Feros Voice Agent OS is built with a clear goal: providing an open, airtight, and enterprise-grade infrastructure layer for production voice AI.

We built Feros to solve the structural problems of the current voice AI ecosystem. With a Rust runtime engineered for sub-second latency, an AI-driven builder, and a Python control plane—all in a single self-hostable monorepo—we address these barriers head-on:

The Approach	The Barrier	The Feros Solution
Managed Platforms (Vapi, Retell)	Per-minute costs compound at scale, with no path to self-host or satisfy strict data residency requirements.	Deploy the complete platform in your own infrastructure — no per-minute taxes, full control over data residency and compliance.
Low-Level Frameworks (Pipecat, LiveKit)	Weeks spent building and maintaining the voice pipeline, rather than focusing on agent quality.	A production-ready voice pipeline — VAD, STT, LLM, TTS — ships on day one. Focus on the agent, not the plumbing.
Visual Node Builders (Legacy platforms)	Hand-wiring agent logic, dragging nodes, and stitching call flows step-by-step becomes unmaintainable.	Describe what you want your agent to do, and the AI autonomously provisions the tools, prompts, and routing logic.

feros.mp4

Architecture

The control plane (Python) handles configuration and management. The voice runtime (Rust) handles every live call. The two layers scale independently, and every component — STT, LLM, TTS, telephony provider — is swappable without touching the rest.

flowchart LR
  UI[Studio Web] --> API[Studio API]
  API --> DB[(Postgres)]

  TEL[Telephony / Browser] --> VS[voice-server]
  VS --> VE[voice-engine]
  VE --> STT[STT]
  VE --> LLM[LLM]
  VE --> TTS[TTS]

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Layer	Component	Purpose
Dashboard	studio/web	Agent builder, call monitoring, in-browser voice testing
Control Plane	studio/api	Agent config, integrations, evaluations, session provisioning
Integrations	integrations	Encrypted credential vault for CRMs, calendars, etc. — third-party secrets never leave your infrastructure in plaintext
Voice Runtime	voice/server	Inbound telephony and WebSocket gateway
	voice/engine	High-performance VAD → STT → LLM → TTS orchestration
Inference (optional)	inference	Self-hosted GPU STT/TTS — drop-in for cloud APIs

Repository Structure

All services live in a single repo and share a common Postgres database and config layer.

Path	Purpose
studio/web	Next.js dashboard and AI-driven agent builder
studio/api	FastAPI control plane — agent config, integrations, evaluations, session setup
voice/server	Rust telephony gateway and session router
voice/engine	Rust runtime core — streaming STT/LLM/TTS orchestration at sub-second latency
integrations	Credential encryption, secret resolution, and automatic token refresh
inference	Optional self-hosted STT/TTS stack for cost control and data sovereignty
proto	Shared protobuf definitions for WebSocket message payloads

Quickstart

Requirements: Docker and Docker Compose.

git clone https://github.com/ferosai/feros.git
cd feros
cp .env.example .env

# Run with prebuilt multi-arch images (Lightning fast startup)
docker compose up -d

# OR, if you need to build the Rust/Python core from source:
# docker compose -f docker-compose.yml -f docker-compose.source.yml up -d --build

Open http://localhost:3000 to access the dashboard.

Local services:

Service	URL
Studio Web	http://localhost:3000
Studio API	http://localhost:8000
Voice Server	http://localhost:8300

AUTH__SECRET_KEY and DATABASE__URL must be set consistently across all services in your .env.

Roadmap

• Outbound calls — agent-initiated dialing with retry and scheduling
• Dynamic Agent Variables — resolve runtime context at session start for personalized conversations
• Granular Usage Billing — step-level cost attribution across models and third-party services
• Gemini Live native audio — end-to-end multimodal backend
• Direct PSTN via SIP — no Twilio or Telnyx required
• Agent-to-agent evaluation — tester agent calling target agent over live audio
• Evaluation replay — run historical transcripts against new agent versions
• Audit logs — immutable trail of agent actions and config changes
• Usage analytics — per-agent cost tracking across STT, LLM, and TTS providers

Contributing

Contributions are welcome. Before starting larger changes, please open an issue or discussion first — alignment before implementation saves time.

• Familiarize yourself with the voice runtime architecture before making changes to the Rust core; it has strict ordering and concurrency constraints.
• Run the affected service locally before opening a PR.
• Add or update tests when behavior changes; the voice pipeline has integration tests that catch regressions the unit tests miss.

License

Apache License 2.0. See LICENSE for details.

Third-party code vendored in this repository remains subject to its own license terms where noted in the source tree.