Building an AI voice agent is easier than it has been since a few years.
You can already connect speech-to-text, an LLM, and text-to-speech, all within in a few hours and create a working demo.
But, a working demo is not the same as a production-ready AI voice agent.
Production voice agents also need to handle:
- Real-time audio streaming
- Low response latency
- Interruptions and barge-in
- Conversation state
- Tool calling
- CRM or database integrations
- Call failures
- Human handoff
- Monitoring and observability
- Testing and evaluation
This is why choosing the right framework matters.
In this article hereon, you can find eight open-source tools that developers use to build production-ready AI voice agents in 2026.
1. LiveKit Agents
Best for: Real-time, scalable voice and multimodal agents
LiveKit Agents is one of the strongest open-source options for developers building real-time voice systems.
It provides infrastructure for real-time communication and a framework for building programmable AI agents that can listen, understand, respond, and use external tools.
Why Developers Use It?
- Real-time audio and video
- WebRTC-based communication
- Python and JavaScript/TypeScript support
- Streaming voice pipelines
- Tool calling
- Multimodal capabilities
- Self-hosting options
- Production deployment support
Real Use Cases
- Customer support voice agents
- AI receptionists
- Voice assistants
- Real-time sales agents
- Telephony applications
- Multimodal AI applications
Example Architecture
Phone or Web Client
↓
LiveKit Room
↓
LiveKit Agent
↓
STT → LLM → TTS
↓
CRM, Calendar, Database
2. Pipecat
Best for: Custom Voice Pipelines and Modular AI-agent Architecture
Pipecat is an open-source Python framework designed for building real-time voice and multimodal conversational agents. It allows developers to connect speech, AI, transport, and tool components through configurable pipelines.
Pipecat is useful when you want to control each part of the voice pipeline.
Why Developers Use It?
- Modular architecture
- Streaming-first design
- Multiple STT integrations
- Multiple LLM integrations
- Multiple TTS integrations
- Custom pipeline logic
- Real-time interruption handling
- Support for voice and multimodal applications
Good Use Cases
- Custom AI voice products
- AI call agents
- Research and experimentation
- Multi-provider voice systems
- Self-hosted voice applications
Example Pipeline
Audio Input
↓
Voice Activity Detection
↓
Streaming STT
↓
LLM
↓
Tool Call
↓
Streaming TTS
↓
Audio Output
Choose Pipecat When: You need fine-grained control over the voice pipeline and want to combine different AI providers.
3. TEN Framework
Best For: Low-latency conversational and multimodal AI
TEN Framework is an open-source framework for building real-time conversational AI applications.
It supports voice, vision, and avatar experiences and is designed around extensible components that can communicate through a real-time architecture.
Why Developers Use It?
- Real-time communication
- Low-latency design
- Voice and multimodal support
- Extensible architecture
- AI-provider integrations
- Custom extensions
Good Use Cases
- Interactive AI assistants
- AI avatars
- Real-time voice applications
- Multimodal customer experiences
Choose TEN When: You are building a real-time conversational product that may expand beyond voice into video, vision, or avatars.
4. Vocode
Best For: Developers building programmable voice applications
Vocode is an open-source framework for building voice-based AI applications.
It provides components for connecting speech recognition, language models, speech synthesis, and communication channels.
Why Developers Use It?
- Voice-agent abstractions
- Telephony support
- Modular components
- Custom conversation logic
- Python-based development
Real Use Cases
- AI phone agents
- Voice assistants
- Automated calling workflows
- Voice-enabled applications
Choose Vocode when: You want a developer-focused voice framework, and its current ecosystem matches your technical requirements.
5. Bolna
Best For: Open-source AI phone-call agents
Bolna focuses on building conversational voice agents for phone calls.
It can be useful for teams developing automated calling systems that need to connect speech models, LLMs, telephony, and business tools.
Why Developers Use It?
- Voice-call automation
- Modular AI components
- Telephony-focused workflows
- Custom conversation logic
- Self-hosting possibilities
Real Use Cases
- Lead qualification
- Appointment booking
- Customer support calls
- Automated outbound calling
- Voice-based business workflows
Choose Bolna when: your primary use case is AI-powered phone conversations rather than browser-based voice interactions.
6. Whisper
Best for: Open-source speech-to-text
A voice agent needs to convert spoken audio into text.
Whisper is an open-source speech-recognition model that can be used as the speech-to-text layer in a voice-agent architecture.
Why developers use it
- Open-source model
- Multilingual speech recognition
- Self-hosting
- Greater control over audio data
- Flexible deployment options
Real use cases
- Voice transcription
- Multilingual assistants
- Private speech-processing workflows
- Custom speech pipelines
Choose Whisper When: You need control over the speech-recognition layer and want to run it within your own infrastructure.
7. Coqui TTS
Best For: Open-source text-to-speech and custom voice generation
Coqui TTS provides open-source tools and models for converting text into speech.
It can be used as the speech-output layer of a custom AI voice system.
Why Developers Use It
- Open-source speech synthesis
- Self-hosting options
- Custom voice capabilities
- Greater control over audio generation
Real Use Cases
- Custom voice assistants
- Private voice systems
- Branded voice experiences
- Research and experimentation
Choose Coqui TTS When: You need more control over the voice-generation layer and are prepared to manage the infrastructure.
8. Langfuse
Best for: Observability, tracing, and evaluation
A production voice agent should not be treated as a black box.
Why Developers Use It
- LLM tracing
- Prompt management
- Evaluation workflows
- Latency monitoring
- Cost tracking
- Debugging support
Real Use Cases
- Monitoring voice-agent conversations
- Debugging tool calls
- Evaluating AI responses
- Identifying failure patterns
- Comparing prompts and models
Example trace
Voice Session
↓
Speech-to-Text: 350 ms
↓
LLM Response: 700 ms
↓
CRM Tool Call: 200 ms
↓
Text-to-Speech: 300 ms
↓
Total Response Time: 1.55 seconds
Choose Langfuse When: You need observability and evaluation for a production AI system.
Which Tool Should You Choose?
Final Thoughts
Building a production-ready AI voice agent deals with additional aspects aside only connecting an LLM to speech-to-text and text-to-speech.
The real challenge is creating a system that responds quickly, handles interruptions, and uses business tools reliably. Furthermore, it should be capable enough to recover from failures and transfer relevant conversations to a human when needed.
Open-source tools give developers the flexibility to control the architecture, customize it, and avoid being locked into a single platform. But they will also require stronger engineering around infrastructure, security, monitoring, and maintenance.
Start with one focused use case - such as lead qualification, appointment booking, or customer support. Build a small end-to-end workflow, test it with real conversations, measure latency and failure rates, and improve the system before scaling.
Planning to build a production-ready AI voice agent? Explore our AI Voice Agent Development Services to turn your voice AI idea into a scalable, reliable solution.
Top comments (0)