agents

name agents description Build voice AI agents with ElevenLabs. Use when creating voice assistants, customer service bots, interactive voice characters, or any real-time voice conversation experience, and when configuring an agent's tools, workflows, or procedures, including creating, editing, compiling, and publishing procedure drafts on an agent branch over the SDKs or REST API. license MIT compatibility Requires internet access and an ElevenLabs API key (ELEVENLABS_API_KEY). metadata {"openclaw":{"requires":{"env":"[Truncated]"},"primaryEnv":"ELEVENLABS_API_KEY"}} 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" , 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" , 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" } } }); CLI The CLI reads ELEVENLABS_API_KEY from the environment automatically: elevenlabs agents create \ --json '{"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 Authenticated WebRTC: Request a session token from your backend. The response includes both the token and the conversation ID: session = client.conversational_ai.conversations.get_webrtc_token( agent_id= "your-agent-id" , ) print (session.token, session.conversation_id) 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" , overrides : { asr : { keywords : [ "ElevenLabs" , "TechCorp" ] } }, onMessage : ( msg ) => console . log ( "Agent:" , msg. message ), onUserTranscript : ( t ) => console . log ( "User:" , t. message ), onPing : ( event ) => console . log ( "Estimated latency:" , event. ping_ms ), onContextUsage : ( { model, context_tokens, context_limit_tokens } ) => console . log ( ` ${model} : ${context_tokens} / ${context_limit_tokens} context tokens` ), onError : ( e ) => console . error (e) }); React Hook: Wrap hook consumers in ConversationProvider . Prefer granular hooks such as useConversationControls and useConversationStatus for session controls and UI state; useConversation remains available as the convenience all-in-one hook. Pass provider-level callbacks such as onError when you want React to handle conversation errors in one place. import { ConversationProvider , useConversationControls, useConversationStatus, } from "@elevenlabs/react" ; function Agent ( { signedUrl }: { signedUrl: string } ) { const { startSession, endSession } = useConversationControls (); const { status } = useConversationStatus (); if (status === "connected" ) { return < button onClick = {endSession} > End conversation </ button > ; } return ( < button onClick = {() => startSession({ signedUrl })}> Start conversation </ button > ); } function App ( { signedUrl }: { signedUrl: string } ) { return ( < ConversationProvider onError = {(error) => console.error("Conversation error:", error)} onPing={(event) => console.log("Estimated latency:", event.ping_ms)} onContextUsage={({ model, context_tokens, context_limit_tokens }) => console.log(`${model}: ${context_tokens}/${context_limit_tokens} context tokens`) } > < Agent signedUrl = {signedUrl} /> </ ConversationProvider > ); } Configuration Provider Models OpenAI gpt-5.6-sol , gpt-5.6-terra , gpt-5.6-luna , gpt-5.5 , gpt-5.5-2026-04-23 , gpt-5.4 , gpt-5.4-mini , gpt-5.4-nano , gpt-5.4-2026-03-05 , gpt-5.4-mini-2026-03-17 , gpt-5.4-nano-2026-03-17 , 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-opus-4-7 , 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-3.7-flash , gemini-3.6-flash , 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 , qwen36-35b-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" }]}, "start_procedure" : {} } } 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 start_procedure Procedure-guided conversations (see Procedures ) end_procedure Completing active procedures skip_turn Tutoring / coaching (silent listening) voicemail_detection Outbound calling play_keypad_touch_tone IVR navigation run_subagent is a system tool for delegating a task to another configured agent. Add it to conversation_config.agent.prompt.tools with params.system_tool_type: "run_subagent" and an agents array. Each entry requires agent_id and description ; branch_id and a JSON-schema parameters object are optional. knowledge_base is a system tool for letting the model choose how to inspect attached knowledge. Add it to conversation_config.agent.prompt.tools with type: "system" , a name , and params.system_tool_type: "knowledge_base" . Use enabled_strategies to expose any combination of cat , keyword , semantic , and ls : { "type" : "system" , "name" : "knowledge_base" , "description" : "Search the attached knowledge base." , "params" : { "system_tool_type" : "knowledge_base" , "enabled_strategies" : [ "semantic" , "keyword" ] } } 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