleven_v3",   // Works!
  text: "Hello"
});

Tip: Always check TypeScript types for correct parameter names. This is the most common error when migrating from Python SDK.

Source: GitHub Issue #300

3. Agent Configuration

System Prompt Architecture (6 Components)

1. Personality - Identity, role, character traits 2. Environment - Communication context (phone, web, video) 3. Tone - Formality, speech patterns, verbosity 4. Goal - Objectives and success criteria 5. Guardrails - Boundaries, prohibited topics, ethical constraints 6. Tools - Available capabilities and when to use them

Template:

{
  "agent": {
    "prompt": {
      "prompt": "Personality:\n[Agent identity and role]\n\nEnvironment:\n[Communication context]\n\nTone:\n[Speech style]\n\nGoal:\n[Primary objectives]\n\nGuardrails:\n[Boundaries and constraints]\n\nTools:\n[Available tools and usage]",
      "llm": "gpt-4o", // gpt-5.1, claude-sonnet-4-5, gemini-3-pro-preview
      "temperature": 0.7
    }
  }
}

2025 LLM Models:

• gpt-5.1

  ,

  gpt-5.1-2025-11-13

  (Oct 2025)

• claude-sonnet-4-5

  ,

  claude-sonnet-4-5@20250929

  (Oct 2025)

• gemini-3-pro-preview

  (2025)

• gemini-2.5-flash-preview-09-2025

  (Oct 2025)

Turn-Taking Modes

{ "conversation_config": { "turn": { "mode": "patient" } } }

Workflows & Agent Management (2025)

Workflow Features:

• Subagent Nodes - Override prompt, voice, turn-taking per node
• Tool Nodes - Guarantee tool execution
• Edges - Conditional routing with

  edge_order

  (determinism, Oct 2025)

{
  "workflow": {
    "nodes": [
      { "id": "node_1", "type": "subagent", "config": { "system_prompt": "...", "turn_eagerness": "patient" } },
      { "id": "node_2", "type": "tool", "tool_name": "transfer_to_human" }
    ],
    "edges": [{ "from": "node_1", "to": "node_2", "condition": "escalation", "edge_order": 1 }]
  }
}

Agent Management (2025):

• Agent Archiving -

  archived: true

  field (Oct 2025)
• Agent Duplication - Clone existing agents
• Service Account API Keys - Management endpoints (Jul 2025)

Dynamic Variables

Use

{{var_name}}

System Variables:

• {{system__agent_id}}

  ,

  {{system__conversation_id}}

• {{system__caller_id}}

  ,

  {{system__called_number}}

  (telephony)

• {{system__call_duration_secs}}

  ,

  {{system__time_utc}}

• {{system__call_sid}}

  (Twilio only)

Custom Variables:

await client.conversations.create({
  agent_id: "agent_123",
  dynamic_variables: { user_name: "John", account_tier: "premium" }
});

Secret Variables:

{{secret__api_key}}

⚠️ Error: Missing variables cause "Missing required dynamic variables" - always provide all referenced variables.

3. Voice & Language Features

Multi-Voice, Pronunciation & Speed

Multi-Voice - Switch voices dynamically (adds ~200ms latency per switch):

{ "prompt": "When speaking as customer, use voice_id 'voice_abc'. As agent, use 'voice_def'." }

Pronunciation Dictionary - IPA, CMU, word substitutions (Turbo v2/v2.5 only):

{
  "pronunciation_dictionary": [
    { "word": "API", "pronunciation": "ey-pee-ay", "format": "cmu" },
    { "word": "AI", "substitution": "artificial intelligence" }
  ]
}

PATCH Support (Aug 2025) - Update dictionaries without replacement

Speed Control - 0.7x-1.2x (use 0.9x-1.1x for natural sound):

{ "voice_settings": { "speed": 1.0 } }

Voice Cloning Best Practices:

• Clean audio (no noise, music, pops)
• Consistent microphone distance
• 1-2 minutes of audio
• Use language-matched voices (English voices fail on non-English)

Language Configuration

32+ Languages with automatic detection and in-conversation switching.

Multi-Language Presets:

{
  "language_presets": [
    { "language": "en", "voice_id": "en_voice", "first_message": "Hello!" },
    { "language": "es", "voice_id": "es_voice", "first_message": "¡Hola!" }
  ]
}

4. Knowledge Base & RAG

Enable agents to access large knowledge bases without loading entire documents into context.

Workflow:

1. Upload documents (PDF, TXT, DOCX)
2. Compute RAG index (vector embeddings)
3. Agent retrieves relevant chunks during conversation

Configuration:

{
  "agent": { "prompt": { "knowledge_base": ["doc_id_1", "doc_id_2"] } },
  "knowledge_base_config": {
    "max_chunks": 5,
    "vector_distance_threshold": 0.8
  }
}

API Upload:

const doc = await client.knowledgeBase.upload({ file: fs.createReadStream('docs.pdf'), name: 'Docs' });
await client.knowledgeBase.computeRagIndex({ document_id: doc.id, embedding_model: 'e5_mistral_7b' });

⚠️ Gotchas: RAG adds ~500ms latency. Check index status before use - indexing can take minutes.

5. Tools (4 Types)

⚠️ BREAKING CHANGE: prompt.tools Deprecated (July 2025)

The legacy

prompt.tools

Migration Timeline:

• July 14, 2025: Legacy format still accepted
• July 15, 2025: GET responses stop including

  tools

  field
• July 23, 2025: POST/PATCH reject

  prompt.tools

  (active now)

Old Format (no longer works):

{
  agent: {
    prompt: {
      tools: [{ name: "get_weather", url: "...", method: "GET" }]
    }
  }
}

New Format (required):

{
  agent: {
    prompt: {
      tool_ids: ["tool_abc123"],         // Client/server tools
      built_in_tools: ["end_call"]       // System tools (new field)
    }
  }
}

Error if both used: "A request must include either prompt.tool_ids or the legacy prompt.tools array — never both"

Note: All tools from legacy format were auto-migrated to standalone tool records.

Source: Official Migration Guide

A. Client Tools (Browser/Mobile)

Execute in browser or mobile app. Tool names case-sensitive.

clientTools: {
  updateCart: {
    description: "Update shopping cart",
    parameters: z.object({ item: z.string(), quantity: z.number() }),
    handler: async ({ item, quantity }) => {
      // Client-side logic
      return { success: true };
    }
  }
}

B. Server Tools (Webhooks)

HTTP requests to external APIs. PUT support added Apr 2025.

{
  "name": "get_weather",
  "url": "https://api.weather.com/{{user_id}}",
  "method": "GET",
  "headers": { "Authorization": "Bearer {{secret__api_key}}" },
  "parameters": { "type": "object", "properties": { "city": { "type": "string" } } }
}

⚠️ Secret variables only in headers (not URL/body)

2025 Features:

• transfer-to-human system tool (Apr 2025)
• tool_latency_secs tracking (Apr 2025)

⚠️ Historical Issue (Fixed Feb 2025): Tool calling was broken with

gpt-4o-mini

Source: Changelog Feb 17, 2025

C. MCP Tools (Model Context Protocol)

Connect to MCP servers for databases, IDEs, data sources.

Configuration: Dashboard → Add Custom MCP Server → Configure SSE/HTTP endpoint

Approval Modes: Always Ask | Fine-Grained | No Approval

2025 Updates:

• disable_interruptions flag (Oct 2025) - Prevents interruption during tool execution
• Tools Management Interface (Jun 2025)

⚠️ Limitations: SSE/HTTP only. Not available for Zero Retention or HIPAA.

D. System Tools

Built-in conversation control (no external APIs):

• end_call

  ,

  detect_language

  ,

  transfer_agent

• transfer_to_number

  (telephony)

• dtmf_playpad

  ,

  voicemail_detection

  (telephony)

2025:

use_out_of_band_dtmf

6. SDK Integration

useConversation Hook (React/React Native)

const { startConversation, stopConversation, status, isSpeaking } = useConversation({
  agentId: 'your-agent-id',
  signedUrl: '/api/auth', // OR apiKey: process.env.NEXT_PUBLIC_ELEVENLABS_API_KEY
  clientTools: { /* ... */ },
  onEvent: (event) => { /* transcript, agent_response, tool_call, agent_tool_request (Oct 2025) */ },
  onConnect/onDisconnect/onError,
  serverLocation: 'us' // 'eu-residency' | 'in-residency' | 'global'
});

2025 Events:

• agent_chat_response_part

  - Streaming responses (Oct 2025)

• agent_tool_request

  - Tool interaction tracking (Oct 2025)

Connection Types: WebRTC vs WebSocket

signedUrl

conversationToken

⚠️ WebRTC: Hardcoded PCM_48000, limited device switching

Platforms

• React:

  @elevenlabs/react@0.12.3

• JavaScript:

  @elevenlabs/client@0.12.2

  -

  new Conversation({...})

• React Native:

  @elevenlabs/react-native@0.5.7

  - Expo SDK 47+, iOS/macOS (custom build required, no Expo Go)
• Swift: iOS 14.0+, macOS 11.0+, Swift 5.9+
• Embeddable Widget:

  <script src="https://elevenlabs.io/convai-widget/index.js"></script>

• Widget Packages (Dec 2025):

  • @elevenlabs/convai-widget-embed@0.5.5

    - For embedding in existing apps

  • @elevenlabs/convai-widget-core@0.5.5

    - Core widget functionality

Scribe (Real-Time Speech-to-Text - Beta 2025)

Real-time transcription with word-level timestamps. Single-use tokens, not API keys.

const { connect, startRecording, stopRecording, transcript, partialTranscript } = useScribe({
  token: async () => (await fetch('/api/scribe/token')).json().then(d => d.token),
  commitStrategy: 'vad', // 'vad' (auto on silence) | 'manual' (explicit .commit())
  sampleRate: 16000, // 16000 or 24000
  onPartialTranscript/onFinalTranscript/onError
});

Events: PARTIAL_TRANSCRIPT, FINAL_TRANSCRIPT_WITH_TIMESTAMPS, SESSION_STARTED, ERROR

⚠️ Closed Beta - requires sales contact. For agents, use Agents Platform instead (LLM + TTS + two-way interaction).

⚠️ Webhook Mode Issue: Using

speechToText.convert()

webhook: true

{ request_id }

Error Message:

ParseError: response: Missing required key "language_code"; Missing required key "text"; ...

Workaround - Use direct fetch API instead of SDK:

const formData = new FormData();
formData.append('file', audioFile);
formData.append('model_id', 'scribe_v1');
formData.append('webhook', 'true');
formData.append('webhook_id', webhookId);

const response = await fetch('https://api.elevenlabs.io/v1/speech-to-text', {
  method: 'POST',
  headers: { 'xi-api-key': apiKey },
  body: formData,
});

const result = await response.json(); // { request_id: 'xxx' }
// Actual transcription delivered to webhook endpoint

Source: GitHub Issue #232 (confirmed by maintainer)

7. Testing & Evaluation

🆕 Agent Testing Framework (Aug 2025)

Comprehensive automated testing with 9 new API endpoints for creating, managing, and executing tests.

Test Types:

• Scenario Testing - LLM-based evaluation against success criteria
• Tool Call Testing - Verify correct tool usage and parameters
• Load Testing - High-concurrency capacity testing

CLI Workflow:

# Create test
elevenlabs tests add "Refund Test" --template basic-llm

# Configure in test_configs/refund-test.json
{
  "name": "Refund Test",
  "scenario": "Customer requests refund",
  "success_criteria": ["Agent acknowledges empathetically", "Verifies order details"],
  "expected_tool_call": { "tool_name": "lookup_order", "parameters": { "order_id": "..." } }
}

# Deploy and execute
elevenlabs tests push
elevenlabs agents test "Support Agent"

9 New API Endpoints (Aug 2025):

1. POST /v1/convai/tests

   - Create test

2. GET /v1/convai/tests/:id

   - Retrieve test

3. PATCH /v1/convai/tests/:id

   - Update test

4. DELETE /v1/convai/tests/:id

   - Delete test

5. POST /v1/convai/tests/:id/execute

   - Execute test

6. GET /v1/convai/test-invocations

   - List invocations (pagination, agent filtering)

7. POST /v1/convai/test-invocations/:id/resubmit

   - Resubmit failed test

8. GET /v1/convai/test-results/:id

   - Get results

9. GET /v1/convai/test-results/:id/debug

   - Detailed debugging info

Test Invocation Listing (Oct 2025):

const invocations = await client.convai.testInvocations.list({
  agent_id: 'agent_123',      // Filter by agent
  page_size: 30,              // Default 30, max 100
  cursor: 'next_page_cursor'  // Pagination
});
// Returns: test run counts, pass/fail stats, titles

Programmatic Testing:

const simulation = await client.agents.simulate({
  agent_id: 'agent_123',
  scenario: 'Refund request',
  user_messages: ["I want a refund", "Order #12345"],
  success_criteria: ["Acknowledges request", "Verifies order"]
});
console.log('Passed:', simulation.passed);

Agent Tracking (Oct 2025): Tests now include

agent_id

8. Analytics & Monitoring

2025 Features:

• Custom Dashboard Charts (Apr 2025) - Display evaluation criteria metrics over time
• Call History Filtering (Apr 2025) -

  call_start_before_unix

  parameter
• Multi-Voice History - Separate conversation history by voice
• LLM Cost Tracking - Per agent/conversation costs with

  aggregation_interval

  (hour/day/week/month)
• Tool Latency (Apr 2025) -

  tool_latency_secs

  tracking
• Usage Metrics - minutes_used, request_count, ttfb_avg, ttfb_p95

Conversation Analysis: Success evaluation (LLM-based), data collection fields, post-call webhooks

Access: Dashboard → Analytics | Post-call Webhooks | API

9. Privacy & Compliance

Data Retention: 2 years default (GDPR). Configure:

{ "transcripts": { "retention_days": 730 }, "audio": { "retention_days": 2190 } }

Encryption: TLS 1.3 (transit), AES-256 (rest)

Regional:

serverLocation: 'eu-residency' | 'us' | 'global' | 'in-residency'

Zero Retention Mode: Immediate deletion (no history, analytics, webhooks, or MCP)

Compliance: GDPR (1-2 years), HIPAA (6 years), SOC 2 (automatic encryption)

10. Cost Optimization

LLM Caching: Up to 90% savings on repeated inputs.

{ "caching": { "enabled": true, "ttl_seconds": 3600 } }

Model Swapping: GPT-5.1, GPT-4o/mini, Claude Sonnet 4.5, Gemini 3 Pro/2.5 Flash (2025 models)

Burst Pricing: 3x concurrency limit at 2x cost.

{ "burst_pricing_enabled": true }

11. Advanced Features

2025 Platform Updates:

• Azure OpenAI (Jul 2025) - Custom LLM with Azure-hosted models (requires API version field)
• Genesys Output Variables (Jul 2025) - Enhanced call analytics
• LLMReasoningEffort "none" (Oct 2025) - Control model reasoning behavior
• Streaming Voice Previews (Jul 2025) - Real-time voice generation
• pcm_48000 audio format (Apr 2025) - New output format support

Events:

audio

transcript

agent_response

tool_call

agent_chat_response_part

agent_tool_request

conversation_state

Custom Models: Bring your own LLM (OpenAI-compatible endpoints).

{ "llm_config": { "custom": { "endpoint": "...", "api_key": "{{secret__key}}" } } }

Post-Call Webhooks: HMAC verification required. Return 200 or auto-disable after 10 failures. Payload includes conversation_id, transcript, analysis.

Chat Mode: Text-only (no ASR/TTS).

{ "chat_mode": true }

Telephony: SIP (sip-static.rtc.elevenlabs.io), Twilio native, Vonage, RingCentral. 2025: Twilio keypad fix (Jul), SIP TLS remote_domains validation (Oct)

12. CLI & DevOps ("Agents as Code")

Installation & Auth:

npm install -g @elevenlabs/agents-cli@0.6.1
elevenlabs auth login
elevenlabs auth residency eu-residency  # 'in-residency' | 'global'
export ELEVENLABS_API_KEY=your-api-key  # For CI/CD

Project Structure:

agents.json

tools.json

tests.json

agent_configs/

tool_configs/

test_configs/

Key Commands:

elevenlabs agents init
elevenlabs agents add "Bot" --template customer-service
elevenlabs agents push --env prod --dry-run  # Preview
elevenlabs agents push --env prod            # Deploy
elevenlabs agents pull                       # Import existing
elevenlabs agents test "Bot"                 # 2025: Enhanced testing

elevenlabs tools add-webhook "Weather" --config-path tool_configs/weather.json
elevenlabs tools push

elevenlabs tests add "Test" --template basic-llm
elevenlabs tests push

Multi-Environment: Create

agent.dev.json

agent.staging.json

agent.prod.json

CI/CD: GitHub Actions with

--dry-run

.gitignore:

.env

.elevenlabs/

*.secret.json

13. Common Errors & Solutions (27 Documented)

Error 1: Missing Required Dynamic Variables

Cause: Variables referenced in prompts not provided at conversation start Solution: Provide all variables in

dynamic_variables: { user_name: "John", ... }

Error 2: Case-Sensitive Tool Names

Cause: Tool name mismatch (case-sensitive) Solution: Ensure

tool_ids: ["orderLookup"]

name: "orderLookup"

Error 3: Webhook Authentication Failures

Cause: Incorrect HMAC signature, not returning 200, or 10+ failures Solution: Verify

hmac = crypto.createHmac('sha256', SECRET).update(payload).digest('hex')

ElevenLabs-Signature

X-ElevenLabs-Signature

Error 4: Voice Consistency Issues

Cause: Background noise, inconsistent mic distance, extreme volumes in training Solution: Use clean audio, consistent distance, avoid extremes

Error 5: Wrong Language Voice

Cause: English-trained voice for non-English language Solution: Use language-matched voices:

{ "language": "es", "voice_id": "spanish_voice" }

Error 6: Restricted API Keys Not Supported (CLI)

Cause: CLI doesn't support restricted API keys Solution: Use unrestricted API key for CLI

Error 7: Agent Configuration Push Conflicts

Cause: Hash-based change detection missed modification Solution:

elevenlabs agents init --override

elevenlabs agents pull

Error 8: Tool Parameter Schema Mismatch

Cause: Schema doesn't match usage Solution: Add clear descriptions:

"description": "Order ID (format: ORD-12345)"

Error 9: RAG Index Not Ready

Cause: Index still computing (takes minutes) Solution: Check

index.status === 'ready'

Error 10: WebSocket Protocol Error (1002)

Cause: Network instability, incompatible browser, or firewall issues Symptoms:

Error receiving message: received 1002 (protocol error)
Error sending user audio chunk: received 1002 (protocol error)
WebSocket is already in CLOSING or CLOSED state

Connection cycles: Disconnected → Connected → Disconnected rapidly

Solution:

1. Use WebRTC instead of WebSocket for better stability:

   connectionType: 'webrtc'

2. Implement reconnection logic with exponential backoff
3. Check network stability and firewall rules (port restrictions)
4. Test on different networks/browsers to isolate the issue

Source: GitHub Issue #134

Error 11: 401 Unauthorized in Production

Cause: Agent visibility or API key config Solution: Check visibility (public/private), verify API key in prod, check allowlist

Error 12: Allowlist Connection Errors

Cause: Allowlist enabled but using shared link, OR localhost validation bug Symptoms:

Host is not supported
Host is not valid or supported
Host is not in insights whitelist
WebSocket is already in CLOSING or CLOSED state

Solution:

1. Configure allowlist domains in dashboard or disable for testing
2. Localhost workaround: Use

   127.0.0.1:3000

   instead of

   localhost:3000

⚠️ Localhost Validation Bug: The dashboard has inconsistent validation for localhost URLs:

• ❌

  localhost:3000

  → Rejected (should be valid)
• ❌

  http://localhost:3000

  → Rejected (protocol not allowed)
• ❌

  localhost:3000/voice-chat

  → Rejected (paths not allowed)
• ✅

  www.localhost:3000

  → Accepted (invalid but accepted!)
• ✅

  127.0.0.1:3000

  → Accepted (use this for local dev)

Source: GitHub Issue #320

Error 13: Workflow Infinite Loops

Cause: Edge conditions creating loops Solution: Add max iteration limits, test all paths, explicit exit conditions

Error 14: Burst Pricing Not Enabled

Cause: Burst not enabled in settings Solution:

{ "call_limits": { "burst_pricing_enabled": true } }

Error 15: MCP Server Timeout

Cause: MCP server slow/unreachable Solution: Check URL accessible, verify transport (SSE/HTTP), check auth, monitor logs

Error 16: First Message Cutoff on Android

Cause: Android needs time to switch audio mode Solution:

connectionDelay: { android: 3_000, ios: 0 }

Error 17: CSP (Content Security Policy) Violations

Cause: Strict CSP blocks

blob:

1. cp node_modules/@elevenlabs/client/dist/worklets/*.js public/elevenlabs/

2. Configure:

   workletPaths: { 'rawAudioProcessor': '/elevenlabs/rawAudioProcessor.worklet.js', 'audioConcatProcessor': '/elevenlabs/audioConcatProcessor.worklet.js' }

3. Update CSP:

   script-src 'self' https://elevenlabs.io; worker-src 'self';

   Gotcha: Update worklets when upgrading

   @elevenlabs/client

Error 18: Webhook Payload - Null Message on Tool Calls

Cause: Schema expects

message: string

null

z.string().nullable()

// ❌ Fails on tool call turns:
message: z.string()

// ✅ Correct:
message: z.string().nullable()

Real payload example:

{ "role": "agent", "message": null, "tool_calls": [{ "tool_name": "my_tool", ... }] }

Error 19: Webhook Payload - call_successful is String, Not Boolean

Cause: Schema expects

call_successful: boolean

"success"

"failure"

// Schema:
call_successful: z.union([z.boolean(), z.string()]).optional()

// Conversion helper:
function parseCallSuccessful(value: unknown): boolean | undefined {
  if (value === undefined || value === null) return undefined
  if (typeof value === 'boolean') return value
  if (typeof value === 'string') return value.toLowerCase() === 'success'
  return undefined
}

Error 20: Webhook Schema Validation Fails Silently

Cause: Real ElevenLabs payloads have many undocumented fields that strict schemas reject Undocumented fields in transcript turns:

• agent_metadata

  ,

  multivoice_message

  ,

  llm_override

  ,

  rag_retrieval_info

• llm_usage

  ,

  interrupted

  ,

  original_message

  ,

  source_medium

  Solution: Add all as

  .optional()

  with

  z.any()

  for fields you don't process Debugging tip: Use https://webhook.site to capture real payloads, then test schema locally

Error 21: Webhook Cost Field is Credits, NOT USD

Cause:

metadata.cost

metadata.charging.llm_price

// ❌ Wrong - displays credits as dollars:
cost: metadata?.cost  // Returns 78 (credits)

// ✅ Correct - actual USD cost:
const charging = metadata?.charging as any
cost: charging?.llm_price ?? null  // Returns 0.0036 (USD)

Real payload structure:

{
  "metadata": {
    "cost": 78,  // ← CREDITS, not dollars!
    "charging": {
      "llm_price": 0.0036188999999999995,  // ← Actual USD cost
      "llm_charge": 18,   // LLM credits
      "call_charge": 60,  // Audio credits
      "tier": "pro"
    }
  }
}

Note:

llm_price

Error 22: User Context Available But Not Extracted

Cause: Webhook contains authenticated user info from widget but code doesn't extract it Solution: Extract

dynamic_variables

conversation_initiation_client_data

const dynamicVars = data.conversation_initiation_client_data?.dynamic_variables
const callerName = dynamicVars?.user_name || null
const callerEmail = dynamicVars?.user_email || null
const currentPage = dynamicVars?.current_page || null

Payload example:

{
  "conversation_initiation_client_data": {
    "dynamic_variables": {
      "user_name": "Jeremy Dawes",
      "user_email": "jeremy@jezweb.net",
      "current_page": "/dashboard/calls"
    }
  }
}

Error 23: Data Collection Results Available But Not Displayed

Cause: ElevenLabs agents can collect structured data during calls (configured in agent settings). This data is stored in

analysis.data_collection_results

const dataCollectionResults = analysis?.dataCollectionResults
  ? JSON.parse(analysis.dataCollectionResults)
  : null

// Display each collected field:
Object.entries(dataCollectionResults).forEach(([key, data]) => {
  console.log(`${key}: ${data.value} (${data.rationale})`)
})

Payload example:

{
  "data_collection_results": {
    "customer_name": { "value": "John Smith", "rationale": "Customer stated their name" },
    "intent": { "value": "billing_inquiry", "rationale": "Asking about invoice" },
    "callback_number": { "value": "+61400123456", "rationale": "Provided for callback" }
  }
}

Error 24: Evaluation Criteria Results Available But Not Displayed

Cause: Custom success criteria (configured in agent) produce results in

analysis.evaluation_criteria_results

const evaluationResults = analysis?.evaluationCriteriaResults
  ? JSON.parse(analysis.evaluationCriteriaResults)
  : null

Object.entries(evaluationResults).forEach(([key, data]) => {
  const passed = data.result === 'success' || data.result === true
  console.log(`${key}: ${passed ? 'PASS' : 'FAIL'} - ${data.rationale}`)
})

Payload example:

{
  "evaluation_criteria_results": {
    "verified_identity": { "result": "success", "rationale": "Customer verified DOB" },
    "resolved_issue": { "result": "failure", "rationale": "Escalated to human" }
  }
}

Error 25: Feedback Rating Available But Not Extracted

Cause: User can provide thumbs up/down feedback. Stored in

metadata.feedback.thumb_rating

const feedback = metadata?.feedback as any
const feedbackRating = feedback?.thumb_rating ?? null  // 1, -1, or null

// Also available:
const likes = feedback?.likes    // Array of things user liked
const dislikes = feedback?.dislikes  // Array of things user disliked

Payload example:

{
  "metadata": {
    "feedback": {
      "thumb_rating": 1,
      "likes": ["helpful", "natural"],
      "dislikes": []
    }
  }
}

Error 26: Per-Turn Metadata Not Extracted (interrupted, source_medium, rag_retrieval_info)

Cause: Each transcript turn has valuable metadata that's often ignored Solution: Store these fields per message for analytics and debugging

const turnAny = turn as any
const messageData = {
  // ... existing fields
  interrupted: turnAny.interrupted ?? null,          // Was turn cut off by user?
  sourceMedium: turnAny.source_medium ?? null,       // Channel: web, phone, etc.
  originalMessage: turnAny.original_message ?? null, // Pre-processed message
  ragRetrievalInfo: turnAny.rag_retrieval_info       // What knowledge was retrieved
    ? JSON.stringify(turnAny.rag_retrieval_info)
    : null,
}

Use cases:

• interrupted: true

  → User spoke over agent (UX insight)

• source_medium

  → Analytics by channel

• rag_retrieval_info

  → Debug/improve knowledge base retrieval

Error 27: Upcoming Audio Flags (August 2025)

Cause: Three new boolean fields coming in August 2025 webhooks that may break schemas Solution: Add these fields to schemas now (as optional) to be ready

// In webhook payload (coming August 15, 2025):
has_audio: boolean        // Was full audio recorded?
has_user_audio: boolean   // Was user audio captured?
has_response_audio: boolean // Was agent audio captured?

// Schema (future-proof):
const schema = z.object({
  // ... existing fields
  has_audio: z.boolean().optional(),
  has_user_audio: z.boolean().optional(),
  has_response_audio: z.boolean().optional(),
})

Note: These match the existing fields in the GET Conversation API response

Error 28: Tool Parsing Fails When Tool Not Found

Cause: Calling

conversations.get(id)

Error: response -> transcript -> [11] -> tool_results -> [0] -> type:
Expected string. Received null.;
response -> transcript -> [11] -> tool_results -> [0] -> type:
[Variant 1] Expected "system". Received null.;
response -> transcript -> [11] -> tool_results -> [0] -> type:
[Variant 2] Expected "workflow". Received null.

Solution:

1. SDK fix needed - SDK should handle null tool_results.type gracefully
2. Workaround for users:
   • Ensure all referenced tools exist before deleting them
   • Wrap

     conversation.get()

     in try-catch until SDK is fixed

   try {
     const conversation = await client.conversationalAi.conversations.get(id);
   } catch (error) {
     console.error('Tool parsing error - conversation may reference deleted tools');
   }
   

Source: GitHub Issue #268

Error 29: SDK Parameter Naming Confusion (snake_case vs camelCase)

Cause: Using snake_case parameters (from API/Python SDK docs) in JS SDK, which expects camelCase Symptoms: Parameters silently ignored, wrong model/voice used, no error messages

Common Mistakes:

// ❌ WRONG - parameter ignored:
convert(voiceId, { model_id: "eleven_v3" })

// ✅ CORRECT:
convert(voiceId, { modelId: "eleven_v3" })

Solution: Always use camelCase for JS SDK parameters. Check TypeScript types if unsure.

Affected Parameters:

model_id

voice_id

output_format

voice_settings

Source: GitHub Issue #300

Error 30: Webhook Mode ParseError with speechToText.convert()

Cause: SDK expects full transcription response but webhook mode returns only

{ request_id }

ParseError: Missing required key "language_code"; Missing required key "text"; ...

Solution: Use direct fetch API instead of SDK for webhook mode:

const formData = new FormData();
formData.append('file', audioFile);
formData.append('model_id', 'scribe_v1');
formData.append('webhook', 'true');
formData.append('webhook_id', webhookId);

const response = await fetch('https://api.elevenlabs.io/v1/speech-to-text', {
  method: 'POST',
  headers: { 'xi-api-key': apiKey },
  body: formData,
});

const result = await response.json(); // { request_id: 'xxx' }

Source: GitHub Issue #232

Error 31: Package Not Compatible with Browser/Web

Cause: Using

@elevenlabs/elevenlabs-js

child_process

Module not found: Can't resolve 'child_process'

Affected Frameworks:

• Next.js client components
• Vite browser builds
• Electron renderer process
• Tauri webview

Solution:

1. For browser/web: Use

   @elevenlabs/client

   or

   @elevenlabs/react

   instead
2. For full API access in browser: Create proxy server endpoint using

   elevenlabs-js

   , call from browser
3. For Electron/Tauri: Use

   elevenlabs-js

   in main process only, not renderer

Note: This is by design -

elevenlabs-js

Source: GitHub Issue #293

Error 32: prompt.tools Deprecated - POST/PATCH Rejected

Cause: Using legacy

prompt.tools

A request must include either prompt.tool_ids or the legacy prompt.tools array — never both

Solution: Migrate to new format:

// ❌ Old (rejected):
{ agent: { prompt: { tools: [...] } } }

// ✅ New (required):
{
  agent: {
    prompt: {
      tool_ids: ["tool_abc123"],         // Client/server tools
      built_in_tools: ["end_call"]       // System tools
    }
  }
}

Note: All legacy tools were auto-migrated to standalone records. Just update your configuration references.

Source: Official Migration Guide

Error 33: GPT-4o Mini Tool Calling Broken (Fixed Feb 2025)

Cause: OpenAI API breaking change affected

gpt-4o-mini

gpt-4o-mini

Source: Changelog Feb 17, 2025

Error 34: Scribe Audio Format Parameter Not Transmitted (Fixed v2.32.0)

Cause: WebSocket URI wasn't including

audio_format

@elevenlabs/elevenlabs-js@2.32.0

Source: GitHub PR #319

14. Agent Versioning (Jan 2026)

ElevenLabs introduced Agent Versioning in January 2026, enabling git-like version control for conversational AI agents. This allows safe experimentation, A/B testing, and gradual rollouts.

Core Concepts

agtvrsn_xxxx

agtbrch_xxxx

Enabling Versioning

// Enable versioning on existing agent
const agent = await client.conversationalAi.agents.update({
  agentId: 'your-agent-id',
  enableVersioningIfNotEnabled: true
});

⚠️ Note: Once enabled, versioning cannot be disabled on an agent.

Branch Management

// Create a new branch for experimentation
const branch = await client.conversationalAi.agents.branches.create({
  agentId: 'your-agent-id',
  parentVersionId: 'agtvrsn_xxxx',  // Branch from this version
  name: 'experiment-v2'
});

// List all branches
const branches = await client.conversationalAi.agents.branches.list({
  agentId: 'your-agent-id'
});

// Delete a branch (must not have active traffic)
await client.conversationalAi.agents.branches.delete({
  agentId: 'your-agent-id',
  branchId: 'agtbrch_xxxx'
});

Traffic Deployment (A/B Testing)

Route traffic between branches using percentage splits:

// Deploy 90/10 traffic split
const deployment = await client.conversationalAi.agents.deployments.create({
  agentId: 'your-agent-id',
  deployments: [
    { branchId: 'agtbrch_main', percentage: 90 },
    { branchId: 'agtbrch_xxxx', percentage: 10 }
  ]
});

// Get current deployment status
const status = await client.conversationalAi.agents.deployments.get({
  agentId: 'your-agent-id'
});

Use Cases:

• A/B Testing - Test new prompts on 10% of traffic before full rollout
• Gradual Rollouts - Increase traffic incrementally (10% → 25% → 50% → 100%)
• Quick Rollback - Route 100% back to stable branch if issues detected

Merging Branches

// Merge successful experiment back to main
const merge = await client.conversationalAi.agents.branches.merge({
  agentId: 'your-agent-id',
  sourceBranchId: 'agtbrch_xxxx',
  targetBranchId: 'agtbrch_main',
  archiveSourceBranch: true  // Clean up after merge
});

Working with Drafts

Drafts are per-user, per-branch work-in-progress states:

// Get current draft
const draft = await client.conversationalAi.agents.drafts.get({
  agentId: 'your-agent-id',
  branchId: 'agtbrch_xxxx'
});

// Update draft (changes not yet committed)
await client.conversationalAi.agents.drafts.update({
  agentId: 'your-agent-id',
  branchId: 'agtbrch_xxxx',
  conversationConfig: {
    agent: { prompt: { prompt: 'Updated system prompt...' } }
  }
});

// Commit draft to create new version
const version = await client.conversationalAi.agents.drafts.commit({
  agentId: 'your-agent-id',
  branchId: 'agtbrch_xxxx',
  message: 'Improved greeting flow'
});

Best Practices

1. Always test on branch first - Never experiment directly on production traffic
2. Use descriptive branch names -

   feature-multilang

   ,

   fix-timeout-handling

3. Start with small traffic splits - Begin at 5-10%, monitor metrics, then increase
4. Archive merged branches - Keep repository clean
5. Commit messages - Use clear messages for version history

Source: Agent Versioning Docs

15. MCP Security & Guardrails

When connecting MCP (Model Context Protocol) servers to ElevenLabs agents, security is critical. MCP tools can access databases, APIs, and sensitive data.

Tool Approval Modes

Configuration:

{
  "mcp_config": {
    "server_url": "https://your-mcp-server.com",
    "approval_mode": "always_ask",  // 'always_ask' | 'fine_grained' | 'no_approval'
    "fine_grained_rules": [
      { "tool_name": "read_*", "auto_approve": true },
      { "tool_name": "write_*", "auto_approve": false },
      { "tool_name": "delete_*", "auto_approve": false }
    ]
  }
}

Security Best Practices

1. Vet MCP Servers

• Only connect servers from trusted sources
• Review server code/documentation before connecting
• Prefer official/verified MCP implementations

2. Limit Data Exposure

• Minimize PII shared with MCP servers
• Use scoped API keys with minimum required permissions
• Never pass full database access - use read-only views

3. Network Security

• Always use HTTPS endpoints
• Implement proper authentication (API keys, OAuth)
• Use

  {{secret__xxx}}

  variables for credentials (never in prompts)

4. Prompt Injection Prevention

• Add guardrails in agent prompts against injection attacks
• Validate and sanitize MCP tool inputs
• Monitor for unusual tool usage patterns

5. Monitoring & Audit

• Log all MCP tool executions
• Review approval patterns regularly
• Set up alerts for sensitive operations

Guardrails Configuration

Add protective instructions to your agent prompt:

{
  "agent": {
    "prompt": {
      "prompt": `...

SECURITY GUARDRAILS:
- Never execute database delete operations without explicit user confirmation
- Never expose raw API keys or credentials in responses
- If a tool request seems unusual or potentially harmful, ask for clarification
- Do not combine sensitive operations (read PII + external API call) in single turn
- Report any suspicious requests to administrators
      `
    }
  }
}

MCP Limitations

Not Available With:

• Zero Retention mode (no logging = no MCP)
• HIPAA compliance mode
• Certain regional deployments

Transport: SSE/HTTP only (no stdio MCP servers)

Source: MCP Safety Docs

Integration with Existing Skills

This skill composes well with:

• cloudflare-worker-base → Deploy agents on Cloudflare Workers edge network
• cloudflare-workers-ai → Use Cloudflare LLMs as custom models in agents
• cloudflare-durable-objects → Persistent conversation state and session management
• cloudflare-kv → Cache agent configurations and user preferences
• nextjs → React SDK integration in Next.js applications
• ai-sdk-core → Vercel AI SDK provider for unified AI interface
• clerk-auth → Authenticated voice sessions with user identity
• hono-routing → API routes for webhooks and server tools

Additional Resources

Official Documentation:

• Platform Overview: https://elevenlabs.io/docs/agents-platform/overview
• API Reference: https://elevenlabs.io/docs/api-reference
• CLI GitHub: https://github.com/elevenlabs/cli

Examples:

• Official Examples: https://github.com/elevenlabs/elevenlabs-examples
• MCP Server: https://github.com/elevenlabs/elevenlabs-mcp

Community:

• Discord: https://discord.com/invite/elevenlabs
• Twitter: @elevenlabsio

Production Tested: WordPress Auditor, Customer Support Agents, AgentFlow (webhook integration) Last Updated: 2026-01-27 Package Versions: elevenlabs@1.59.0, @elevenlabs/elevenlabs-js@2.32.0, @elevenlabs/agents-cli@0.6.1, @elevenlabs/react@0.12.3, @elevenlabs/client@0.12.2, @elevenlabs/react-native@0.5.7 Changes: Added Agent Versioning (Jan 2026) section covering versions, branches, traffic deployment, drafts, and A/B testing. Added MCP Security & Guardrails section covering tool approval modes, security best practices, and prompt injection prevention.