enterprise-realtime-voice-agent

KO | EN

gitlite — search

#hacktoberfest #ai #llm #golang #go #rust #nodejs #awesome #cli #kubernetes #awesome-list #vue #ios #mcp

enterprise-realtime-voice-agent

(★ 54)

No description available.

enterprise-realtime-voice-agent Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Enterprise Realtime Voice Agent

A step-by-step tutorial for building a **streaming, enterprise-grade voice agent** from first principles. You'll understand exactly how production voice agents work internally.

## What You'll Build

A voice agent that:
- Listens to speech in real-time (Deepgram streaming STT)
- Reasons and calls tools via a streaming LLM (self-hosted vLLM or OpenAI API)
- Speaks responses in real-time (ElevenLabs streaming TTS)
- Handles interruptions, turn-taking, and multi-turn conversations
- Supports enterprise function calling (book appointments, check orders, etc.)

```
[Browser Mic] --> WebSocket --> [Deepgram STT] --> [LLM + Tools] --> [ElevenLabs TTS] --> WebSocket --> [Browser Speaker]
                                  streaming          streaming          streaming
                                   ~400ms             ~340ms             ~220ms
```

## Key Insight

A "voice agent" is really just an **LLM agent with voice I/O**. And "realtime" comes from **streaming + pipelining** -- not from any single fast model:

```
WITHOUT streaming (turn-based):
[====STT====][======LLM======][====TTS====][play]     --> ~2s wait

WITH streaming (overlapping):
[====STT====]
             [tok][tok]["Hello,"][tok][tok]["I can"]
                        |                    |
                  [TTS "Hello,"]      [TTS "I can"]
                        |                    |
                     [play]               [play]       --> ~700ms wait
```

## Measured Latency

| Component | P50 | Min |
|-----------|-----|-----|
| Deepgram STT | 337ms | 184ms |
| LLM TTFT (vLLM, Qwen2.5-7B) | 337ms | 318ms |
| ElevenLabs TTS TTFB | 219ms | 215ms |
| **End-to-end TTFA** | **947ms** | **729ms** |
| Pipeline (streaming overlap) | -- | **755ms** |

## Quick Start

```bash
# 1. Clone and set up
git clone https://github.com/SalesforceAIResearch/enterprise-realtime-voice-agent.git
cd enterprise-realtime-voice-agent
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# 2. Configure API keys
cp credentials.env.example credentials.env
# Edit credentials.env with your Deepgram, OpenAI, and ElevenLabs keys

# 3. Run chapters progressively
source credentials.env
python chapters/01_streaming_stt/deepgram_streaming.py
python chapters/02_streaming_llm/vllm_streaming.py
python chapters/03_streaming_tts/elevenlabs_streaming.py
python chapters/04_streaming_pipeline/pipeline.py
python chapters/09_production/benchmark.py
```

## Tutorial Structure

| Chapter | What You Learn | Key File |
|---------|---------------|----------|
| 1. Streaming STT | Deepgram WebSocket, partial/final transcripts | `deepgram_streaming.py` |
| 2. Streaming LLM | vLLM/OpenAI streaming, function calling | `vllm_function_calling.py` |
| 3. Streaming TTS | ElevenLabs streaming, TTFB measurement | `elevenlabs_streaming.py` |
| 4. **Streaming Pipeline** | **Sentence aggregation, overlapping execution** | `pipeline.py` |
| 5. WebSocket Server | Bidirectional audio, protocol design | `server.py` |
| 6. VAD & Interruptions | Silero VAD, conversation state machine | `vad_basics.py` |
| 7. Web Client | AudioWorklet, mic capture, streaming playback | `index.html`, `app.js` |
| 8. Enterprise Agent | Function calling, tool use, domain logic | `agent.py`, `tools.py` |
| 9. Production | Benchmarking, latency measurement | `benchmark.py` |

## Architecture

```
+---------------------------------------------------------------+
|                    Browser Client (Ch 7)                       |
|  [Mic] --> AudioWorklet (16kHz PCM) --> WebSocket -------+    |
|  [Speaker] <-- AudioWorklet (24kHz PCM) <-- WebSocket <--+--+ |
+-----------------------------------------------------------+--+-+
                                                            |  |
+-----------------------------------------------------------+--+-+
|                   FastAPI Server (Ch 5-6)                  |  | |
|                                                            |  | |
|  Silero VAD (Ch 6)     Conversation State Machine          |  | |
|  [speech detect] -->   IDLE->LISTENING->PROCESSING         |  | |
|                                    |                       |  | |
|              +---------------------+-------------------+   |  | |
|              |  Streaming Pipeline (Ch 4)              |   |  | |
|              |                                         |   |  | |
|              |  Deepgram STT (Ch 1)  <-----------------+---+  | |
|              |      | transcript                       |      | |
|              |  LLM Agent + Tools (Ch 2, 8)            |      | |
|              |      | tokens --> sentence buffer        |      | |
|              |  ElevenLabs TTS (Ch 3)  ----------------+------+ |
|              |      | audio chunks                     |        |
|              +-----------------------------------------+        |
+-----------------------------------------------------------------+
```

## Tech Stack

| Component | Technology | Why |
|-----------|-----------|-----|
| STT | Deepgram Nova-3 | Fastest streaming STT (~200-500ms) |
| LLM | vLLM (self-hosted) or OpenAI API | Streaming tokens, function calling |
| TTS | ElevenLabs | High quality, streaming (~220ms TTFB) |
| VAD | Silero VAD | <1ms/chunk, 2MB, CPU-only |
| Server | FastAPI + WebSocket | Async, bidirectional |
| Client | Vanilla JS + AudioWorklet | Low-latency, no build tools |

## Required API Keys

```bash
# credentials.env
DEEPGRAM_API_KEY=...        # https://deepgram.com (free tier: 12,000 mins/year)
ELEVENLABS_API_KEY=...      # https://elevenlabs.io (free tier: 10,000 chars/month)
OPENAI_API_KEY=...          # https://platform.openai.com (or use self-hosted vLLM)
```

## Self-Hosted LLM (Optional)

For lowest latency, serve an LLM locally with vLLM:

```bash
pip install vllm==0.8.5.post1
vllm serve Qwen/Qwen2.5-7B-Instruct --port 8000 --gpu-memory-utilization 0.8
```

The code auto-detects vLLM on `localhost:8000` and uses it instead of the OpenAI API.

## Citation

```bibtex
@article{qiu2026building,
  title={Building Enterprise Realtime Voice Agents from Scratch: A Technical Tutorial},
  author={Qiu, Jielin and Chen, Zixiang and Yang, Liangwei and Zhu, Ming and Liu, Zhiwei and Tan, Juntao and Zhao, Wenting and Murthy, Rithesh and Ram, Roshan and Prabhakar, Akshara and others},
  journal={arXiv preprint arXiv:2603.05413},
  year={2026}
}
```

## License

Apache 2.0

enterprise-realtime-voice-agent

enterprise-realtime-voice-agent Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

enterprise-realtime-voice-agent

enterprise-realtime-voice-agent Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README