How to build your first AI Voice agent on Pipecat

Customers no longer compare your service to your competitors. They compare it to their last best experience. Is your voice support ready for that test?

For most businesses, the honest answer is "no." The good news? You are not alone. The bar for what's possible has been raised dramatically by AI. It's now possible to offer a support agent that is always available, instantly knowledgeable, and never keeps a customer waiting. The best part is: you can build it yourself.

Meet Pipecat: Your AI Voice Agent Builder

Unlike a few years ago, building a conversational AI agent has become less complex with the introduction of frameworks like Pipecat. When compared to its competitors, Pipecat offers superior features and cost-effectiveness, positioning it as the first choice. Pipecat stands out as a powerful open-source framework for building real-time multimodal and voice conversational agents. Whether you’re exploring automated voice systems for customer support or experimenting with voice chat APIs for outbound calling, Pipecat offers a developer-friendly and cost-effective path to deployment.

We have created this blog to act as a guide for building a human-like voice agent for your business in clear and organized steps. The businesses that will win customer loyalty in the next decade are those building their own AI voice agents, not as a cost-cutting tool, but as a flagship experience.

Pre-requisites to building a Voice Agent with Pipecat

Here are the essential accounts, API keys, and setup tools needed to ensure a smooth start in AI voice agent building.

Core environment and framework

• Pipecat installed
• Node/Python environment
• A hosting server with WebSocket support
• A basic understanding of real-time audio pipelines

Speech & Audio Processing

• API access to STT like Whisper, Deepgram, etc.
• API access to TTS engines like OpenAI TTS, ElevenLabs, etc.
• VoIP/WebRTC setup for call or browser-based voice interaction
• Audio I/O capabilities

AI and knowledge base

• Access to an LLM like OpenAI, Claude, Llama, etc.
• Knowledge base/domain data

Integrations and Tools

• Required API integrations like CRM, OMS, etc.
• Demo or custom tools

Building an AI Voice Agent with Pipecat

Discover the essential steps businesses have to follow to design, connect, and deploy effective AI voice agents using Pipecat.

Begin with Model Setup

• Configure Speech-to-Text, which turns the caller’s voice into text

• Configure LLM, which decides the best reply by setting the base URL, choosing the model name, and adding a system prompt explaining its role and how to respond.

• Configure Text-to-Speech, which converts the reply into a human-like voice.

• Set up tools that enable the agent to do useful tasks while talking

• Register demo tool check_availability (returns { "available": true } after 2s).

• Tools can later be extended to connect with CRM, booking systems, or inventory.

Build the conversation flow

Pipecat’s pipeline processes audio → text → AI → audio response:

• Transport.input
• Speech-to-Text
• Context Aggregator adds user text to LLM context
• LLM
• Text-to-Speech
• Transport.output sends audio back to caller
• ContextAggregator.assistant saves assistant responses to memory

Task Configuration

• Set audio sample rate: 8000 Hz for standard for phone calls and higher rates for STT
• Enable metrics for debugging/logs
• Define events: on_client_connected → send greeting on_disconnect → stop pipeline when caller hangs up

Twilio Transport & Entry Point

• Use FastAPI WebSocket transport
• Process Twilio call data
• Enable audio in/out, Silero VAD analyzer
• Start bot server

Inbound Call Setup

• Run setup_ngrok_twilio.py
• Select the Twilio number to attach to the agent
• Update webhook to Ngrok public URL
• Save Ngrok host in .env
• Call the Twilio number to test inbound calls

Outbound Calls

• Run a simple command with: to = customer phone number from = Twilio number linked to agent
• Once connected, audio streams into the Pipecat pipeline

Testing

• Inbound: Start bot server, call Twilio number, observe logs + conversation flow
• Outbound: Run script, answer call, agent responds instantly.

Production Deployment

• Containerize code with Docker
• Deploy via Pipecat CLI to Pipecat Cloud
• Cloud handles scaling, reliability, and reserved agents

By following these steps, you will have a working AI outbound calling agent or inbound voice bot that interacts with your customers in an empathetic manner.

If your in-house team lacks the resources or expertise to build the agent using these steps, partner with AI software development companies experienced in providing AI voice-based solutions for businesses. At SayOne, we build voice agents tailored to your unique workflows and ensure the solution fits smoothly into your customer journey.

Contact us to build a voice AI that works for you 24/7 efficiently.