Open Source AI Voice Agent Tools

Why Open Source

Open source voice agent tools appeal to organizations that need control that managed platforms do not provide. Data sovereignty requirements may mandate that all conversation data stays within the organization infrastructure, prohibiting the use of third-party platforms that process audio and transcripts on external servers. Highly specialized use cases may require custom modifications to the conversation pipeline that closed platforms do not allow. Cost optimization at scale may favor the capital expense of building and maintaining infrastructure over the ongoing per-minute fees of managed platforms.

The tradeoff is engineering investment. Open source tools provide building blocks, not finished products. Teams must assemble the components, handle deployment and scaling, implement monitoring and alerting, and maintain the system over time. This requires dedicated engineering resources with expertise in real-time audio processing, distributed systems, and machine learning infrastructure.

The break-even point between open source and managed platforms depends on call volume. At low volumes (under 10,000 minutes per month), the engineering cost of maintaining open source infrastructure typically exceeds the platform fees you would pay on a managed service. At high volumes (over 100,000 minutes per month), the per-minute savings from eliminating platform fees can justify the engineering investment. Between these thresholds, the decision depends on whether your requirements demand the customization that open source provides regardless of cost.

LiveKit

LiveKit is an open source platform for real-time audio and video communication. Originally designed for video conferencing and live streaming, it has expanded to support voice agent use cases through its Agents framework. LiveKit provides the WebRTC and SIP infrastructure that voice agents need to connect to callers over both web and phone networks.

The LiveKit Agents framework allows developers to build voice agents that connect to LiveKit rooms. An agent joins a room as a participant, receives audio from the human participant, processes it through an ASR-LLM-TTS pipeline, and sends synthesized audio back. The framework includes built-in support for popular ASR providers (Deepgram, AssemblyAI), LLM providers (OpenAI, Anthropic), and TTS providers (ElevenLabs, PlayHT, Cartesia).

LiveKit handles the challenging real-time infrastructure, including WebRTC peer connections, audio codec negotiation, network adaptation, and SIP gateway integration. This allows agent developers to focus on conversation logic rather than audio transport. The platform also provides client SDKs for web, iOS, Android, and other platforms, enabling voice agents to work across multiple channels from the same backend.

The LiveKit server is written in Go for performance and can be deployed on standard cloud infrastructure. It scales horizontally, allowing teams to add capacity by deploying additional server instances behind a load balancer. The project has strong community support and active development, with regular releases that add new capabilities and improve performance.

Pipecat

Pipecat, developed by Daily (a real-time video infrastructure company), is an open source framework specifically designed for building voice and multimodal conversational agents. It provides a pipeline abstraction where developers define a chain of processors that handle audio input, transcription, language model processing, speech synthesis, and audio output.

The pipeline architecture makes Pipecat flexible and modular. Each processor handles one stage of the conversation pipeline, and processors can be swapped or customized independently. Want to switch from Deepgram to AssemblyAI for speech recognition? Replace one processor in the pipeline. Want to add custom logic between the LLM and TTS stages? Insert a new processor. This modularity makes experimentation and iteration straightforward.

Pipecat includes built-in support for transport layers (Daily, WebRTC, WebSocket), ASR providers, LLM providers, and TTS providers. It handles turn-taking, interruption detection, and conversation state management. The framework is written in Python, making it accessible to the large community of Python developers working on AI applications.

Pipecat also supports multimodal interactions beyond voice. The pipeline can process video frames alongside audio, enabling agents that can see what the user is showing them through a camera. This capability is useful for visual support scenarios, document verification, and other use cases where seeing the context improves the agent ability to help.

Vocode

Vocode provides both an open source library and a hosted platform for building voice-based LLM applications. The open source component is a Python library that handles conversation orchestration, including the coordination of ASR, LLM, and TTS components, turn-taking logic, and telephony integration through Twilio.

Vocode abstractions define conversations through three main concepts: transcribers (ASR), agents (LLM logic), and synthesizers (TTS). Each concept has multiple implementations that can be mixed and matched. The library also includes conversation management, handling the state machine that tracks where the conversation is and what should happen next.

The open source library is suitable for teams that want to self-host their voice agent infrastructure. It provides the orchestration layer while allowing teams to choose their own providers for each component and deploy on their own infrastructure. The hosted platform offers the same capabilities with managed infrastructure for teams that want the flexibility of Vocode architecture without the operational overhead.

Infrastructure Requirements

Running open source voice agents in production requires several infrastructure components. A compute layer provides the CPU and GPU resources for running the agent code, hosting any locally-deployed models, and processing audio. Most deployments use cloud instances (AWS EC2, Google Cloud Compute, or Azure VMs) with GPU-equipped instances for any local LLM or TTS models.

A telephony gateway bridges the SIP/PSTN network and the agent infrastructure. LiveKit includes a built-in SIP bridge. For other frameworks, teams typically deploy a dedicated SIP gateway (like Opal or FreeSWITCH) that connects inbound phone calls to the agent audio pipeline. The gateway handles call signaling, codec transcoding, and audio routing.

Monitoring and observability infrastructure is essential for production operations. Deploy centralized logging (ELK stack, Datadog, or similar) to aggregate logs from all agent instances. Set up metrics collection (Prometheus, Grafana) to track per-call performance metrics, system resource utilization, and error rates. Implement distributed tracing to follow a single call through every pipeline stage for debugging latency issues.

Storage infrastructure handles call recordings, transcripts, and analytics data. Recordings consume significant storage (approximately 500 KB per minute of call at typical telephony quality), so plan storage capacity based on your expected call volume and retention policies. Implement lifecycle policies that archive or delete old recordings according to your compliance requirements.

Building with Open Source

A typical open source voice agent deployment involves selecting a transport layer (LiveKit, Daily, or raw WebRTC/SIP), an orchestration framework (Pipecat, Vocode, or custom), and individual providers for ASR, LLM, and TTS. The deployment runs on cloud infrastructure (usually GPU-equipped instances for any locally-hosted models) with appropriate monitoring, logging, and scaling automation.

The development workflow starts with a local prototype that validates the conversation design and component selection. Teams test with simulated calls, then move to a staging environment with real phone connectivity for pilot testing with actual users. Production deployment adds monitoring, alerting, call recording, analytics, and scaling policies.

Operational considerations include model updates (keeping ASR, LLM, and TTS models current), infrastructure scaling (handling variable call volume), cost management (optimizing GPU utilization and API costs), and security (protecting call recordings and transcripts). These responsibilities shift from the platform vendor to the operating team when using open source tools.