elevenlabs-agents

仓库: jezweb/claude-skills
安装量: 679
排名: #1731

安装

npx skills add https://github.com/jezweb/claude-skills --skill elevenlabs-agents

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:

ASR (Automatic Speech Recognition) - Converts speech to text (32+ languages, sub-second latency) LLM (Large Language Model) - Reasoning and response generation (GPT, Claude, Gemini, custom models) TTS (Text-to-Speech) - Converts text to speech (5000+ voices, 31 languages, low latency) 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 (uninstall if present)

⚠️ 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?

Package Environment Use Case @elevenlabs/elevenlabs-js Server only (Node.js) Full API access, TTS, voices, models @elevenlabs/client Browser + Server Agents SDK, WebSocket, lightweight @elevenlabs/react React apps Conversational AI hooks @elevenlabs/react-native Mobile iOS/Android agents

⚠️ 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" } } });

  1. 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:

API/Python (snake_case) JS SDK (camelCase) 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

  1. Agent Configuration System Prompt Architecture (6 Components)

  2. 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 Mode Behavior Best For Eager Responds quickly Fast-paced support, quick orders Normal Balanced (default) General customer service Patient Waits longer Information collection, therapy { "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}} syntax in prompts, messages, and tool parameters.

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}} (headers only, never sent to LLM)

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

  1. 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!" } ] }

  1. Knowledge Base & RAG

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

Workflow:

Upload documents (PDF, TXT, DOCX) Compute RAG index (vector embeddings) 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.

  1. Tools (4 Types) ⚠️ BREAKING CHANGE: prompt.tools Deprecated (July 2025)

The legacy prompt.tools array was removed on July 23, 2025. All agent configurations must use the new format.

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 due to an OpenAI API change. This was fixed in SDK v2.25.0+ (Feb 17, 2025). If using older SDK versions, upgrade to avoid silent tool execution failures on that model.

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 flag for telephony integration

  1. 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 Feature WebSocket WebRTC (Jul 2025 rollout) Auth signedUrl conversationToken Audio Configurable (16k/24k/48k) PCM_48000 (hardcoded) Latency Standard Lower Best For Flexibility Low-latency

⚠️ 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: 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() with webhook: true causes SDK parsing errors. The API returns only { request_id } for webhook mode, but the SDK expects the full transcription schema.

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)

  1. 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):

POST /v1/convai/tests - Create test GET /v1/convai/tests/:id - Retrieve test PATCH /v1/convai/tests/:id - Update test DELETE /v1/convai/tests/:id - Delete test POST /v1/convai/tests/:id/execute - Execute test GET /v1/convai/test-invocations - List invocations (pagination, agent filtering) POST /v1/convai/test-invocations/:id/resubmit - Resubmit failed test GET /v1/convai/test-results/:id - Get results 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 association for better organization

  1. 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

  1. 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)

  1. 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 }

  1. 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 (streaming, Oct 2025), agent_tool_request (Oct 2025), 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 }. Saves ~200ms + costs.

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

  1. 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 for overrides

CI/CD: GitHub Actions with --dry-run validation before deploy

.gitignore: .env, .elevenlabs/, *.secret.json

  1. 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"] matches name: "orderLookup" exactly

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') and return 200 ⚠️ Header Name: Use ElevenLabs-Signature (NOT X-ElevenLabs-Signature - no X- prefix!)

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 + push

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' before using

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:

Use WebRTC instead of WebSocket for better stability: connectionType: 'webrtc' Implement reconnection logic with exponential backoff Check network stability and firewall rules (port restrictions) 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:

Configure allowlist domains in dashboard or disable for testing 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 } (3s for audio routing)

Error 17: CSP (Content Security Policy) Violations

Cause: Strict CSP blocks blob: URLs. SDK uses Audio Worklets loaded as blobs Solution: Self-host worklets:

cp node_modules/@elevenlabs/client/dist/worklets/*.js public/elevenlabs/ Configure: workletPaths: { 'rawAudioProcessor': '/elevenlabs/rawAudioProcessor.worklet.js', 'audioConcatProcessor': '/elevenlabs/audioConcatProcessor.worklet.js' } 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 but ElevenLabs sends null when agent makes tool calls Solution: Use z.string().nullable() for message field in Zod schemas

// ❌ 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 but ElevenLabs sends "success" or "failure" strings Solution: Accept both types and convert for database storage

// 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 contains ElevenLabs credits, not USD dollars. Displaying this directly shows wildly wrong values (e.g., "$78.0000" when actual cost is ~$0.003) Solution: Extract actual USD from metadata.charging.llm_price instead

// ❌ 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 only covers LLM costs. Audio costs may require separate calculation based on your plan.

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 from 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 but often not parsed/displayed in UI. Solution: Parse the JSON and display collected fields with their values and rationales

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 but often not parsed/displayed Solution: Parse and show pass/fail status with rationales

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 but not extracted Solution: Extract and store the rating (1 = thumbs up, -1 = thumbs down)

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) when conversation contains tool_results where the tool was deleted/not found Error Message:

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:

SDK fix needed - SDK should handle null tool_results.type gracefully 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, and all API parameters

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 } Error Message:

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 in browser/client environments (depends on Node.js child_process) Error Message:

Module not found: Can't resolve 'child_process'

Affected Frameworks:

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

Solution:

For browser/web: Use @elevenlabs/client or @elevenlabs/react instead For full API access in browser: Create proxy server endpoint using elevenlabs-js, call from browser For Electron/Tauri: Use elevenlabs-js in main process only, not renderer

Note: This is by design - elevenlabs-js is server-only

Source: GitHub Issue #293

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

Cause: Using legacy prompt.tools array field after July 23, 2025 cutoff Error Message:

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 tool execution (historical issue) Symptoms: Tools silently fail to execute, no error messages Solution: Upgrade to SDK v2.25.0+ (released Feb 17, 2025). If using older SDK versions, upgrade or avoid gpt-4o-mini for tool-based workflows.

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 parameter even when specified (historical issue) Solution: Upgrade to @elevenlabs/elevenlabs-js@2.32.0 or later (released Jan 19, 2026)

Source: GitHub PR #319

  1. 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 Concept ID Format Description Version agtvrsn_xxxx Immutable snapshot of agent config at a point in time Branch agtbrch_xxxx Isolated development path (like git branches) Draft Per-user/branch Work-in-progress changes before committing Deployment Traffic splits A/B testing with percentage-based routing 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 Always test on branch first - Never experiment directly on production traffic Use descriptive branch names - feature-multilang, fix-timeout-handling Start with small traffic splits - Begin at 5-10%, monitor metrics, then increase Archive merged branches - Keep repository clean Commit messages - Use clear messages for version history

Source: Agent Versioning Docs

  1. 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 Mode Behavior Use When Always Ask Explicit approval for every tool execution Default - recommended for most cases Fine-Grained Auto-approve trusted ops, require approval for sensitive Established, trusted MCP servers No Approval All tool executions auto-approved Only thoroughly vetted, internal servers

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

  1. 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

  1. Network Security

Always use HTTPS endpoints Implement proper authentication (API keys, OAuth) Use {{secret__xxx}} variables for credentials (never in prompts)

  1. Prompt Injection Prevention

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

  1. 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.

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