Phone Call Agent

Phone Call Agent

An open-source, self-hosted AI voice call agent. Give it a phone number and a task — it calls the person, has a natural conversation, and returns the full transcript and outcome back to you.

Works in two modes:

• Outbound — agent dials a real phone number via SIP, or generates a browser call link
• Inbound — agent answers incoming calls from your SIP trunk

Quick Start

git clone https://github.com/Littlesheepxy/phone-call-agent
cd phone-call-agent
cp .env.example .env
# Fill in .env with your API keys (see Configuration below)
docker compose up

Services started:

Public URL (needed for share links to work on other devices)

# No account needed — temporary URL, good for testing:
docker compose --profile quick-tunnel up quick-tunnel

# Permanent URL (free Cloudflare account):
# Set CLOUDFLARE_TOKEN= in .env, then:
docker compose --profile tunnel up tunnel

Configuration (.env)

# LiveKit (included in docker compose — defaults work out of the box)
LIVEKIT_URL=ws://localhost:7880
LIVEKIT_API_KEY=devkey
LIVEKIT_API_SECRET=secret

# LLM — pick one
LLM_PROVIDER=openai
LLM_API_KEY=sk-...
LLM_MODEL=gpt-4o-mini

# STT (speech-to-text)
ASR_PROVIDER=openai_whisper   # or: volcengine (lower latency for Chinese)

# TTS (text-to-speech)
TTS_PROVIDER=openai           # or: volcengine
TTS_VOICE=alloy

# Volcengine / Doubao (recommended for Chinese — lower latency, better quality)
VOLCENGINE_LLM_API_KEY=...
VOLCENGINE_LLM_MODEL=doubao-pro-32k-241215
VOLCENGINE_ASR_APP_ID=...
VOLCENGINE_ASR_TOKEN=...
VOLCENGINE_TTS_APP_ID=...
VOLCENGINE_TTS_TOKEN=...

# SIP trunk (only needed for real outbound phone calls)
SIP_OUTBOUND_TRUNK_ID=...     # LiveKit SIP trunk ID

# Optional: called with transcript + summary JSON when each call ends
WEBHOOK_URL=https://your-app.com/call-webhook

# Public base URL for share links (set to your tunnel/domain)
PUBLIC_URL=https://your-tunnel.trycloudflare.com

MCP Server Setup

After docker compose up, connect the MCP server to Claude Desktop:

~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

{
  "mcpServers": {
    "phone-call-agent": {
      "command": "python",
      "args": ["-m", "backend.mcp_server"],
      "cwd": "/path/to/phone-call-agent",
      "env": {
        "AGENT_API_URL": "http://localhost:8001"
      }
    }
  }
}

Install the MCP dependency first:

cd phone-call-agent && pip install mcp>=1.0.0

MCP Tools

Once connected, Claude has access to these tools:

list_skills

List all available call skills (loaded from skills/*.md).

→ follow_up: 跟进待处理事项 (outbound)
→ appointment_reminder: 提醒联系人即将到来的预约 (outbound)
→ customer_survey: 服务后进行简短满意度调查 (outbound)

create_share_link(skill_id, mode?)

Generate a browser-based call URL — no phone number needed.

• mode: "outbound" → AI speaks first (agent initiates the conversation)
• mode: "inbound" → user speaks first (agent waits and responds)
• Default: use the skill's own mode setting

Returns share_url, room_name, mode, expires_in.

make_voice_call(to, skill_id, context)

Dial a real phone number via SIP. Requires SIP_OUTBOUND_TRUNK_ID in .env.

• to: E.164 phone number e.g. "+8613800138000"
• context: fills {{variable}} placeholders in the skill's system prompt

get_call_result(room_name, wait_seconds?)

Retrieve the transcript and summary of a completed call.

• wait_seconds: long-poll up to 300 seconds — returns the moment the call ends
• Returns: transcript[], summary.outcome, summary.summary, summary.sentiment

get_skill(skill_id)

Get a skill's details including context variables.

check_pending_inbound / accept_inbound_call(room_name, skill_id)

Handle incoming SIP calls — list pending calls and accept them with a chosen skill.

Skills

Skills are Markdown files in skills/ — YAML frontmatter + LLM system prompt:

---
name: my_skill
description: 简短描述
language: zh          # zh | en | auto
mode: outbound        # outbound | inbound | both
max_duration: 300
context_variables:
  - contact_name
  - topic
---
你是 Relay，一个 AI 助理。联系人：{{contact_name}}。话题：{{topic}}。

No code changes needed. Add a file → restart → immediately available via MCP.

Typical Agent Workflow

User: "帮我跟进一下张三的Q1合同签署"

Claude:
  1. list_skills() → 找到 follow_up
  2. create_share_link("follow_up", mode="outbound")
     → share_url: https://your-tunnel.../call/web-xxx?token=...
  3. 把链接发给用户：
     "请点击此链接，AI 会主动跟进您的合同事宜：[链接]"
  4. get_call_result(room_name, wait_seconds=300)
     → 通话结束后立即返回
  5. 总结并回复：
     "张三确认将于本周五提交合同。通话时长 2 分 18 秒。"

Architecture

User prompt
    │
    ▼
Claude (MCP client)
    │  tools: create_share_link / get_call_result
    ▼
phone-call-agent backend (FastAPI :8001)
    │
    ├── LiveKit room (WebRTC)
    │       ├── AI agent (Pipecat pipeline)
    │       │       ├── VAD (Silero)
    │       │       ├── STT (Whisper / Volcengine)
    │       │       ├── LLM (GPT-4o / Doubao)
    │       │       └── TTS (OpenAI / Volcengine)
    │       └── User (browser via share link, or SIP phone)
    │
    └── Results stored → GET /calls/{room}/result

Supported Providers