How to Build a Voice AI Agent Using Open-Source Tools (www.freecodecamp.org)

A new open-source stack centered on the EchoKit server shows how to build real-time, customizable voice AI agents that you can run locally. EchoKit is a Rust-based agent orchestrator that exposes a WebSocket interface to stream audio from clients (an ESP32-based echokit_box or a browser JS client), coordinates VAD, ASR, LLM and TTS services, and returns streaming audio responses. The project ships prebuilt binaries (x86/arm64) and uses a config.toml to wire services: Groq’s Whisper ASR (whisper-large-v3) and Groq-hosted LLMs (e.g., openai/gpt-oss-20b) are used in examples, Silero VAD has been ported to Rust to run on port 9094, and TTS can be ElevenLabs or an open-source GPT-SoVITS server. EchoKit also supports the MCP protocol for LLM tool/action flows (e.g., ExamPrepAgent) so agents can fetch structured data and perform tasks, not just chat. The significance: it demonstrates a pragmatic alternative to monolithic end-to-end voice models by orchestrating specialized components to maximize customization, privacy, and voice cloning while minimizing latency through streaming and Rust performance. Trade-offs are clear—multi-model pipelines need optimizations (streaming I/O, device/server VAD) to compete with single-step models, but they enable system prompts, custom knowledge injection, tool calls, and on-prem deployment. For teams needing low-latency, private, and highly customizable voice agents, EchoKit provides a production-ready reference stack and roadmap for migrating Python tooling into a fast, safe Rust runtime.