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Offline voice agent framework for robots — agents, skills, workflows, sub-second voice pipeline, 2D/3D simulation (IR-SIM + MuJoCo), 16 languages, 56 voices, ROS2-native. Runs on CPU/CUDA/Metal, no cloud.

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# EdgeVox

**Offline voice agent framework for robots and desktop apps.**
**Local streaming voice pipeline. Fully private.**

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<table>
 <tr>
 <td align="center" width="50%">
 <a href="https://raw.githubusercontent.com/nrl-ai/edgevox/main/docs/public/screenshot.png"><img src="https://raw.githubusercontent.com/nrl-ai/edgevox/main/docs/public/screenshot.png" alt="EdgeVox TUI — streaming voice pipeline"/></a>
 Voice pipeline TUI — streaming STT · LLM · TTS with VAD barge-in
 </td>
 <td align="center" width="50%">
 <a href="https://raw.githubusercontent.com/nrl-ai/edgevox/main/docs/public/robot_panda.png"><img src="https://raw.githubusercontent.com/nrl-ai/edgevox/main/docs/public/robot_panda.png" alt="MuJoCo Franka Panda pick-and-place"/></a>
 MuJoCo · Franka arm — voice-controlled pick-and-place
 </td>
 </tr>
 <tr>
 <td align="center" width="50%">
 <a href="https://raw.githubusercontent.com/nrl-ai/edgevox/main/docs/public/robot_unitree_g1.png"><img src="https://raw.githubusercontent.com/nrl-ai/edgevox/main/docs/public/robot_unitree_g1.png" alt="Unitree G1 humanoid"/></a>
 Unitree G1 humanoid — procedural gait + ONNX policy slot
 </td>
 <td align="center" width="50%">
 <a href="https://raw.githubusercontent.com/nrl-ai/edgevox/main/docs/public/rook_app.png"><img src="https://raw.githubusercontent.com/nrl-ai/edgevox/main/docs/public/rook_app.png" alt="RookApp — PySide6 desktop chess robot"/></a>
 RookApp desktop — offline chess partner (Qt + LLMAgent + Stockfish, one Python process)
 </td>
 </tr>
</table>

---

---

## Why EdgeVox?

- **Voice is the interface** — streaming STT → LLM → TTS pipeline targeting sub-second first-audio on consumer GPUs, with Jetson and CPU fallback paths. (No measured perf published yet — see [`benchmarks/`](benchmarks/) for the harness; numbers land here once the first run completes.)
- **Agents are the program model** — write `@tool` and `@skill` functions in Python; compose them with `Sequence`, `Fallback`, `Loop`, `Parallel`, and `Router` workflows; delegate across agents with OpenAI-SDK-style handoffs.
- **Robots and desktop apps are both first-class** — robots get cancellable skills with `GoalHandle`, hard-stop `SafetyMonitor` that bypasses the LLM, three-tier simulation (stdlib → IR-SIM → MuJoCo), and a ROS2 bridge. Desktop apps get the same agent framework + voice pipeline shipped as a single PySide6 process — see [RookApp](#rookapp--desktop-chess-robot) below.
- **Everything is offline** — no cloud APIs, no telemetry, no vendor lock. Gemma 4 via llama.cpp, faster-whisper, Kokoro/Piper/Supertonic TTS. Your mic audio never leaves the machine.

## 30-second demo

**2D — IR-SIM (mobile robot navigation)**

```bash
pip install 'edgevox[sim]'
edgevox-setup                      # downloads ~3 GB of models, one time
edgevox-agent robot-irsim --text-mode
```

A matplotlib window opens showing a 10×10 apartment with four rooms. Type "go to the kitchen" — the blue robot drives visibly. Say "stop" mid-flight and the safety monitor preempts the skill *before the LLM is consulted* — the halt path doesn't wait on a model round-trip. Swap `--text-mode` for `--simple-ui` to drive it by voice.

**3D — MuJoCo (tabletop arm pick-and-place)**

```bash
pip install 'edgevox[sim-mujoco]'
edgevox-agent robot-panda --text-mode
```

A MuJoCo viewer opens with a Franka Panda arm above a table with three colored cubes. Type "pick up the red cube" — the arm moves, grasps, and lifts. Voice commands control `move_to`, `grasp`, `release`, and `goto_home` skills.

**3D — MuJoCo humanoid (Unitree G1 / H1)**

```bash
pip install 'edgevox[sim-mujoco]'
edgevox-agent robot-humanoid --simple-ui
```

A Unitree humanoid (auto-fetched from `nrl-ai/edgevox-models` on first use, ~15 MB) appears in the MuJoCo viewer standing on its home keyframe. Say "walk forward half a meter", "turn left ninety degrees", "stand" — a procedural gait swings the legs + arms while the root advances. Plug in an ONNX walking policy via `MujocoHumanoidEnvironment.set_walking_policy(...)` for real RL locomotion.

**Real robot or external sim via ROS2**

```bash
source /opt/ros/jazzy/setup.bash
edgevox-agent robot-external --text-mode
```

Subscribes to `odom`, optionally `scan` + `camera/image_raw`, and publishes `cmd_vel` + `goal_pose`. Drives any Gazebo Harmonic world, Isaac Sim (via ROS2 bridge), or a real mobile robot that speaks the standard contract — the same agent code works unchanged.

## Features

### Agent framework

- **`@tool` / `@skill` decorators** — auto-derive JSON schemas from Python signatures + docstrings
- **`LLMAgent`** with per-run history isolation, reentrant, thread-safe
- **Workflows**: `Sequence`, `Fallback`, `Loop`, `Parallel`, `Router`, `Supervisor`, `Orchestrator`, `Retry`, `Timeout` — behavior-tree-shaped + multi-agent patterns, nestable
- **Handoffs** — OpenAI-SDK-style "agent-as-return-value" (2 LLM hops per delegation vs smolagents' 3); LangGraph-style `state_update` writes blackboard keys before the target runs
- **Grammar-constrained tool calling** — auto-built GBNF from `ToolRegistry` schemas; `tool_choice_policy="required_first_hop"` is the canonical SLM loop-break (no malformed JSON, no fabricated tool names)
- **Hooks** — 6 fire points (`on_run_start`, `before_llm`, `after_llm`, `before_tool`, `after_tool`, `on_run_end`), priority-ordered (Safety 100 → Observability 0), 12 built-ins ship + 3 SLM hardening hooks
- **Memory** — bi-temporal `Fact` schema (`facts_as_of(t)`), `JSONMemoryStore` + `SQLiteSessionStore`, `Compactor` with tokenizer-exact counts, file-based `NotesFile`
- **Artifacts** — versioned, indexed, exposable as LLM tools via `make_artifact_tools(store)`
- **Cancellable skills** — `GoalHandle` lifecycle with `poll` / `cancel` / `feedback`; mid-flight preempt is bounded by the skill-poll interval (no LLM round-trip on the cancel path)
- **`SafetyMonitor`** — stop-word preempt before the LLM is consulted
- **`EventBus`** — thread-safe pub/sub for observability, metrics, main-thread scheduling
- **`SimEnvironment` protocol** — agent code swaps cleanly between `ToyWorld` (stdlib), `IrSimEnvironment` (IR-SIM), and `MujocoArmEnvironment` (MuJoCo)
- **Parallel tool/skill dispatch** inside a single turn via `ThreadPoolExecutor`
- **8 built-in example agents** — `home`, `robot`, `dev`, `robot-scout`, `robot-irsim`, `robot-panda`, `robot-humanoid`, `robot-external`

### Voice pipeline (substrate)

- **Streaming pipeline** — VAD (32 ms frames) + faster-whisper STT + Gemma 4 E2B + Kokoro TTS, designed for sub-second first-audio on consumer GPUs (target, not measured — bench harness in [`benchmarks/`](benchmarks/))
- **16 languages** with 56 voices across 4 TTS backends
- **Robust voice interrupt** — speak over the bot to cut it off; `specsub` AEC on by default + energy-ratio gate so the bot doesn't hear itself; LLM generation aborted via llama-cpp `stopping_criteria` (cancellation lands at the next decode step); back-to-back barge-ins re-arm cleanly
- **4 wake words** — "Hey Jarvis", "Alexa", "Hey Mycroft", "Okay Nabu"
- **4 interfaces** — TUI (Textual), Web UI (FastAPI + Vue), simple CLI, text mode
- **ROS2-native** — voice-pipeline bridge, TF2 / Nav2 / sensor adapter, `edgevox_msgs/action/ExecuteSkill` action server, external-sim driver for Gazebo / Isaac / real hardware
- **Auto hardware detection** — CUDA / Metal / CPU fallback, VRAM-aware GPU-layer selection

## Quick Start

```bash
# 1. Install uv (fast Python package manager)
curl -LsSf https://astral.sh/uv/install.sh | sh

# 2. Create a virtual environment
uv venv --python 3.12
source .venv/bin/activate

# 3. Install llama-cpp-python (GPU or CPU, your choice)
uv pip install llama-cpp-python \
    --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124
# For Apple Silicon: CMAKE_ARGS="-DGGML_METAL=on" uv pip install llama-cpp-python
# For CPU only:      uv pip install llama-cpp-python

# 4. Install EdgeVox with the sim extra (pulls in ir-sim)
uv pip install -e '.[sim]'

# 5. Download models (~3 GB)
edgevox-setup

# 6a. Run a voice agent with a visible robot
edgevox-agent robot-irsim --text-mode

# 6b. OR run the classic voice pipeline
edgevox
```

## Build your own agent

```python
from edgevox.agents import AgentContext, GoalHandle, ToyWorld, skill
from edgevox.examples.agents.framework import AgentApp
from edgevox.llm import tool

@tool
def set_light(room: str, on: bool, ctx: AgentContext) -> str:
    """Turn a room's light on or off.

Args:
        room: the room name, e.g. "kitchen".
        on: true to turn on, false to turn off.
    """
    ctx.deps.apply_action("set_light", room=room, on=on)
    return f"{room} light is now {'on' if on else 'off'}"

@skill(latency_class="slow", timeout_s=30.0)
def navigate_to(room: str, ctx: AgentContext) -> GoalHandle:
    """Drive the robot to a named room.

Args:
        room: target room, e.g. "kitchen", "bedroom".
    """
    return ctx.deps.apply_action("navigate_to", room=room)

AgentApp(
    name="Scout",
    instructions="You are Scout, a terse home robot. Confirm what you did in one sentence.",
    tools=[set_light],
    skills=[navigate_to],
    deps=ToyWorld(),
    stop_words=("stop", "halt", "freeze"),
).run()
```

Launch with `python my_agent.py --text-mode` and you have a voice-controllable robot running on a stdlib-only reference sim. Swap `ToyWorld()` for `IrSimEnvironment()` and a matplotlib window opens. Full guide: [`docs/documentation/agents.md`](docs/documentation/agents.md).

The five built-in agents are subcommands of `edgevox-agent`:

| Command | What it does |
|---|---|
| `edgevox-agent home` | Home automation — lights, thermostat, timers, weather |
| `edgevox-agent robot` | Simple robot demo with pose + battery |
| `edgevox-agent dev` | Developer toolbox — arithmetic, unit conversion, notes |
| `edgevox-agent robot-scout` | Full agent demo on `ToyWorld` (stdlib, no extra deps) |
| `edgevox-agent robot-irsim` | Full agent demo on IR-SIM with matplotlib window |
| `edgevox-agent robot-panda` | MuJoCo Franka Panda — voice pick-and-place |
| `edgevox-agent robot-humanoid` | MuJoCo Unitree G1 / H1 — voice walk / turn / stand |
| `edgevox-agent robot-external` | Drive any external ROS2 robot (Gazebo, Isaac, real hardware) |

Each one supports `--text-mode`, `--simple-ui`, or (default) full TUI. Any of them composes with `--ros2` to attach the full ROS2 bridge + Nav2 / TF2 / sensor adapter + `execute_skill` action server.

## RookApp — desktop chess robot

![RookApp screenshot](https://raw.githubusercontent.com/nrl-ai/edgevox/main/docs/public/rook_app.png)

Voice-controlled offline chess partner built on EdgeVox — a PySide6 desktop app that runs the Qt UI, the `LLMAgent`, llama-cpp (Llama-3.2-1B Q4_K_M), and Stockfish all in one Python process. No browser, no web server, no Node toolchain. Three personas (`casual`, `grandmaster`, `trash_talker`), six board themes, three piece sets, Kokoro TTS, Whisper STT with barge-in.

```bash
# 1. Install the desktop extra (PySide6 + qtawesome + rlottie + pillow)
uv pip install -e '.[desktop]'

# 2. Stockfish on $PATH (GPL — stays out-of-process, app stays MIT)
sudo apt-get install -y stockfish          # Linux
brew install stockfish                     # macOS
# Windows: https://stockfishchess.org/download/

# 3. Download STT + LLM + TTS models (~3 GB, one time)
edgevox-setup

# 4. Launch
edgevox-chess-robot
```

Flags: `--persona {grandmaster,casual,trash_talker}`, `--user-plays {white,black}`, `--engine {stockfish,maia}`, `--stockfish-skill 0..20`, `--maia-weights PATH`, `-v`. Every flag also reads an `EDGEVOX_CHESS_*` env var. Piece set, board theme, persona, audio devices, and debug-mode settings live in the in-app **☰ → Settings…** dialog. Preferences persist via `QSettings`. Full guide: [`docs/documentation/desktop.md`](docs/documentation/desktop.md).

## Simulation tiers

| Tier | Sim | Dependencies | Role | Status |
|---|---|---|---|---|
| 0 | `ToyWorld` | stdlib only | unit tests, trivial examples | shipped |
| 1 | `IrSimEnvironment` | `pip install ir-sim` | 2D visual demo (matplotlib, diff-drive, LiDAR) | shipped |
| 2a | `MujocoArmEnvironment` | `pip install mujoco` | 3D physics, Franka Panda pick-and-place | shipped |
| 2b | `MujocoHumanoidEnvironment` | `pip install mujoco` | Unitree G1 / H1, procedural gait, ONNX policy slot | shipped |
| 3 | `ExternalROS2Environment` | sourced ROS2 workspace | Gazebo Harmonic, Isaac Sim (ROS2 bridge), real robots | shipped |

All tiers implement the same `SimEnvironment` protocol — agent code doesn't change when you swap backends. `robot-irsim` is Tier 1; `robot-panda` is Tier 2a; `robot-humanoid` is Tier 2b; `robot-external` is Tier 3 and can point at any Gazebo world or real robot that publishes `nav_msgs/Odometry` and accepts `geometry_msgs/Twist`.

## Voice pipeline

EdgeVox's original identity and the agent framework's substrate. Streaming STT → LLM → TTS with voice interrupt, wake words, 16 languages, TUI and web UIs. Run it without any agent code:

```bash
edgevox                         # full TUI (default)
edgevox --web-ui                # browser interface
edgevox --wakeword "hey jarvis"
edgevox-cli                     # simple CLI
edgevox-cli --text-mode         # no microphone needed
```

### Languages & backends

| Language | STT | TTS | Voices |
|---|---|---|---|
| 🇺🇸 English, 🇫🇷 French, 🇪🇸 Spanish, 🇮🇳 Hindi, 🇮🇹 Italian, 🇧🇷 Portuguese, 🇯🇵 Japanese, 🇨🇳 Chinese | faster-whisper | Kokoro | 25 |
| 🇻🇳 Vietnamese | sherpa-onnx (Zipformer) | Piper | 20 |
| 🇩🇪 German, 🇷🇺 Russian, 🇸🇦 Arabic, 🇮🇩 Indonesian | faster-whisper | Piper | varies |
| 🇰🇷 Korean | faster-whisper | Supertonic | 10 |
| 🇹🇭 Thai | faster-whisper | PyThaiTTS | 1 |

Models are hosted on [`nrl-ai/edgevox-models`](https://huggingface.co/nrl-ai/edgevox-models) (HuggingFace) with fallback to upstream repos.

Full TUI + slash-command reference: [`docs/documentation/commands.md`](docs/documentation/commands.md).

## Hardware requirements

| Device | RAM | GPU | Status |
|--------|-----|-----|---|
| PC (i9 + RTX 3080 16 GB) | 64 GB | CUDA | supported — first-audio latency TBD |
| Jetson Orin Nano | 8 GB | CUDA | supported — first-audio latency TBD |
| MacBook Air M1 | 8 GB | Metal | supported — first-audio latency TBD |
| Any modern laptop | 16 GB+ | CPU only | supported — first-audio latency TBD |

> Latency cells are intentionally TBD. EdgeVox does not publish projected or untested numbers. Once `benchmarks/perf/bench_voice_ttft.py` runs on a given device, the measured value lands here. PRs welcome with hardware fingerprint + script output.

## ROS2 integration

EdgeVox ships a full ROS2 surface, opt-in with `--ros2` on any agent or the voice pipeline. Topics live under a configurable namespace (default `/edgevox`).

```bash
source /opt/ros/jazzy/setup.bash
edgevox --ros2                                      # voice pipeline
edgevox-agent robot-humanoid --simple-ui --ros2     # G1 + voice + ROS2
edgevox-agent robot-external --text-mode            # drive an external ROS2 robot
```

**Published**: `transcription`, `response`, `state` (transient-local), `audio_level`, `metrics`, `bot_token`, `bot_sentence`, `wakeword`, `info`, `robot_state` (sim snapshot), `agent_event` (JSON stream of tool calls / skill goals / handoffs / safety preempts). With a sim attached, `RobotROS2Adapter` adds `/tf`, `pose`, `scan` (IR-SIM lidar), `image_raw` (MuJoCo camera).

**Subscribed**: `tts_request`, `command`, `text_input`, `interrupt`, `set_language`, `set_voice`, `cmd_vel` (Nav2 Twist), `goal_pose` (Nav2 PoseStamped).

**Services**: `list_voices`, `list_languages`, `hardware_info`, `model_info` — each an `std_srvs/srv/Trigger` returning JSON.

**Actions**: `execute_skill` (`edgevox_msgs/action/ExecuteSkill`) — generic `skill_name` + `arguments_json` goal so any agent skill is callable by a stock `rclpy.action.ActionClient`. Build the companion interface package with `colcon build --packages-select edgevox_msgs`.

Launch files under `launch/`: `edgevox.launch.py`, `edgevox_irsim.launch.py`, `edgevox_panda.launch.py`. Full reference: [`docs/documentation/ros2.md`](docs/documentation/ros2.md).

## Architecture

EdgeVox follows the classical robotics three-layer pattern. The agent framework lives only in the **deliberative** layer; safety reflexes and real-time control never touch the LLM.

```mermaid
flowchart TB
    D["`**Deliberative** — ≤ 1 Hz
LLMAgent · Workflows · Router · Handoff
@tool · @skill (Python functions)`"]
    E["`**Executive** — 10–50 Hz
Skill library · GoalHandle lifecycle · BT-shaped workflows
goal / feedback / cancel`"]
    R["`**Reactive** — ≥ 100 Hz
Motor control · watchdogs · SafetyMonitor
*(bypasses LLM)*`"]
    VP["`**Voice pipeline** — the substrate
Mic → VAD → STT → AgentProcessor → SentenceSplit → TTS → Spk`"]
    OUT["`ROS2 bridge · IR-SIM (Tier 1) · MuJoCo arm (Tier 2a) · Unitree G1/H1 (Tier 2b) · External ROS2 / Gazebo (Tier 3)`"]
    D --> E --> R
    R --> VP
    VP --> D
    VP --> OUT
    classDef deliberative fill:#f3bd92,stroke:#8c4000,color:#4a2200,stroke-width:2px
    classDef executive    fill:#e3e3e1,stroke:#a6a6a3,color:#2a2a2a
    classDef reactive     fill:#cececb,stroke:#7e7e7c,color:#1a1a1d
    classDef substrate    fill:#f1f1ef,stroke:#cececb,color:#424241
    classDef out          fill:#fafaf8,stroke:#cececb,color:#5c5c5a
    class D deliberative
    class E executive
    class R reactive
    class VP substrate
    class OUT out
```

The LLM never enters the reactive layer. Safety reflexes bypass it. Skills expose *intents* (`navigate_to(room)`), not *control* (`set_speed(mps)`). Every other design choice flows from this rule.

## Model sizes

| Component | Model | Size | RAM |
|---|---|---|---|
| VAD | Silero VAD v6 | ~2 MB | ~10 MB |
| STT | whisper-small | 500 MB | ~600 MB |
| STT | whisper-large-v3-turbo | 1.5 GB | ~2 GB |
| LLM | Gemma 4 E2B IT Q4_K_M | 1.8 GB | ~2.5 GB |
| TTS | Kokoro 82M | 200 MB | ~300 MB |
| Wake | pymicro-wakeword | ~5 MB | ~10 MB |

- **M1 Air (8 GB):** whisper-small + Q4_K_M = **3.4 GB**
- **PC with GPU:** whisper-large-v3-turbo + Q4_K_M = **5.8 GB**

## Documentation

- **[Agents & Tools guide](docs/documentation/agents.md)** — full agent framework reference: tools vs skills, workflows, safety monitor, simulation tiers, threading model, anti-patterns
- **[Workflow recipes](docs/documentation/workflow-recipes.md)** — `PlanExecuteEvaluate` and friends: one-line factories for the multi-agent harness patterns from Anthropic's "Harness Design for Long-running Apps". Includes a runnable demo on ToyWorld + a model-size benchmark
- **[Architecture](docs/documentation/architecture.md)** — streaming pipeline deep-dive
- **[Quick start](docs/documentation/quickstart.md)**
- **[TUI commands](docs/documentation/commands.md)**
- **[ROS2 guide](docs/documentation/ros2.md)** — bridge topics, services, `execute_skill` action, TF2 / Nav2 / sensor interop, launch files

### Harness architecture

In-depth docs for each subsystem of the agent harness:

- **[Agent loop](docs/documentation/agent-loop.md)** — six-fire-point loop, parallel dispatch, handoff short-circuit, cancel-token plumbing
- **[Hooks](docs/documentation/hooks.md)** — fire points, payloads, priority scale, built-ins, SLM hardening
- **[Memory](docs/documentation/memory.md)** — `MemoryStore` / `SessionStore` / `Compactor` / `NotesFile`, bi-temporal facts
- **[Multi-agent](docs/documentation/multiagent.md)** — Blackboard, Supervisor, Orchestrator, BackgroundAgent (OTP restart policies)
- **[Interrupt & barge-in](docs/documentation/interrupt.md)** — `cancel_token` to llama-cpp `stopping_criteria`, AEC defaults, repeatable interrupts
- **[Tool calling](docs/documentation/tool-calling.md)** — parser chain, GBNF grammar-constrained decoding, `tool_choice_policy`

Full site: [EdgeVox Docs](https://edgevox.nrl.ai) (VitePress). Run locally:

```bash
cd docs && npm run dev
```

## Citation

If you use EdgeVox in research or production, please cite:

```bibtex
@software{edgevox2026,
  author = {Nguyen, Viet-Anh and {Neural Research Lab}},
  title  = {EdgeVox: on-device voice agents for robotics},
  year   = {2026},
  url    = {https://github.com/nrl-ai/edgevox},
  note   = {MIT License}
}
```

## License

MIT

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