Three months.
That’s how long many teams spend building telephony infrastructure before writing a single line of actual conversation logic for an AI voice agent.

Not because the AI was hard.
Because telephony is brutal.

Today, we’re open-sourcing the solution so you don’t have to go through the same pain.

The Hidden Problem with AI Calling Agents

Building an AI calling agent sounds straightforward:

• Use an LLM

• Add speech-to-text

• Add text-to-speech

• Connect it to a phone number

In reality, that’s where most teams hit a wall.

To make real phone calls, you end up dealing with:

• SIP trunks & PSTN providers

• Low-latency, bidirectional audio

• Real-time orchestration of STT, LLM, and TTS

• Call state, interruptions, transfers

• Scaling, monitoring, recordings, persistence

The result?
Most teams spend weeks or months on infrastructure before they ever touch the conversation itself.

We did too. And eventually asked:

“Why is building voice AI still this hard?”

Introducing Siphon

Siphon is an open-source Python framework that handles the telephony complexity for you, so you can focus on building great conversations.

Here’s what a complete AI receptionist looks like with Siphon:

from siphon.agent import Agent
from siphon.plugins import openai, cartesia, deepgram

agent = Agent(
    agent_name="receptionist",
    llm=openai.LLM(model="gpt-4"),
    tts=cartesia.TTS(voice="helpful-assistant"),
    stt=deepgram.STT(model="nova-2"),
    system_instructions="""
    You are a friendly receptionist for Acme Corp.
    Help callers schedule appointments or route them correctly.
    """
)

if __name__ == "__main__":
    agent.start()

Run this, and your agent can answer real phone calls via any SIP provider (Twilio, Telnyx, etc.).

What Siphon Handles for You

• 🔌 SIP & PSTN connectivity
  Works with any SIP provider, no FreeSWITCH pain.

• ⚡ Real-time audio pipeline
  Built on LiveKit with streaming audio and sub-500ms voice-to-voice latency.

• 🤖 AI orchestration
  Plug-and-play support for LLMs, STT, and TTS.

Swap providers with a single line:

  llm=anthropic.LLM(model="claude-3-5-sonnet")

• 📈 Production-ready by default Auto-scaling, call recordings, transcripts, state handling, and observability.

Quick Start

Install:

pip install siphon-ai

Create an agent:

from siphon.agent import Agent
from siphon.plugins import openai, cartesia, deepgram

agent = Agent(
    agent_name="my_first_agent",
    llm=openai.LLM(),
    tts=cartesia.TTS(),
    stt=deepgram.STT(),
    system_instructions="You are a helpful assistant.",
)

agent.start()

That’s it.
Your agent is live and answering phone calls.

(Full setup, outbound calling, and advanced examples are in the docs.)

Why We Open-Sourced It

We could’ve kept Siphon proprietary or turned it into a closed SaaS.

But we believe voice AI shouldn’t be locked behind massive infrastructure effort.

Siphon is:

• Apache 2.0 licensed

• Provider-agnostic

• Fully self-hostable

• No vendor lock-in

Use it commercially, modify it, or build on top of it.

What You Can Build

• 📞 Customer support agents

• 📅 Appointment scheduling

• 💼 Sales qualification

• 📊 Surveys & feedback collection

• 🏥 Healthcare intake systems

If it involves phone calls and conversations, Siphon handles the hard parts.

Get Involved

⭐ GitHub: https://github.com/blackdwarftech/siphon
📖 Docs: https://siphon.blackdwarf.in/docs
🐛 Issues & feature requests welcome
🤝 PRs encouraged

We’re building Siphon in public and would love community feedback.

If you’ve ever thought

“I wish building AI calling agents was simpler”

— give Siphon a try.

Built by BLACKDWARF
Mission: Democratize complex technologies for developers.