API安装
- 作者仓库星标 3,924
- 许可证 MIT
- 作者更新于 实时读取
- 作者仓库 OpenMontage
- 领域
- AI 智能
- 兼容 Agent
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- 信任分
- 94 / 100 · 已通过审计
- 作者 / 版本 / 许可
- @calesthio · MIT
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 需要 · Vendor-specific
- 兼容的系统
- macOS · Linux · Windows
- 底层运行要求
- Python
- 文件与系统权限
-
- 只读
- 允许写入 / 修改
- 读取环境变量
- 网络行为
- 允许外网请求
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: agents
description: Build voice AI agents with ElevenLabs. Use when creating voice assistants, customer service bots…
category: AI 智能
runtime: Python
---
# agents 输出预览
## PART A: 任务判断
- 适用问题:提示词、Agent 工作流、模型评估或自动化推理。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“Quick Start with CLI / Python / JavaScript”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于提示词、Agent 工作流、模型评估或自动化推理,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“Quick Start with CLI / Python / JavaScript”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、读取环境变量、会按任务需要访问外部网络、需要准备 Vendor-specific API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件、读取环境变量;会按任务需要访问外部网络;需要准备 Vendor-specific API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文出现了 `/v1`、`/products` 这类斜杠命令;如果你的 Agent 支持命令触发,优先用命令开场,再补充目标和边界。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件、读取环境变量。
先用一个小任务确认它会围绕“Quick Start with CLI / Python / JavaScript”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: agents
description: Build voice AI agents with ElevenLabs. Use when creating voice assistants, customer service bots…
category: AI 智能
source: calesthio/OpenMontage
---
# agents
## 什么时候使用
- 把 AI / Agent方向的常用动作沉淀成 Agent 可调用的技能 适合处理AI Agent、提示词、模型评估与自动化推理,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查…
- 面向提示词、Agent 工作流、模型评估或自动化推理,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Quick Start with CLI / Python / JavaScript」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件、读取环境变量;会按任务需要访问外部网络;需要准备 Vendor-specific API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "agents" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Quick Start with CLI / Python / JavaScript
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> Python | 读取文件、写入/修改文件、读取环境变量 | 会按任务需要访问外部网络
安全层 -> 需要准备 Vendor-specific API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} ElevenLabs Agents Platform
Build voice AI agents with natural conversations, multiple LLM providers, custom tools, and easy web embedding.
Setup: See Installation Guide for CLI and SDK setup.
Quick Start with CLI
The ElevenLabs CLI is the recommended way to create and manage agents:
# Install CLI and authenticate
npm install -g @elevenlabs/cli
elevenlabs auth login
# Initialize project and create an agent
elevenlabs agents init
elevenlabs agents add "My Assistant" --template complete
# Push to ElevenLabs platform
elevenlabs agents push
Available templates: complete, minimal, voice-only, text-only, customer-service, assistant
Python
from elevenlabs import ElevenLabs
client = ElevenLabs()
agent = client.conversational_ai.agents.create(
name="My Assistant",
enable_versioning=True,
conversation_config={
"agent": {
"first_message": "Hello! How can I help?",
"language": "en",
"prompt": {
"prompt": "You are a helpful assistant. Be concise and friendly.",
"llm": "gemini-2.0-flash",
"temperature": 0.7
}
},
"tts": {"voice_id": "JBFqnCBsd6RMkjVDRZzb"}
}
)
JavaScript
import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js";
const client = new ElevenLabsClient();
const agent = await client.conversationalAi.agents.create({
name: "My Assistant",
enableVersioning: true,
conversationConfig: {
agent: {
firstMessage: "Hello! How can I help?",
language: "en",
prompt: {
prompt: "You are a helpful assistant.",
llm: "gemini-2.0-flash",
temperature: 0.7
}
},
tts: { voiceId: "JBFqnCBsd6RMkjVDRZzb" }
}
});
cURL
curl -X POST "https://api.elevenlabs.io/v1/convai/agents/create?enable_versioning=true" \
-H "xi-api-key: $ELEVENLABS_API_KEY" -H "Content-Type: application/json" \
-d '{"name": "My Assistant", "conversation_config": {"agent": {"first_message": "Hello!", "language": "en", "prompt": {"prompt": "You are helpful.", "llm": "gemini-2.0-flash"}}, "tts": {"voice_id": "JBFqnCBsd6RMkjVDRZzb"}}}'
Starting Conversations
Server-side (Python): Get signed URL for client connection:
signed_url = client.conversational_ai.conversations.get_signed_url(
agent_id="your-agent-id",
environment="staging",
)
Client-side (JavaScript):
import { Conversation } from "@elevenlabs/client";
const conversation = await Conversation.startSession({
agentId: "your-agent-id",
environment: "staging",
onMessage: (msg) => console.log("Agent:", msg.message),
onUserTranscript: (t) => console.log("User:", t.message),
onError: (e) => console.error(e)
});
React Hook:
import { useConversation } from "@elevenlabs/react";
const conversation = useConversation({ onMessage: (msg) => console.log(msg) });
// Get a signed URL for the target environment from your backend, then:
await conversation.startSession({ signedUrl: token });
Configuration
| Provider | Models |
|---|---|
| OpenAI | gpt-5, gpt-5-mini, gpt-5-nano, gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-4o, gpt-4o-mini, gpt-4-turbo |
| Anthropic | claude-sonnet-4-6, claude-sonnet-4-5, claude-sonnet-4, claude-haiku-4-5, claude-3-7-sonnet, claude-3-5-sonnet, claude-3-haiku |
gemini-3.1-flash-lite-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, 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, and capability flags such as image-input 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.
Tools
Extend agents with webhook, client, or built-in system tools. Tools are defined inside conversation_config.agent.prompt:
Workspace environment variables can resolve per-environment server tool URLs, headers, and auth connections, and runtime system variables such as {{system__conversation_history}} can pass full conversation context into tool calls when needed.
"prompt": {
"prompt": "You are a helpful assistant that can check the weather.",
"llm": "gemini-2.0-flash",
"tools": [
# Webhook: server-side API call
{"type": "webhook", "name": "get_weather", "description": "Get weather",
"api_schema": {"url": "https://api.example.com/weather", "method": "POST",
"request_body_schema": {"type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"]}}},
# Client: runs in the browser
{"type": "client", "name": "show_product", "description": "Display a product",
"parameters": {"type": "object", "properties": {"productId": {"type": "string"}}, "required": ["productId"]}}
],
"built_in_tools": {
"end_call": {},
"transfer_to_number": {"transfers": [{"transfer_destination": {"type": "phone", "phone_number": "+1234567890"}, "condition": "User asks for human support"}]}
}
}
Client tools run in browser:
clientTools: {
show_product: async ({ productId }) => {
document.getElementById("product").src = `/products/${productId}`;
return { success: true };
}
}
See Client Tools Reference for complete documentation.
Widget Embedding
<elevenlabs-convai agent-id="your-agent-id"></elevenlabs-convai>
<script src="https://unpkg.com/@elevenlabs/convai-widget-embed" async type="text/javascript"></script>
Customize with attributes: avatar-image-url, action-text, start-call-text, end-call-text.
See Widget Embedding Reference for all options.
Outbound Calls
Make outbound phone calls using your agent via Twilio integration:
Python
response = client.conversational_ai.twilio.outbound_call(
agent_id="your-agent-id",
agent_phone_number_id="your-phone-number-id",
to_number="+1234567890",
call_recording_enabled=True
)
print(f"Call initiated: {response.conversation_id}")
JavaScript
const response = await client.conversationalAi.twilio.outboundCall({
agentId: "your-agent-id",
agentPhoneNumberId: "your-phone-number-id",
toNumber: "+1234567890",
callRecordingEnabled: true,
});
cURL
curl -X POST "https://api.elevenlabs.io/v1/convai/twilio/outbound-call" \
-H "xi-api-key: $ELEVENLABS_API_KEY" -H "Content-Type: application/json" \
-d '{"agent_id": "your-agent-id", "agent_phone_number_id": "your-phone-number-id", "to_number": "+1234567890", "call_recording_enabled": true}'
See Outbound Calls Reference for configuration overrides and dynamic variables.
Managing Agents
Using CLI (Recommended)
# List agents and check status
elevenlabs agents list
elevenlabs agents status
# Import agents from platform to local config
elevenlabs agents pull # Import all agents
elevenlabs agents pull --agent <agent-id> # Import specific agent
# Push local changes to platform
elevenlabs agents push # Upload configurations
elevenlabs agents push --dry-run # Preview changes first
# Add tools
elevenlabs tools add-webhook "Weather API"
elevenlabs tools add-client "UI Tool"
Project Structure
The CLI creates a project structure for managing agents:
your_project/
├── agents.json # Agent definitions
├── tools.json # Tool configurations
├── tests.json # Test configurations
├── agent_configs/ # Individual agent configs
├── tool_configs/ # Individual tool configs
└── test_configs/ # Individual test configs
SDK Examples
# List
agents = client.conversational_ai.agents.list()
# Get
agent = client.conversational_ai.agents.get(agent_id="your-agent-id")
# Update (partial - only include fields to change)
client.conversational_ai.agents.update(agent_id="your-agent-id", name="New Name")
client.conversational_ai.agents.update(agent_id="your-agent-id",
conversation_config={
"agent": {"prompt": {"prompt": "New instructions", "llm": "claude-sonnet-4"}}
})
# Delete
client.conversational_ai.agents.delete(agent_id="your-agent-id")
See Agent Configuration for all configuration options and SDK examples.
Error Handling
try:
agent = client.conversational_ai.agents.create(...)
except Exception as e:
print(f"API error: {e}")
Common errors: 401 (invalid key), 404 (not found), 422 (invalid config), 429 (rate limit)
References
- Installation Guide - SDK setup and migration
- Agent Configuration - All config options and CRUD examples
- Client Tools - Webhook, client, and system tools
- Widget Embedding - Website integration
- Outbound Calls - Twilio phone call integration
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核